Final year IEEE 2016-2017 PROJECTS TITLES (IEEE 2016 papers) For ME,M.Tech,BE,B.Tech (.net,java,matlab,vlsi,ns2,mech,power elc & power sys)

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CSE IEEE PROJECTS -

2016-2017

S3I_DN16_001 - Dynamic and Public Auditing with Fair Arbitration for Cloud Data

Cloud users no longer physically possess their data, so how to ensure the

integrity of their outsourced data becomes a challenging task. Recently proposed

schemes such as “provable data possession” and “proofs of retrievability” are designed to address this problem, but they are designed to audit static archive data and

therefore lack of data dynamics support. Moreover, threat models in these schemes

usually assume an honest data owner and focus on detecting a dishonest cloud service provider despite the fact that clients may also misbehave. This paper proposes a public

auditing scheme with data dynamics support and fairness arbitration of potential

disputes. In particular, we design an index switcher to eliminate the limitation of index usage in tag computation in current schemes and achieve efficient handling of

data dynamics. To address the fairness problem so that no party can misbehave

without being detected, we further extend existing threat models and adopt signature exchange idea to design fair arbitration protocols, so that any possible dispute can be

fairly settled. The security analysis shows our scheme is provably secure, and the

performance evaluation demonstrates the overhead of data dynamics and dispute

arbitration are reasonable.

S3I_DN16_002 - Enabling Cloud Storage Auditing with Verifiable Outsourcing

of Key Updates Key-exposure resistance has always been an important issue for in-depth cyber

defence in many security applications. Recently, how to deal with the key exposure

problem in the settings of cloud storage auditing has been proposed and studied. To address the challenge, existing solutions all require the client to update his secret keys

in every time period, which may inevitably bring in new local burdens to the client,

especially those with limited computation resources, such as mobile phones. In this

paper, we focus on how to make the key updates as transparent as possible for the client and propose a new paradigm called cloud storage auditing with verifiable

outsourcing of key updates. In this paradigm, key updates can be safely outsourced to

some authorized party, and thus the key-update burden on the client will be kept minimal. In particular, we leverage the third party auditor (TPA) in many existing

public auditing designs, let it play the role of authorized party in our case, and make it

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in charge of both the storage auditing and the secure key updates for key-exposure

resistance. In our design, TPA only needs to hold an encrypted version of the client's secret key while doing all these burdensome tasks on behalf of the client. The client

only needs to download the encrypted secret key from the TPA when uploading new

files to cloud. Besides, our design also equips the client with capability to further verify the validity of the encrypted secret keys provided by the TPA. All these salient

features are carefully designed to make the whole auditing procedure with key

exposure resistance as transparent as possible for the client. We formalize the

definition and the security model of this paradigm. The security proof and the performance simulation show that our detailed design instantiations are secure and

efficient.

S3I_DN16_003 - Providing User Security Guarantees in Public Infrastructure

Clouds

The infrastructure cloud (IaaS) service model offers improved resource flexibility and availability, where tenants – insulated from the minutiae of hardware

maintenance – rent computing resources to deploy and operate complex systems.

Large-scale services running on IaaS platforms demonstrate the viability of this model; nevertheless, many organizations operating on sensitive data avoid migrating

operations to IaaS platforms due to security concerns. In this paper, we describe a

framework for data and operation security in IaaS, consisting of protocols for a trusted

launch of virtual machines and domain-based storage protection. We continue with an extensive theoretical analysis with proofs about protocol resistance against attacks in

the defined threat model. The protocols allow trust to be established by remotely

attesting host platform configuration prior to launching guest virtual machines and ensure confidentiality of data in remote storage, with encryption keys maintained

outside of the IaaS domain. Presented experimental results demonstrate the validity

and efficiency of the proposed protocols. The framework prototype was implemented on a test bed operating a public electronic health record system, showing that the

proposed protocols can be integrated into existing cloud environments.

S3I_DN16_004 - Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps

The rapid adoption of mobile messaging Apps has enabled us to collect massive

amount of encrypted Internet traffic of mobile messaging. The classification of this traffic into different types of in-App service usages can help for intelligent network

management, such as managing network bandwidth budget and providing quality of

services. Traditional approaches for classification of Internet traffic rely on packet inspection, such as parsing HTTP headers. However, messaging Apps are increasingly

using secure protocols, such as HTTPS and SSL, to transmit data. This imposes

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significant challenges on the performances of service usage classification by packet

inspection. To this end, in this paper, we investigate how to exploit encrypted Internet traffic for classifying in-App usages. Specifically, we develop a system, named

CUMMA, for classifying service usages of mobile messaging Apps by jointly

modeling user behavioral patterns, network traffic characteristics and temporal dependencies. Along this line, we first segment Internet traffic from traffic-flows into

sessions with a number of dialogs in a hierarchical way. Also, we extract the

discriminative features of traffic data from two perspectives: (i) packet length and (ii)

time delay. Next, we learn a service usage predictor to classify these segmented dialogs into single-type usages or outliers. In addition, we design a clustering Hidden

Markov Model (HMM) based method to detect mixed dialogs from outliers and

decompose mixed dialogs into sub-dialogs of single-type usage. Indeed, CUMMA enables mobile analysts to identify service usages and analyze end-user in-App

behaviors even for encrypted Internet traffic. Finally, the extensive experiments on

real-world messaging data demonstrate the effectiveness and efficiency of the proposed method for service usage classification.

S3I_DN16_005 - Text Mining the Contributors to Rail Accidents Rail accidents represent an important safety concern for the transportation

industry in many countries. In the 11 years from 2001 to 2012, the U.S. had more than

40 000 rail accidents that cost more than $45 million. While most of the accidents

during this period had very little cost, about 5200 had damages in excess of $141 500. To better understand the contributors to these extreme accidents, the Federal Railroad

Administration has required the railroads involved in accidents to submit reports that

contain both fixed field entries and narratives that describe the characteristics of the accident. While a number of studies have looked at the fixed fields, none have done

an extensive analysis of the narratives. This paper describes the use of text mining

with a combination of techniques to automatically discover accident characteristics that can inform a better understanding of the contributors to the accidents. The study

evaluates the efficacy of text mining of accident narratives by assessing predictive

performance for the costs of extreme accidents. The results show that predictive

accuracy for accident costs significantly improves through the use of features found by text mining and predictive accuracy further improves through the use of modern

ensemble methods. Importantly, this study also shows through case examples how the

findings from text mining of the narratives can improve understanding of the contributors to rail accidents in ways not possible through only fixed field analysis of

the accident reports.

S3I_DN16_006 - MMBcloud-tree: Authenticated Index for Verifiable Cloud

Service Selection

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Cloud brokers have been recently introduced as an additional computational

layer to facilitate cloud selection and service management tasks for cloud consumers. However, existing brokerage schemes on cloud service selection typically assume that

brokers are completely trusted, and do not provide any guarantee over the correctness

of the service recommendations. It is then possible for a compromised or dishonest broker to easily take advantage of the limited capabilities of the clients and provide

incorrect or incomplete responses. To address this problem, we propose an innovative

Cloud Service Selection Verification (CSSV) scheme and index structures

(MMBcloud-tree) to enable cloud clients to detect misbehavior of the cloud brokers during the service selection process. We demonstrate correctness and efficiency of our

approaches both theoretically and empirically.

S3I_DN16_007 - Identity-Based Proxy-Oriented Data Uploading and Remote

Data Integrity Checking in Public Cloud

More and more clients would like to store their data to public cloud servers (PCSs) along with the rapid development of cloud computing. New security problems

have to be solved in order to help more clients process their data in public cloud.

When the client is restricted to access PCS, he will delegate its proxy to process his data and upload them. On the other hand, remote data integrity checking is also an

important security problem in public cloud storage. It makes the clients check whether

their outsourced data are kept intact without downloading the whole data. From the

security problems, we propose a novel proxy-oriented data uploading and remote data integrity checking model in identity-based public key cryptography: identity-based

proxy-oriented data uploading and remote data integrity checking in public cloud (ID-

PUIC). We give the formal definition, system model, and security model. Then, a concrete ID-PUIC protocol is designed using the bilinear pairings. The proposed ID-

PUIC protocol is provably secure based on the hardness of computational Diffie-

Hellman problem. Our ID-PUIC protocol is also efficient and flexible. Based on the original client's authorization, the proposed ID-PUIC protocol can realize private

remote data integrity checking, delegated remote data integrity checking, and public

remote data integrity checking.

S3I_DN16_008 - Fine-grained Two-factor Access Control for Web-based Cloud

Computing Services

In this paper, we introduce a new fine-grained two-factor authentication (2FA) access control system for web-based cloud computing services. Specifically, in our

proposed 2FA access control system, an attribute-based access control mechanism is

implemented with the necessity of both a user secret key and a lightweight security device. As a user cannot access the system if they do not hold both, the mechanism

can enhance the security of the system, especially in those scenarios where many

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users share the same computer for web-based cloud services. In addition, attribute-

based control in the system also enables the cloud server to restrict the access to those users with the same set of attributes while preserving user privacy, i.e., the cloud

server only knows that the user fulfills the required predicate, but has no idea on the

exact identity of the user. Finally, we also carry out a simulation to demonstrate the practicability of our proposed 2FA system.

S3I_DN16_009 - Cloud workflow scheduling with deadlines and time slot

availability Allocating service capacities in cloud computing is based on the assumption

that they are unlimited and can be used at any time. However, available service

capacities change with workload and cannot satisfy users’ requests at any time from the cloud provider’s perspective because cloud services can be shared by multiple

tasks. Cloud service providers provide available time slots for new user’s requests

based on available capacities. In this paper, we consider workflow scheduling with deadline and time slot availability in cloud computing. An iterated heuristic

framework is presented for the problem under study which mainly consists of initial

solution construction, improvement, and perturbation. Three initial solution construction strategies, two greedy- and fair-based improvement strategies and a

perturbation strategy are proposed. Different strategies in the three phases result in

several heuristics. Experimental results show that different initial solution and

improvement strategies have different effects on solution qualities.

S3I_DN16_010 - Publicly Verifiable Inner Product Evaluation over Outsourced

Data Streams under Multiple Keys Uploading data streams to a resource-rich cloud server for inner product

evaluation, an essential building block in many popular stream applications (e.g.,

statistical monitoring), is appealing to many companies and individuals. On the other hand, verifying the result of the remote computation plays a crucial role in addressing

the issue of trust. Since the outsourced data collection likely comes from multiple data

sources, it is desired for the system to be able to pinpoint the originator of errors by

allotting each data source a unique secret key, which requires the inner product verification to be performed under any two parties’ different keys. However, the

present solutions either depend on a single key assumption or powerful yet

practicallyinefficient fully homomorphic cryptosystems. In this paper, we focus on the more challenging multi-key scenario where data streams are uploaded by multiple

data sources with distinct keys. We first present a novel homomorphic verifiable tag

technique to publicly verify the outsourced inner product computation on the dynamic data streams, and then extend it to support the verification of matrix product

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computation. We prove the security of our scheme in the random oracle model.

Moreover, the experimental result also shows the practicability of our design.

S3I_DN16_011 - Inverted Linear Quadtree: Efficient Top K Spatial Keyword

Search With advances in geo-positioning technologies and geo-location services, there are a

rapidly growing amount of spatiotextual objects collected in many applications such

as location based services and social networks, in which an object is described by its

spatial location and a set of keywords (terms). Consequently, the study of spatial keyword search which explores both location and textual description of the objects

has attracted great attention from the commercial organizations and research

communities. In the paper, we study two fundamental problems in the spatial keyword queries: top k spatial keyword search (TOPK-SK), and batch top k spatial keyword

search (BTOPK-SK). Given a set of spatio-textual objects, a query location and a set

of query keywords, the TOPK-SK retrieves the closest k objects each of which contains all keywords in the query. BTOPK-SK is the batch processing of sets of

TOPK-SK queries. Based on the inverted index and the linear quadtree, we propose a

novel index structure, called inverted linear quadtree (IL-Quadtree), which is carefully designed to exploit both spatial and keyword based pruning techniques to effectively

reduce the search space. An efficient algorithm is then developed to tackle top k

spatial keyword search. To further enhance the filtering capability of the signature of

linear quadtree, we propose a partition based method. In addition, to deal with BTOPK-SK, we design a new computing paradigm which partition the queries into

groups based on both spatial proximity and the textual relevance between queries. We

show that the IL-Quadtree technique can also efficiently support BTOPK-SK. Comprehensive experiments on real and synthetic data clearly demonstrate the

efficiency of our methods.

S3I_DN16_012 - Securing SIFT: Privacy-preserving Outsourcing Computation

of Feature Extractions over Encrypted Image Data

Advances in cloud computing have greatly motivated data owners to outsource their

huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility.

Despite the tremendous benefits, the outsourced multimedia data and its originated

applications may reveal the data owner’s private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused

new research interest on privacy-preserving computations over outsourced multimedia

data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform

(SIFT) over massive encrypted image data. We first show that previous solutions to

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this problem have either efficiency/security or practicality issues, and none can well

preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and

security requirements simultaneously with the preservation of its key characteristics,

by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature

extraction computations onto two independent cloud servers. We both carefully

analyze and extensively evaluate the security and effectiveness of our design. The

results show that our solution is practically secure, outperforms the state-of-theart, and performs comparably to the original SIFT in terms of various characteristics,

including rotation invariance, image scale invariance, robust matching across affine

distortion, addition of noise and change in 3D viewpoint and illumination.

S3I_DN16_013 - A Secure and Dynamic Multi-keyword Ranked Search Scheme

over Encrypted Cloud Data Due to the increasing popularity of cloud computing, more and more data owners are

motivated to outsource their data to cloud servers for great convenience and reduced

cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-

based document retrieval. In this paper, we present a secure multi-keyword ranked

search scheme over encrypted cloud data, which simultaneously supports dynamic

update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF IDF model are combined in the index

construction and query generation. We construct a special tree-based index structure

and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and

query vectors, and meanwhile ensure accurate relevance score calculation between

encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results . Due to the use of our special

tree-based index structure, the proposed scheme can achieve sub-linear search time

and deal with the deletion and insertion of documents flexibly. Extensive experiments

are conducted to demonstrate the efficiency of the proposed scheme.

S3I_DN16_014 - Protecting Your Right: Verifiable Attribute-based Keyword

Search with Fine-grained Owner-enforced Search Authorization in the Cloud Search over encrypted data is a critically important enabling technique in cloud

computing, where encryption-beforeoutsourcing is a fundamental solution to

protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the

outsourced dataset or the secure searchable index of the dataset are encrypted and

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managed by a single owner, typically based on symmetric cryptography. In this paper,

we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e.

multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we

present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e. file-level) search authorization.

Our scheme allows multiple owners to encrypt and outsource their data to the cloud

server independently. Users can generate their own search capabilities without relying

on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further,

by incorporating proxy re-encryption and lazy re-encryption techniques, we are able

to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the

proposed ABKS-UR scheme selectively secure against chosen-keyword attack. To

build confidence of data user in the proposed secure search system, we also design a search result verification scheme. Finally, performance evaluation shows that the

efficiency of our scheme.

S3I_DN16_015 - Secure Data Analytics for Cloud-Integrated Internet of Things

Applications

Cloud-integrated Internet of Things (IoT) is emerging as the next-generation

service platform that enables smart functionality worldwide. IoT applications such as smart grid and power systems, e-health, and body monitoring applications along with

large-scale environmental and industrial monitoring are increasingly generating large

amounts of data that can conveniently be analyzed through cloud service provisioning. However, the nature of these applications mandates the use of secure and privacy-

preserving implementation of services that ensures the integrity of data without any

unwarranted exposure. This article explores the unique challenges and issues within this context of enabling secure cloud-based data analytics for the IoT. Three main

applications are discussed in detail, with solutions outlined based on the use of fully

homomorphic encryption systems to achieve data security and privacy over cloud-

based analytical phases. The limitations of existing technologies are discussed and models proposed with regard to achieving high efficiency and accuracy in the

provisioning of analytic services for encrypted data over a cloud platform.

S3I_DN16_016 - A Low-Cost Low-Power Ring Oscillator-based Truly Random

Number Generator for Encryption on Smart Cards

W. Bit rate of the TRNG after post processing is 100 kb/s. The proposed TRNG has been made into an IP and successfully applied in an SD card for encryption

application. The proposed TRNG has passed the NIST tests and Diehard tests.m

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standard CMOS process, the proposed TRNG has an area as low as 0.005 mm2.

Powered by a single 1.8 V supply voltage, the TRNG has a power consumption of 40

The design of a low-cost low-power ring oscillator-based truly random number generator (TRNG) macro-cell, suitable to be integrated in smart cards, is presented.

The oscillator sampling technique is exploited and a tetrahedral oscillator with large

jitter has been employed to realize the TRNG. Techniques to improve the statistical quality of the ring oscillator-based TRNGs’ bit sequences have been presented and

verified by simulation and measurement. Post digital processor is added to further

enhance the randomness of the output bits. Fabricated in HHNEC 0.13

S3I_DN16_017 - Encrypted Data Management with Deduplication in Cloud

Computing

Cloud computing offers a new way to deliver services by rearranging resources over the Internet and providing them to users on demand. It plays an important role in

supporting data storage, processing, and management in the Internet of Things (IoT).

Various cloud service providers (CSPs) offer huge volumes of storage to maintain and manage IoT data, which can include videos, photos, and personal health records.

These CSPs provide desirable service properties, such as scalability, elasticity, fault

tolerance, and pay per use. Thus, cloud computing has become a promising service

paradigm to support IoT applications and IoT system deployment. To ensure data privacy, existing research proposes to outsource only encrypted data to CSPs.

However, the same or different users could save duplicated data under different

encryption schemes at the cloud. Although cloud storage space is huge, this kind of duplication wastes networking resources, consumes excess power, and complicates

data management. Thus, saving storage is becoming a crucial task for CSPs.

Deduplication can achieve high space and cost savings, reducing up to 90 to 95 percent of storage needs for backup applications (http://opendedup.org) and up to 68

percent in standard file systems.1 Obviously, the savings, which can be passed back

directly or indirectly to cloud users, are significant to the economics of cloud

business. At the same time, data owners want CSPs to protect their personal data from unauthorized access. CSPs should therefore perform access control based on the data

owner’s expectations. In addition, data owners want to control not only data access

but also its storage and usage. From a flexibility viewpoint, data deduplication should cooperate with data access control mechanisms. That is, the same data, although in an

encrypted form, is only saved once at the cloud but can be accessed by different users

based on the data owners’ policies.

S3I_DN16_018 - Dual-Server Public-Key Encryption with Keyword Search for

Secure Cloud Storage

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Searchable encryption is of increasing interest for protecting the data privacy in

secure searchable cloud storage. In this work, we investigate the security of a well-known cryptographic primitive, namely Public Key Encryption with Keyword Search

(PEKS) which is very useful in many applications of cloud storage. Unfortunately, it

has been shown that the traditional PEKS framework suffers from an inherent insecurity called inside Keyword Guessing Attack (KGA) launched by the malicious

server. To address this security vulnerability, we propose a new PEKS framework

named Dual-Server Public Key Encryption with Keyword Search (DS-PEKS). As

another main contribution, we define a new variant of the Smooth Projective Hash Functions (SPHFs) referred to as linear and homomorphic SPHF (LH-SPHF). We

then show a generic construction of secure DS-PEKS from LH-SPHF. To illustrate

the feasibility of our new framework, we provide an efficient instantiation of the general framework from a DDH-based LH-SPHF and show that it can achieve the

strong security against inside KGA.

S3I_DN16_019 - A recommendation system based on hierarchical clustering of

an article-level citation network

The scholarly literature is expanding at a rate that necessitates intelligent algorithms for search and navigation.For the most part, the problem of delivering scholarly

articles has been solved. If one knows the title of an article, locating it requires little

effort and, paywalls permitting, acquiring a digital copy has become trivial.However,

the navigational aspect of scientific search – finding relevant, influential articles that one does not know exist – is in its early development. In this paper, we introduce

Eigenfactor Recommends – a citation-based method for improving scholarly

navigation. The algorithm uses the hierarchical structure of scientific knowledge, making possible multiple scales of relevance for different users. We implement the

method and generate more than 300 million recommendations from more than 35

million articles from various bibliographic databases including the AMiner dataset. We find little overlap with co-citation, another well-known citation recommender,

which indicates potential complementarity. In an online A-B comparison using SSRN,

we find that our approach performs as well as co-citation, but this new approach

offers much larger recommendation coverage. We make the code and recommendations freely available at babel.eigenfactor.org and provide an API for

others to use for implementing and comparing the recommendations on their own

platforms.

S3I_DN16_020 - Efficient Group Key Transfer Protocol for WSNs

Special designs are needed for cryptographic schemes in wireless sensor networks (WSNs). This is because sensor nodes are limited in memory storage and

computational power. The existing group key transfer protocols for WSNs using

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classical secret sharing require that a t-degree interpolating polynomial be computed

in order to encrypt and decrypt the secret group key. This approach is too computationally intensive. In this paper, we propose a new group key transfer

protocol using a linear secret sharing scheme (LSSS) and factoring assumption. The

proposed protocol can resist potential attacks and also significantly reduce the computation complexity of the system while maintaining low communication cost.

Such a scheme is desirable for secure group communications in wireless sensor

networks (WSNs), where portable devices or sensors need to reduce their computation

as much as possible due to battery power limitations.

S3I_SE16_001 - A Tool-Supported Methodology for Validation and Refinement

of Early-Stage Domain Models

Model-driven engineering (MDE) promotes automated model transformations along the entire development process. Guaranteeing the quality of early models is

essential for a successful application of MDE techniques and related tool-supported

model refinements. Do these models properly reflect the requirements elicited from the owners of the problem domain? Ultimately, this question needs to be asked to the

domain experts. The problem is that a gap exists between the respective backgrounds

of modeling experts and domain experts. MDE developers cannot show a model to the domain experts and simply ask them whether it is correct with respect to the

requirements they had in mind. To facilitate their interaction and make such validation

more systematic, we propose a methodology and a tool that derive a set of

customizable questionnaires expressed in natural language from each model to be validated. Unexpected answers by domain experts help to identify those portions of

the models requiring deeper attention. We illustrate the methodology and the current

status of the developed tool MOTHIA, which can handle UML Use Case, Class, and Activity diagrams. We assess MOTHIA effectiveness in reducing the gap between

domain and modeling experts, and in detecting modeling faults on the European

Project CHOReOS.

S3I_SE16_002 - Trust Agent-Based Behavior Induction in Social Networks

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The essence of social networks is that they can influence people's public

opinions and group behaviors form quickly. Negative group behavior influences societal stability significantly, but existing behavior-induction approaches are too

simple and inefficient. To automatically and efficiently induct behavior in social

networks, this article introduces trust agents and designs their features according to group behavior features. In addition, a dynamics control mechanism can be generated

to coordinate participant behaviors in social networks to avoid a specific restricted

negative group behavior.

S3I_DE16_001 - Incremental and Decremental Max-flow for Online Semi-

supervised Learning

In classification, if a small number of instances is added or removed,

incremental and decremental techniques can be applied to quickly update the model. However, the design of incremental and decremental algorithms involves many

considerations. In this paper, we focus on linear classifiers including logistic

regression and linear SVM because of their simplicity over kernel or other methods. By applying a warm start strategy, we investigate issues such as using primal or dual

formulation, choosing optimization methods, and creating practical implementations.

Through theoretical analysis and practical experiments, we conclude that a warm start setting on a high-order optimization method for primal formulations is more suitable

than others for incremental and decremental learning of linear classification.

S3I_DE16_002 - Personalized Influential Topic Search via Social Network

Summarization

Social networks are a vital mechanism to disseminate information to friends and

colleagues. In this work, we investigate an important problem - the personalized

influential topic search, or PIT-Search in a social network: Given a keyword query q issued by a user u in a social network, a PIT-Search is to find the top-k q-related

topics that are most influential for the query user u. The influence of a topic to a query

user depends on the social connection between the query user and the social users containing the topic in the social network. To measure the topics’ influence at the

similar granularity scale, we need to extract the social summarization of the social

network regarding topics. To make effective topic-aware social summarization, we

propose two random-walk based approaches: random clustering and an L-length random walk. Based on the proposed approaches, we can find a small set of

representative users with assigned influential scores to simulate the influence of the

large number of topic users in the social network with regards to the topic. The

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selected representative users are denoted as the social summarization of topic-aware

influence spread over the social network. And then, we verify the usefulness of the social summarization by applying it to the problem of personalized influential topic

search. Finally, we evaluate the performance of our algorithms using real-world

datasets, and show the approach is efficient and effective in practice.

S3I_DE16_003 - Survey on Aspect-Level Sentiment Analysis

The field of sentiment analysis, in which sentiment is gathered, analyzed, and

aggregated from text, has seen a lot of attention in the last few years. The

corresponding growth of the field has resulted in the emergence of various subareas, each addressing a different level of analysis or research question. This survey focuses

on aspect-level sentiment analysis, where the goal is to find and aggregate sentiment

on entities mentioned within documents or aspects of them. An in-depth overview of the current state-of-the-art is given, showing the tremendous progress that has already

been made in finding both the target, which can be an entity as such, or some aspect

of it, and the corresponding sentiment. Aspect-level sentiment analysis yields very fine-grained sentiment information which can be useful for applications in various

domains. Current solutions are categorized based on whether they provide a method

for aspect detection, sentiment analysis, or both. Furthermore, a breakdown based on

the type of algorithm used is provided. For each discussed study, the reported performance is included. To facilitate the quantitative evaluation of the various

proposed methods, a call is made for the standardization of the evaluation

methodology that includes the use of shared data sets. Semanticallyrich concept-centric aspect-level sentiment analysis is discussed and identified as one of the most

promising future research direction.

S3I_DE16_004 - Multilabel Classification via Co-evolutionary Multilabel

Hypernetwork

Multilabel classification is prevalent in many real-world applications where

data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of

the existing multilabel learning algorithms are either ineffective or computational

demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label

correlations in an effective and efficient way. To this end, we firstly convert the

traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning

algorithm to learn an integrated classification model for all labels. The proposed Co-

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MLHN exploits arbitrary order label correlations and has linear computational

complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against

state-of-the-art multilabel learning algorithms, in terms of both classification

performance and scalability with respect to the number of labels.

S3I_DE16_005 - Answering Pattern Queries Using Views

Answering queries using views has proven effective for querying relational and

semistructured data. This paper investigates this issue for graph pattern queries based on graph simulation. We propose a notion of pattern containment to characterize

graph pattern matching using graph pattern views. We show that a pattern query can

be answered using a set of views if and only if it is contained in the views. Based on this characterization, we develop efficient algorithms to answer graph pattern queries.

We also study problems for determining (minimal, minimum) containment of pattern

queries. We establish their complexity (from cubic-time to NPcomplete) and provide efficient checking algorithms (approximation when the problem is intractable). In

addition, when a pattern query is not contained in the views, we study maximally

contained rewriting to find approximate answers; we show that it is in cubic-time to compute such rewriting, and present a rewriting algorithm. We experimentally verify

that these methods are able to efficiently answer pattern queries on large real-world

graphs.

S3I_DE16_006 - Similarity Measure Selection for Clustering Time Series

Databases

In the past few years, clustering has become a popular task associated with time series. The choice of a suitable distance measure is crucial to the clustering process

and, given the vast number of distance measures for time series available in the

literature and their diverse characteristics, this selection is not straightforward. With the objective of simplifying this task, we propose a multi-label classification

framework that provides the means to automatically select the most suitable distance

measures for clustering a time series database. This classifier is based on a novel

collection of characteristics that describe the main features of the time series databases and provide the predictive information necessary to discriminate between a set of

distance measures. In order to test the validity of this classifier, we conduct a

complete set of experiments using both synthetic and real time series databases and a set of five common distance measures. The positive results obtained by the designed

classification framework for various performance measures indicate that the proposed

methodology is useful to simplify the process of distance selection in time series clustering tasks.

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S3I_DE16_007 - Incremental Consolidation of Data-Intensive Multi-Flows Business intelligence (BI) systems depend on efficient integration of disparate

and often heterogeneous data. The integration of data is governed by data-intensive

flows and is driven by a set of information requirements. Designing such flows is in general a complex process, which due to the complexity of business environments is

hard to be done manually. In this paper, we deal with the challenge of efficient design

and maintenance of data-intensive flows and propose an incremental approach, namely CoAl , for semi-automatically consolidating data-intensive flows satisfying a

given set of information requirements. CoAl works at the logical level and

consolidates data flows from either high-level information requirements or platform-

specific programs. As CoAl integrates a new data flow, it opts for maximal reuse of existing flows and applies a customizable cost model tuned for minimizing the overall

cost of a unified solution. We demonstrate the efficiency and effectiveness of our

approach through an experimental evaluation using our implemented prototype.

S3I_WSN16_001 - Analysis of PKF: A Communication Cost Reduction Scheme for Wireless Sensor Networks

Energy efficiency is a primary concern for wireless sensor networks (WSNs).

One of its most energy-intensive processes is the radio communication. This work uses a predictor combined with a Kalman filter (KF) to reduce the communication

energy cost for cluster-based WSNs. The technique, called PKF, is suitable for typical

WSN applications with adjustable data quality and tens of picojoule computation cost.

However, it is challenging to precisely quantify its underlying process from a

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mathematical point of view. Through an in-depth mathematical analysis, we formulate

the tradeoff between energy efficiency and reconstruction quality of PKF. One of our prominent results for that is the explicit expression for the covariance of the doubly

truncated multivariate normal distribution; it improves the previous methods and has

generality. The validity and accuracy of the analysis are verified with both artificial and real signals. The simulation results, using real temperature values, demonstrate

the efficiency of PKF: without additional data degradation, it reduces the

communication cost by more than 88%. Compared to previous works based on KF,

PKF requires less computational effort while improving the reconstruction quality; compared with the techniques without KF, the advantages of PKF are even more

significant. It reduces the transmission rate of them by at least 29%. Besides, it can be

integrated into network level techniques to further extend the whole network lifetime.

S3I_WSN16_002 - Online Packet Dispatching for Delay Optimal Concurrent Transmissions in Heterogeneous Multi-RAT Networks

In this paper, we consider the problem of concurrent transmissions in a wireless

network consisting of multiple radio access technologies (multi-RATs). That is, a single flow of packets is dispatched over multiple RATs so that the complementary

advantages of different RATs can be exploited. One of the challenging issues arising

in concurrent transmissions is the packet outof- order problem due to diverse wireless

channel states and scheduling policies of different RATs, leading to substantial performance degradation to delay sensitive applications. To address this problem, we

firstly propose a state-independent packet dispatching (SIPD) policy, which attempts

to find the traffic dispatching ratios over multiple RATs to minimize the maximum average delay across different RATs in the long run. We further propose a state-

dependent packet dispatching (SDPD) policy, which achieves fine-grained packet

dispatching in the short-term. We use the value function as a measure of the admittance cost for packet dispatching given the current queueing states, and

formulate the SDPD problem as a convex programming problem. We derive the

close-form solutions for both problems for the special case of two RATs, and adopt

the dual decomposition technique as the solution for the general cases. Simulation results are presented to compare the performance of the proposed schemes with

existing solutions.

S3I_WSN16_003 - Toward Optimal Adaptive Wireless Communications in

Unknown Environments

Designing efficient channel access schemes for wireless communications without any prior knowledge about the nature of environments has been a very

challenging issue, in which the channel state distribution of all spectrum resources

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could be entirely or partially stochastic or adversarial at different times and locations.

In this paper, we propose an online learning algorithm for adaptive channel access of wireless communications in unknown environments based on the theory of

multiarmed bandits (MAB) problems. By automatically tuning two control

parameters, i.e., learning rate and exploration probability, our algorithms could find the optimal channel access strategies and achieve the almost optimal learning

performance over time in different scenarios. The quantitative performance studies

indicate the superior throughput gain when compared with previous solutions and the

flexibility of our algorithm in practice, which is resilient to both oblivious and adaptive jamming attacks with different intelligence and attacking strength that ranges

from no-attack to the full-attack of all spectrum resources. We conduct extensive

simulations to validate our theoretical analysis.

S3I_WSN16_004 - Adaptive Pilot Clustering in Heterogeneous Massive MIMO

Networks We consider the uplink of a cellular massive MIMO network. Acquiring

channel state information at the base stations (BSs) requires uplink pilot signaling.

Since the number of orthogonal pilot sequences is limited by the channel coherence, pilot reuse across cells is necessary to achieve high spectral efficiency. However,

finding efficient pilot reuse patterns is nontrivial especially in practical asymmetric

BS deployments. We approach this problem using coalitional game theory. Each BS

has a few unique pilots and can form coalitions with other BSs to gain access to more pilots. The BSs in a coalition thus benefit from serving more users in their cells, at the

expense of higher pilot contamination and interference. Given that a cell’s average

spectral efficiency depends on the overall pilot reuse pattern, the suitable coalitional game model is in partition form. We develop a low-complexity distributed coalition

formation based on individual stability. By incorporating a base station

intercommunication budget constraint, we are able to control the overhead in message exchange between the base stations and ensure the algorithm’s convergence to a

solution of the game called individually stable coalition structure. Simulation results

reveal fast algorithmic convergence and substantial performance gains over the

baseline schemes with no pilot reuse, full pilot reuse, or random pilot reuse pattern.

S3I_WSN16_005 - Data Aggregation and Principal Component Analysis in

WSNs Data aggregation plays an important role inWireless Sensor Networks (WSNs)

as far as it reduces power consumption and boosts the scalability of the network,

specially in topologies that are prone to bottlenecks (e.g. cluster-trees). Existing works in the literature use clustering approaches, Principal Component Analysis (PCA)

and/or Compressed Sensing (CS) strategies. Our contribution is aligned with PCA and

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explores whether a projection basis that is not the eigenvectors basis may be valid to

sustain a Normalized Mean Squared Error (NMSE) threshold in signal reconstruction and reduce the energy consumption. We derivate first the NSME achieved with the

new basis and elaborate then on the Jacobi eigenvalue decomposition ideas to propose

a new subspace-based data aggregation method. The proposed solution reduces transmissions among the sink and one or more Data Aggregation Nodes (DANs) in

the network. In our simulations we consider without loss of generality a single cluster

network and results show that the new technique succeeds in satisfying the NMSE

requirement and gets close in terms of energy consumption to the best possible solution employing subspace representations. Additionally the proposed method

alleviates the computational load with respect to an eigenvector-based strategy (by a

factor of six in our simulations).

S3I_WSN16_006 - A New cost-effective approach for Battlefield Surveillance in

Wireless Sensor Networks Assuring security (in the form of attacking mode as well as in safeguard mode)

and at the same time keeping strong eye on the opposition's status (position, quantity,

availability) is the key responsibility of a commander in the battlefield. Battlefield surveillance is one of the strong applications of Wireless Sensor Networks (WSNs). A

commander is not only liable to his above responsibilities, but also to manage his

duties in an efficient way. For this reason, ensuring maximum destruction with

minimum resources is a major concern of a commander in the battlefield. This paper focuses on the maximum destruction problem in military affairs. In the work of

Jaigirdar and Islam (2012), the authors proposed two novel algorithms (Maximum

degree analysis and Maximum clique analysis) that ensure the efficiency and cost-effectiveness of the above problem. A comparative study explaining the number of

resources required for commencing required level of destruction made to the

opponents has been provided in the paper. In this paper the authors have come forward with another algorithm for the same problem. With the simulation studies and

comparative analysis of the same example set the authors in this paper demonstrate

the effectiveness (in both the quality and quantity) of the new method to be best

among the three.

S3I_NS16_001 - Contributory Broadcast Encryption with Efficient Encryption

and Short Ciphertexts Broadcast encryption (BE) schemes allow a sender to securely broadcast to any

subset of members but require a trusted party to distribute decryption keys. Group key

agreement (GKA) protocols enable a group of members to negotiate a common encryption key via open networks so that only the group members can decrypt the

ciphertexts encrypted under the shared encryption key, but a sender cannot exclude

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any particular member from decrypting the ciphertexts. In this paper, we bridge these

two notions with a hybrid primitive referred to as contributory broadcast encryption (ConBE). In this new primitive, a group of members negotiate a common public

encryption key while each member holds a decryption key. A sender seeing the public

group encryption key can limit the decryption to a subset of members of his choice. Following this model, we propose a ConBE scheme with short ciphertexts. The

scheme is proven to be fully collusion-resistant under the decision n-Bilinear Diffie-

Hellman Exponentiation (BDHE) assumption in the standard model. Of independent

interest, we present a new BE scheme that is aggregatable. The aggregatability property is shown to be useful to construct advanced protocols.

S3I_NS16_002 - Building an intrusion detection system using a filter-based feature selection algorithm

Redundant and irrelevant features in data have caused a long-term problem in

network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially

when coping with big data. In this paper, we propose a mutual information based

algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly

dependent data features. Its effectiveness is evaluated in the cases of network

intrusion detection. An Intrusion Detection System (IDS), named Least Square

Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is

evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99,

NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve

better accuracy and lower computational cost compared with the state-of-the-art

methods.

S3I_SEC_001: Cloud Workflow Scheduling with Deadlines and Time Slot

Availability

Allocating service capacities in cloud computing is based on the assumption that

they are unlimited and can be used at any time. However, available service capacities

change with workload and cannot satisfy users’ requests at any time from the cloud

provider’s perspective because cloud services can be shared by multiple tasks. Cloud service providers provide available time slots for new user’s requests based on

available capacities. In this paper, we consider workflow scheduling with deadline

and time slot availability in cloud computing. An iterated heuristic framework is

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presented for the problem under study which mainly consists of initial solution

construction, improvement, and perturbation. Three initial solution construction strategies, two greedy- and fair-based improvement strategies and a perturbation

strategy are proposed. Different strategies in the three phases result in several

heuristics. Experimental results show that different initial solution and improvement strategies have different effects on solution qualities.

S3I_SEC_002: Publicly Verifiable Inner Product Evaluation over Outsourced

Data Streams under Multiple Keys

Uploading data streams to a resource-rich cloud server for inner product

evaluation, an essential building block in many popular stream applications (e.g., statistical monitoring), is appealing to many companies and individuals. On the other

hand, verifying the result of the remote computation plays a crucial role in addressing

the issue of trust. Since the outsourced data collection likely comes from multiple data sources, it is desired for the system to be able to pinpoint the originator of errors by

allotting each data source a unique secret key, which requires the inner product

verification to be performed under any two parties’ different keys. However, the present solutions either depend on a single key assumption or powerful yet practically

inefficient fully homomorphic cryptosystems. In this paper, we focus on the more

challenging multi-key scenario where data streams are uploaded by multiple data

sources with distinct keys. We first present a novel homomorphic verifiable tag technique to publicly verify the outsourced inner product computation on the dynamic

data streams, and then extend it to support the verification of matrix product

computation. We prove the security of our scheme in the random oracle model. Moreover, the experimental result also shows the practicability of our design.

S3I_DPS16_001 - Cost Minimization for Rule Caching in Software Defined

Networking Software-defined networking (SDN) is an emerging network paradigm that

simplifies network management by decoupling the control plane and data plane, such

that switches become simple data forwarding devices and network management is controlled by logically centralized servers. In SDN-enabled networks, network flow is

managed by a set of associated rules that are maintained by switches in their local

Ternary Content Addressable Memories (TCAMs) which support high-speed parallel

lookup on wildcard patterns. Since TCAM is an expensive hardware and extremely power-hungry, each switch has only limited TCAM space and it is inefficient and

even infeasible to maintain all rules at local switches. On the other hand, if we

eliminate TCAM occupation by forwarding all packets to the centralized controller for

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processing, it results in a long delay and heavy processing burden on the controller. In

this paper, we strive for the fine balance between rule caching and remote packet processing by formulating a minimum weighted flow provisioning ( MWFP) problem

with an objective of minimizing the total cost of TCAM occupation and remote packet

processing. We propose an efficient offline algorithm if the network traffic is given, otherwise, we propose two online algorithms with guaranteed competitive ratios.

Finally, we conduct extensive experiments by simulations using real network traffic

traces. The simulation results demonstrate that our proposed algorithms can

significantly reduce the total cost of remote controller processing and TCAM occupation, and the solutions obtained are nearly optimal.

S3I_DPS16_002 - On Binary Decomposition based Privacy-preserving Aggregation Schemes in Real-time Monitoring Systems

In real-time monitoring systems, fine-grained measurements would pose great

privacy threats to the participants as real-time measurements could disclose accurate

people-centric activities. Differential privacy has been proposed to formalize and guide the design of privacy-preserving schemes. Nonetheless, due to the correlations

and high fluctuations in time-series data, it is hard to achieve an effective privacy and

utility tradeoff by differential privacy mechanisms. To address this issue, in this paper, we first proposed novel multi-dimensional decomposition based schemes to

compress the noise and enhance the utility in differential privacy. The key idea is to

decompose the measurements into multi-dimensional records and to achieve differential privacy in bounded dimensions so that the error caused by unbounded

measurements can be significantly reduced. We then extended our developed scheme

and developed a binary decomposition scheme for privacy-preserving time-series

aggregation in real-time monitoring systems. Through a combination of extensive theoretical analysis and experiments, our data shows that our proposed schemes can

effectively improve usability while achieving the same level of differential privacy

than existing schemes. S3I_DPS16_003 - An OpenMP Extension that Supports Thread-Level

Speculation

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OpenMP directives are the de-facto standard for shared-memory parallel

programming. However, OpenMP does not guarantee the correctness of the parallel execution of a given loop if runtime data dependences arise. Consequently, many

highly-parallel regions cannot be safely parallelized with OpenMP due to the

possibility of a dependence violation. In this paper, we propose to augment OpenMP capabilities, by adding thread-level speculation (TLS) support. Our contribution is

threefold. First, we have defined a new speculative clause for variables inside parallel

loops. This clause ensures that all accesses to these variables will be carried out

according to sequential semantics. Second, we have created a new, software-based TLS runtime library to ensure correctness in the parallel execution of OpenMP loops

that include speculative variables. Third, we have developed a new GCC plugin,

which seamlessly translates our OpenMP speculative clause into calls to our TLS runtime engine. The result is the ATLaS C Compiler framework, which takes

advantage of TLS techniques to expand OpenMP functionalities, and guarantees the

sequential semantics of any parallelized loop.

S3I_DPS16_004 - Real-Time Semantic Search Using Approximate Methodology

for Large-Scale Storage Systems The challenges of handling the explosive growth in data volume and

complexity cause the increasing needs for semantic queries. The semantic queries can

be interpreted as the correlation-aware retrieval, while containing approximate results.

Existing cloud storage systems mainly fail to offer an adequate capability for the semantic queries. Since the true value or worth of data heavily depends on how

efficiently semantic search can be carried out on the data in (near-) real-time, large

fractions of data end up with their values being lost or significantly reduced due to the data staleness. To address this problem, we propose a near-real-time and cost-

effective semantic queries based methodology, called FAST. The idea behind FAST is

to explore and exploit the semantic correlation within and among datasets via correlation-aware hashing and manageable flat-structured addressing to significantly

reduce the processing latency, while incurring acceptably small loss of data-search

accuracy. The near-real-time property of FAST enables rapid identification of

correlated files and the significant narrowing of the scope of data to be processed. FAST supports several types of data analytics, which can be implemented in existing

searchable storage systems. We conduct a real-world use case in which children

reported missing in an extremely crowded environment (e.g., a highly popular scenic spot on a peak tourist day) are identified in a timely fashion by analyzing 60 million

images using FAST. FAST is further improved by using semantic-aware namespace

to provide dynamic and adaptive namespace management for ultra-large storage systems. Extensive experimental results demonstrate the efficiency and efficacy of

FAST in the performance improvements.

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S3I_MC16_001: Service Usage Classification with Encrypted Internet Traffic in

Mobile Messaging Apps

The rapid adoption of mobile messaging Apps has enabled us to collect

massive amount of encrypted Internet traffic of mobile messaging. The classification

of this traffic into different types of in-App service usages can help for intelligent

network management, such as managing network bandwidth budget and providing quality of services. Traditional approaches for classification of Internet traffic rely on

packet inspection, such as parsing HTTP headers. However, messaging Apps are

increasingly using secure protocols, such as HTTPS and SSL, to transmit data. This imposes significant challenges on the performances of service usage classification by

packet inspection. To this end, in this paper, we investigate how to exploit encrypted

Internet traffic for classifying in-App usages. Specifically, we develop a system, named CUMMA, for classifying service usages of mobile messaging Apps by jointly

modeling user behavioral patterns, network traffic characteristics and temporal

dependencies. Along this line, we first segment Internet traffic from traffic-flows into

sessions with a number of dialogs in a hierarchical way. Also, we extract the discriminative features of traffic data from two perspectives: (i) packet length and (ii)

time delay. Next, we learn a service usage predictor to classify these segmented

dialogs into single-type usages or outliers. In addition, we design a clustering Hidden Markov Model (HMM) based method to detect mixed dialogs from outliers and

decompose mixed dialogs into sub-dialogs of single-type usage. Indeed, CUMMA

enables mobile analysts to identify service usages and analyze end-user in-App behaviors even for encrypted Internet traffic. Finally, the extensive experiments on

real-world messaging data demonstrate the effectiveness and efficiency of the

proposed method for service usage classification.

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S3I_CC16_001: Dynamic and Public Auditing with Fair Arbitration for Cloud Data Cloud users no longer physically possess their data, so how to ensure the integrity of their

outsourced data becomes a challenging task. Recently proposed schemes such as “provable data possession” and “proofs of retrievability” are designed to address this problem, but they are

designed to audit static archive data and therefore lack of data dynamics support. Moreover, threat models in these schemes usually assume an honest data owner and focus on detecting a dishonest cloud service provider despite the fact that clients may also misbehave. This paper

proposes a public auditing scheme with data dynamics support and fairness arbitration of potential disputes. In particular, we design an index switcher to eliminate the limitation of index

usage in tag computation in current schemes and achieve efficient handling of data dynamics. To address the fairness problem so that no party can misbehave without being detected, we further extend existing threat models and adopt signature exchange idea to design fair arbitration

protocols, so that any possible dispute can be fairly settled. The security analysis shows our scheme is provably secure, and the performance evaluation demonstrates the overhead of data

dynamics and dispute arbitration are reasonable. S3I_CC16_002: Enabling Cloud Storage Auditing with Verifiable Outsourcing of Key

Updates Key-exposure resistance has always been an important issue for in-depth cyber defence in many security applications. Recently, how to deal with the key exposure problem in the settings

of cloud storage auditing has been proposed and studied. To address the challenge, existing solutions all require the client to update his secret keys in every time period, which may

inevitably bring in new local burdens to the client, especially those with limited computation resources, such as mobile phones. In this paper, we focus on how to make the key updates as transparent as possible for the client and propose a new paradigm called cloud storage auditing

with verifiable outsourcing of key updates. In this paradigm, key updates can be safely outsourced to some authorized party, and thus the key-update burden on the client will be kept

minimal. In particular, we leverage the third party auditor (TPA) in many existing public auditing designs, let it play the role of authorized party in our case, and make it in charge of both the storage auditing and the secure key updates for key-exposure resistance. In our design, TPA

only needs to hold an encrypted version of the client's secret key while doing all these burdensome tasks on behalf of the client. The client only needs to download the encrypted secret

key from the TPA when uploading new files to cloud. Besides, our design also equips the client with capability to further verify the validity of the encrypted secret keys provided by the TPA. All these salient features are carefully designed to make the whole auditing procedure with key

exposure resistance as transparent as possible for the client. We formalize the definition and the security model of this paradigm. The security proof and the performance simulation show that

our detailed design instantiations are secure and efficient. S3I_CC16_003: Providing User Security Guarantees in Public Infrastructure Clouds

The infrastructure cloud (IaaS) service model offers improved resource flexibility and availability, where tenants – insulated from the minutiae of hardware maintenance – rent

computing resources to deploy and operate complex systems. Large-scale services running on IaaS platforms demonstrate the viability of this model; nevertheless, many organizations operating on sensitive data avoid migrating operations to IaaS platforms due to security

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concerns. In this paper, we describe a framework for data and operation security in IaaS, consisting of protocols for a trusted launch of virtual machines and domain-based storage

protection. We continue with an extensive theoretical analysis with proofs about protocol resistance against attacks in the defined threat model. The protocols allow trust to be established

by remotely attesting host platform configuration prior to launching guest virtual machines and ensure confidentiality of data in remote storage, with encryption keys maintained outside of the IaaS domain. Presented experimental results demonstrate the validity and efficiency of the

proposed protocols. The framework prototype was implemented on a test bed operating a public electronic health record system, showing that the proposed protocols can be integrated into

existing cloud environments. S3I_CC16_004: Attribute-Based Data Sharing Scheme Revisited in Cloud Computing

Cipher-text-policy attribute-based encryption (CP-ABE) is a very promising encryption technique for secure data sharing in the context of cloud computing. Data owner is allowed to

fully control the access policy associated with his data which to be shared. However, CP-ABE is limited to a potential security risk that is known as key escrow problem, whereby the secret keys of users have to be issued by a trusted key authority. Besides, most of the existing CP-ABE

schemes cannot support attribute with arbitrary state. In this paper, we revisit attribute-based data sharing scheme in order to solve the key escrow issue but also improve the expressiveness of attribute, so that the resulting scheme is more friendly to cloud computing applications. We

propose an improved two-party key issuing protocol that can guarantee that neither key authority nor cloud service provider can compromise the whole secret key of a user individually.

Moreover, we introduce the concept of attribute with weight, being provided to enhance the expression of attribute, which can not only extend the expression from binary to arbitrary state, but also lighten the complexity of access policy. Therefore, both storage cost and encryption

complexity for a ciphertext are relieved. The performance analysis and the security proof show that the proposed scheme is able to achieve efficient and secure data sharing in cloud computing.

S3I_CC16_005: An Efficient File Hierarchy Attribute-Based Encryption Scheme in Cloud

Computing

Ciphertext-policy attribute-based encryption (CP-ABE) has been a preferred encryption technology to solve the challenging problem of secure data sharing in cloud computing. The

shared data files generally have the characteristic of multilevel hierarchy, particularly in the area of healthcare and the military. However, the hierarchy structure of shared files has not been explored in CP-ABE. In this paper, an efficient file hierarchy attribute-based encryption scheme

is proposed in cloud computing. The layered access structures are integrated into a single access structure, and then, the hierarchical files are encrypted with the integrated access structure. The

ciphertext components related to attributes could be shared by the files. Therefore, both ciphertext storage and time cost of encryption are saved. Moreover, the proposed scheme is proved to be secure under the standard assumption. Experimental simulat ion shows that the

proposed scheme is highly efficient in terms of encryption and decryption. With the number of the files increasing, the advantages of our scheme become more and more conspicuous.

S3I_CC16_006: Identity-Based Proxy-Oriented Data Uploading and Remote Data Integrity

Checking in Public Cloud

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More and more clients would like to store their data to public cloud servers

(PCSs) along with the rapid development of cloud computing. New security problems have to be solved in order to help more clients process their data in public cloud.

When the client is restricted to access PCS, he will delegate its proxy to process his

data and upload them. On the other hand, remote data integrity checking is also an important security problem in public cloud storage. It makes the clients check whether

their outsourced data are kept intact without downloading the whole data. From the

security problems, we propose a novel proxy-oriented data uploading and remote data

integrity checking model in identity-based public key cryptography: identity-based proxy-oriented data uploading and remote data integrity checking in public cloud (ID-

PUIC). We give the formal definition, system model, and security model. Then, a

concrete ID-PUIC protocol is designed using the bilinear pairings. The proposed ID-PUIC protocol is provably secure based on the hardness of computational Diffie-

Hellman problem. Our ID-PUIC protocol is also efficient and flexible. Based on the

original client's authorization, the proposed ID-PUIC protocol can realize private remote data integrity checking, delegated remote data integrity checking, and public

remote data integrity checking.

S3I_CC16_007: Enabling Cloud Storage Auditing With Verifiable Outsourcing

of Key Updates

Key-exposure resistance has always been an important issue for in-depth cyber

defence in many security applications. Recently, how to deal with the key exposure problem in the settings of cloud storage auditing has been proposed and studied. To

address the challenge, existing solutions all require the client to update his secret keys

in every time period, which may inevitably bring in new local burdens to the client, especially those with limited computation resources, such as mobile phones. In this

paper, we focus on how to make the key updates as transparent as possible for the

client and propose a new paradigm called cloud storage auditing with verifiable outsourcing of key updates. In this paradigm, key updates can be safely outsourced to

some authorized party, and thus the key-update burden on the client will be kept

minimal. In particular, we leverage the third party auditor (TPA) in many existing

public auditing designs, let it play the role of authorized party in our case, and make it in charge of both the storage auditing and the secure key updates for key-exposure

resistance. In our design, TPA only needs to hold an encrypted version of the client's

secret key while doing all these burdensome tasks on behalf of the client. The client only needs to download the encrypted secret key from the TPA when uploading new

files to cloud. Besides, our design also equips the client with capability to further

verify the validity of the encrypted secret keys provided by the TPA. All these salient features are carefully designed to make the whole auditing procedure with key

exposure resistance as transparent as possible for the client. We formalize the

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definition and the security model of this paradigm. The security proof and the

performance simulation show that our detailed design instantiations are secure and efficient.

S3I_CC16_008: Outsourcing Eigen-Decomposition and Singular Value Decomposition of Large Matrix to a Public Cloud

Cloud computing enables customers with limited computational resources to

outsource their huge computation workloads to the cloud with massive computational

power. However, in order to utilize this computing paradigm, it presents various challenges that need to be addressed, especially security. As eigen-decomposition

(ED) and singular value decomposition (SVD) of a matrix are widely applied in

engineering tasks, we are motivated to design secure, correct, and efficient protocols for outsourcing the ED and SVD of a matrix to a malicious cloud in this paper. In

order to achieve security, we employ efficient privacy-preserving transformations to

protect both the input and output privacy. In order to check the correctness of the result returned from the cloud, an efficient verification algorithm is employed. A

computational complexity analysis shows that our protocols are highly efficient. We

also introduce an outsourcing principle component analysis as an application of our two proposed protocols.

S3I_CC16_009: Performance limitations of a text search application running in cloud instances

This article analyzes the performance of MySQL in clouds based on commodity

hardware in order to identify the bottlenecks in the execution of series of scripts developed on the SQL standard. The developed scripts were designed in order to

perform text search in a considerable amount of records. Two types of platforms were

employed: a physical machine that serves as host and an instance within a cloud

infrastructure. The results show that the intensive use of a relational database presents a greater loss of performance in a cloud instance due limitations in the primary storage

system that was employed in the cloud infrastructure.

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S3I_CC16_0010: A dynamic load balancing method of cloud-center based on

SDN In order to achieve dynamic load balancing based on data flow level, in this

paper, we apply SDN technology to the cloud data center, and propose a dynamic load

balancing method of cloud center based on SDN. The approach of using the SDN technology in the current task scheduling flexibility, accomplish real-time monitoring

of the service node flow and load condition by the OpenFlow protocol. When the load

of system is imbalanced, the controller can allocate globally network resources.

What's more, by using dynamic correction, the load of the system is not obvious tilt in the long run. The results of simulation show that this approach can realize and ensure

that the load will not tilt over a long period of time, and improve the system

throughput.

S3I_CC16_0011: Encryption-Based Solution for Data Sovereignty in Federated

Clouds The rapidly growing demand for cloud services in the current business practice

has favored the success of the hybrid clouds and the advent of cloud federation. The

available literature of this topic has focused on middleware abstraction to interoperate heterogeneous cloud platforms and orchestrate different management and business

models. However, cloud federation implies serious security and privacy issues with

respect to data sovereignty when data is outsourced across different judicial and legal

systems. This column describes a solution that applies encryption to protect data sovereignty in federated clouds rather than restricting the elasticity and migration of

data across federated clouds.

S3I_CC16_0012: Attribute-based access control for multi-authority systems with

constant size ciphertext in cloud computing

In most existing CP-ABE schemes, there is only one authority in the system and all the public keys and private keys are issued by this authority, which incurs

ciphertext size and computation costs in the encryption and decryption operations that

depend at least linearly on the number of attributes involved in the access policy. We

propose an efficient multi-authority CP-ABE scheme in which the authorities need not interact to generate public information during the system initialization phase. Our

scheme has constant ciphertext length and a constant number of pairing computations.

Our scheme can be proven CPA-secure in random oracle model under the decision q-BDHE assumption. When user's attributes revocation occurs, the scheme transfers

most re-encryption work to the cloud service provider, reducing the data owner's

computational cost on the premise of security. Finally the analysis and simulation result show that the schemes proposed in this thesis ensure the privacy and secure

access of sensitive data stored in the cloud server, and be able to cope with the

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dynamic changes of users' access privileges in large-scale systems. Besides, the multi-

authority ABE eliminates the key escrow problem, achieves the length of ciphertext optimization and enhances the efficiency of the encryption and decryption operations.

S3I_CC16_0013: AMTS: Adaptive multi-objective task scheduling strategy in cloud computing

Task scheduling in cloud computing environments is a multi-objective

optimization problem, which is NP hard. It is also a challenging problem to find an

appropriate trade-off among resource utilization, energy consumption and Quality of Service (QoS) requirements under the changing environment and diverse tasks.

Considering both processing time and transmission time, a PSO-based Adaptive

Multi-objective Task Scheduling (AMTS) Strategy is proposed in this paper. First, the task scheduling problem is formulated. Then, a task scheduling policy is advanced to

get the optimal resource utilization, task completion time, average cost and average

energy consumption. In order to maintain the particle diversity, the adaptive acceleration coefficient is adopted. Experimental results show that the improved PSO

algorithm can obtain quasi-optimal solutions for the cloud task scheduling problem.

S3I_CC16_0014: Efficient R-Tree Based Indexing Scheme for Server-Centric

Cloud Storage System

Cloud storage system poses new challenges to the community to support

efficient concurrent querying tasks for various data-intensive applications, where indices always hold important positions. In this paper, we explore a practical method

to construct a two-layer indexing scheme for multi-dimensional data in diverse server-

centric cloud storage system. We first propose RT-HCN, an indexing scheme integrating R-tree based indexing structure and HCN-based routing protocol. RT-

HCN organizes storage and compute nodes into an HCN overlay, one of the newly

proposed sever-centric data center topologies. Based on the properties of HCN, we design a specific index mapping technique to maintain layered global indices and

corresponding query processing algorithms to support efficient query tasks. Then, we

expand the idea of RT-HCN onto another server-centric data center topology DCell,

discovering a potential generalized and feasible way of deploying two-layer indexing schemes on other server-centric networks. Furthermore, we prove theoretically that

RT-HCN is both space-efficient and query-efficient, by which each node actually

maintains a tolerable number of global indices while high concurrent queries can be processed within accepted overhead. We finally conduct targeted experiments on

Amazon's EC2 platforms, comparing our design with RT-CAN, a similar indexing

scheme for traditional P2P network. The results validate the query efficiency, especially the speedup of point query of RT-HCN, depicting its potential applicability

in future data centers.

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S3I_CC16_0015: Dynamic Certification of Cloud Services: Trust, but Verify! Although intended to ensure cloud service providers' security, reliability, and

legal compliance, current cloud service certifications are quickly outdated. Dynamic

certification, on the other hand, provides automated monitoring and auditing to verify cloud service providers' ongoing adherence to certification requirements.

S3I_CC16_0016: Privacy preserving and delegated access control for cloud

applications In cloud computing applications, users' data and applications are hosted by

cloud providers. This paper proposed an access control scheme that uses a

combination of discretionary access control and cryptographic techniques to secure users' data and applications hosted by cloud providers. Many cloud applications

require users to share their data and applications hosted by cloud providers. To

facilitate resource sharing, the proposed scheme allows cloud users to delegate their access permissions to other users easily. Using the access control policies that guard

the access to resources and the credentials submitted by users, a third party can infer

information about the cloud users. The proposed scheme uses cryptographic techniques to obscure the access control policies and users' credentials to ensure the

privacy of the cloud users. Data encryption is used to guarantee the confidentiality of

data. Compared with existing schemes, the proposed scheme is more flexible and easy

to use. Experiments showed that the proposed scheme is also efficient.

S3I_CC16_0017: Auditing a Cloud Provider’s Compliance With Data Backup

Requirements: A Game Theoretical Analysis The new developments in cloud computing have introduced significant security

challenges to guarantee the confidentiality, integrity, and availability of outsourced

data. A service level agreement (SLA) is usually signed between the cloud provider (CP) and the customer. For redundancy purposes, it is important to verify the CP’s

compliance with data backup requirements in the SLA. There exist a number of

security mechanisms to check the integrity and availability of outsourced data. This

task can be performed by the customer or be delegated to an independent entity that we will refer to as the verifier. However, checking the availability of data introduces

extra costs, which can discourage the customer of performing data verification too

often. The interaction between the verifier and the CP can be captured using game theory in order to find an optimal data verification strategy. In this paper, we

formulate this problem as a two player non-cooperative game. We consider the case in

which each type of data is replicated a number of times, which can depend on a set of parameters including, among others, its size and sensitivity. We analyze the strategies

of the CP and the verifier at the Nash equilibrium and derive the expected behavior of

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both the players. Finally, we validate our model numerically on a case study and

explain how we evaluate the parameters in the model.

S3I_CC16_0018: An Efficient Privacy-Preserving Ranked Keyword Search

Method Cloud data owners prefer to outsource documents in an encrypted form for the

purpose of privacy preserving. Therefore it is essential to develop efficient and

reliable ciphertext search techniques. One challenge is that the relationship between

documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data

centers has experienced a dramatic growth. This will make it even more challenging

to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical

clustering method is proposed to support more search semantics and also to meet the

demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance

threshold, and then partitions the resulting clusters into sub-clusters until the

constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size

increase of document collection. In order to verify the authenticity of search results, a

structure called minimum hash sub-tree is designed in this paper. Experiments have

been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed

method increases linearly whereas the search time of the traditional method increases

exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents

S3I_CC16_0019: Towards Building Forensics Enabled Cloud Through Secure Logging-as-a-Service

Collection and analysis of various logs (e.g., process logs, network logs) are

fundamental activities in computer forensics. Ensuring the security of the activity logs

is therefore crucial to ensure reliable forensics investigations. However, because of the black-box nature of clouds and the volatility and co-mingling of cloud data,

providing the cloud logs to investigators while preserving users' privacy and the

integrity of logs is challenging. The current secure logging schemes, which consider the logger as trusted cannot be applied in clouds since there is a chance that cloud

providers (logger) collude with malicious users or investigators to alter the logs. In

this paper, we analyze the threats on cloud users' activity logs considering the collusion between cloud users, providers, and investigators. Based on the threat

model, we propose Secure-Logging-as-a-Service ( SecLaaS), which preserves various

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logs generated for the activity of virtual machines running in clouds and ensures the

confidentiality and integrity of such logs. Investigators or the court authority can only access these logs by the RESTful APIs provided by SecLaaS, which ensures

confidentiality of logs. The integrity of the logs is ensured by hash-chain scheme and

proofs of past logs published periodically by the cloud providers. In prior research, we used two accumulator schemes Bloom filter and RSA accumulator to build the proofs

of past logs. In this paper, we propose a new accumulator scheme - Bloom-Tree,

which performs better than the other two accumulators in terms of time and space

requirement.

S3I_CC16_0020: A Genetic Algorithm for Virtual Machine Migration in

Heterogeneous Mobile Cloud Computing Mobile Cloud Computing (MCC) improves the performance of a mobile

application by executing it at a resourceful cloud server that can minimize execution

time compared to a resource-constrained mobile device. Virtual Machine (VM) migration in MCC brings cloud resources closer to a user so as to further minimize the

response time of an offloaded application. Such resource migration is very effective

for interactive and real-time applications. However, the key challenge is to find an optimal cloud server for migration that offers the maximum reduction in computation

time. In this paper, we propose a Genetic Algorithm (GA) based VM migration

model, namely GAVMM, for heterogeneous MCC system. In GAVMM, we take user

mobility and load of the cloud servers into consideration to optimize the effectiveness of VM migration. The goal of GAVMM is to select the optimal cloud server for a

mobile VM and to minimize the total number of VM migrations, resulting in a

reduced task execution time. Additionally, we present a thorough numerical evaluation to investigate the effectiveness of our proposed model compared to the

state-of-the-art VM migration policies.

S3I_CC16_0021: Online Resource Scheduling Under Concave Pricing for Cloud

Computing

With the booming growth of cloud computing industry, computational

resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service

providers offer several pricing strategies such as pay as you go, pay less per unit when

you use more (so called volume discount), and pay even less when you reserve. The diverse pricing schemes among different IaaS service providers or even in the same

provider form a complex economic landscape that nurtures the market of cloud

brokers. By strategically scheduling multiple customers' resource requests, a cloud broker can fully take advantage of the discounts offered by cloud service providers. In

this paper, we focus on how a broker may help a group of customers to fully utilize

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the volume discount pricing strategy offered by cloud service providers through cost-

efficient online resource scheduling. We present a randomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower bound of its

competitive ratio. Our simulation shows that ROSA achieves a competitive ratio close

to the theoretical lower bound under a special case cost function. Trace driven simulation using Google cluster data demonstrates that ROSA is superior to the

conventional online scheduling algorithms in terms of cost saving.

S3I_CC16_0022: Dynamic Bin Packing for On-Demand Cloud Resource Allocation

Dynamic Bin Packing (DBP) is a variant of classical bin packing, which

assumes that items may arrive and depart at arbitrary times. Existing works on DBP generally aim to minimize the maximum number of bins ever used in the packing. In

this paper, we consider a new version of the DBP problem, namely, the MinTotal

DBP problem which targets at minimizing the total cost of the bins used overtime. It is motivated by the request dispatching problem arising from cloud gaming systems.

We analyze the competitive ratios of the modified versions of the commonly used

First Fit, Best Fit, and Any Fit packing (the family of packing algorithms that open a new bin only when no currently open bin can accommodate the item to be packed)

algorithms for the MinTotal DBP problem. We show that the competitive ratio of Any

Fit packing cannot be better than μ + 1, where μ is the ratio of the maximum item

duration to the minimum item duration. The competitive ratio of Best Fit packing is not bounded for any given μ. For First Fit packing, if all the item sizes are smaller

than 1/β of the bin capacity (β> 1 is a constant), the competitive ratio has an upper

bound of β/β-1·μ+3β/β-1 + 1. For the general case, the competitive ratio of First Fit packing has an upper bound of 2μ + 7. We also propose a Hybrid First Fit packing

algorithm that can achieve a competitive ratio no larger than 5/4 μ + 19/4 when μ is

not known and can achieve a competitive ratio no larger than μ + 5 when μ is known.

S3I_CC16_0023: A Scalable Data Chunk Similarity based Compression

Approach for Efficient Big Sensing Data Processing on Cloud

https://3.bp.blogspot.com/-nwhbjjORWmU/VzlVKLx7UEI/AAAAAAAAED8/rYM4tGZhOR0hWYRM5YEqEd59NorNs0ZHwCLcB/s1600/tpds-gagraphic-2393868.jpg

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Big sensing data is prevalent in both industry and scientific research

applications where the data is generated with high volume and velocity. Cloud computing provides a promising platform for big sensing data processing and storage

as it provides a flexible stack of massive computing, storage, and software services in

a scalable manner. Current big sensing data processing on Cloud have adopted some data compression techniques. However, due to the high volume and velocity of big

sensing data, traditional data compression techniques lack sufficient efficiency and

scalability for data processing. Based on specific on-Cloud data compression

requirements, we propose a novel scalable data compression approach based on calculating similarity among the partitioned data chunks. Instead of compressing basic

data units, the compression will be conducted over partitioned data chunks. To restore

original data sets, some restoration functions and predictions will be designed. MapReduce is used for algorithm implementation to achieve extra scalability on

Cloud. With real world meteorological big sensing data experiments on U-Cloud

platform, we demonstrate that the proposed scalable compression approach based on data chunk similarity can significantly improve data compression efficiency with

affordable data accuracy loss.

S3I_J16_001 - SecRBAC: Secure data in the Clouds

Most current security solutions are based on perimeter security. However,

Cloud computing breaks the organization perimeters. When data resides in the Cloud, they reside outside the organizational bounds. This leads users to a loos of control

over their data and raises reasonable security concerns that slow down the adoption of

Cloud computing. Is the Cloud service provider accessing the data? Is it legitimately applying the access control policy defined by the user? This paper presents a data-

centric access control solution with enriched role-based expressiveness in which

security is focused on protecting user data regardless the Cloud service provider that holds it. Novel identity-based and proxy re-encryption techniques are used to protect

the authorization model. Data is encrypted and authorization rules are

cryptographically protected to preserve user data against the service provider access

or misbehavior. The authorization model provides high expressiveness with role hierarchy and resource hierarchy support. The solution takes advantage of the logic

formalism provided by Semantic Web technologies, which enables advanced rule

management like semantic conflict detection. A proof of concept implementation has been developed and a working prototypical deployment of the proposal has been

integrated within Google services.

S3I_J16_002 - Trust Agent-Based Behavior Induction in Social Networks

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The essence of social networks is that they can influence people's public

opinions and group behaviors form quickly. Negative group behavior influences societal stability significantly, but existing behavior-induction approaches are too

simple and inefficient. To automatically and efficiently induct behavior in social

networks, this article introduces trust agents and designs their features according to group behavior features. In addition, a dynamics control mechanism can be generated

to coordinate participant behaviors in social networks to avoid a specific restricted

negative group behavior.

S3I_J16_003 - A Shoulder Surfing Resistant Graphical Authentication System

Authentication based on passwords is used largely in applications for computer

security and privacy. However, human actions such as choosing bad passwords and inputting passwords in an insecure way are regarded as ”the weakest link” in the

authentication chain. Rather than arbitrary alphanumeric strings, users tend to choose

passwords either short or meaningful for easy memorization. With web applications and mobile apps piling up, people can access these applications anytime and

anywhere with various devices. This evolution brings great convenience but also

increases the probability of exposing passwords to shoulder surfing attacks. Attackers can observe directly or use external recording devices to collect users’ credentials. To

overcome this problem, we proposed a novel authentication system PassMatrix, based

on graphical passwords to resist shoulder surfing attacks. With a one-time valid login

indicator and circulative horizontal and vertical bars covering the entire scope of pass-images, PassMatrix offers no hint for attackers to figure out or narrow down the

password even they conduct multiple camera-based attacks. We also implemented a

PassMatrix prototype on Android and carried out real user experiments to evaluate its memorability and usability. From the experimental result, the proposed system

achieves better resistance to shoulder surfing attacks while maintaining usability.

S3I_J16_004 - A Locality Sensitive Low-Rank Model for Image Tag Completion

Many visual applications have benefited from the outburst of web images, yet

the imprecise and incomplete tags arbitrarily provided by users, as the thorn of the

rose, may hamper the performance of retrieval or indexing systems relying on such data. In this paper, we propose a novel locality sensitive low-rank model for image tag

completion, which approximates the global nonlinear model with a collection of local

linear models. To effectively infuse the idea of locality sensitivity, a simple and effective pre-processing module is designed to learn suitable representation for data

partition, and a global consensus regularizer is introduced to mitigate the risk of

overfitting. Meanwhile, low-rank matrix factorization is employed as local models, where the local geometry structures are preserved for the low-dimensional

representation of both tags and samples. Extensive empirical evaluations conducted

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on three datasets demonstrate the effectiveness and efficiency of the proposed method,

where our method outperforms pervious ones by a large margin.

S3I_J16_005 - Quality-Aware Subgraph Matching Over Inconsistent

Probabilistic Graph Databases Resource Description Framework (RDF) has been widely used in the Semantic

Web to describe resources and their relationships. The RDF graph is one of the most

commonly used representations for RDF data. However, in many real applications

such as the data extraction/integration, RDF graphs integrated from different data sources may often contain uncertain and inconsistent information (e.g., uncertain

labels or that violate facts/rules), due to the unreliability of data sources. In this paper,

we formalize the RDF data by inconsistent probabilistic RDF graphs, which contain both inconsistencies and uncertainty. With such a probabilistic graph model, we focus

on an important problem, quality-aware subgraph matching over inconsistent

probabilistic RDF graphs (QA-gMatch), which retrieves subgraphs from inconsistent probabilistic RDF graphs that are isomorphic to a given query graph and with high

quality scores (considering both consistency and uncertainty). In order to efficiently

answer QA-gMatch queries, we provide two effective pruning methods, namely adaptive label pruning and quality score pruning, which can greatly filter out false

alarms of subgraphs. We also design an effective index to facilitate our proposed

pruning methods, and propose an efficient approach for processing QA-gMatch

queries. Finally, we demonstrate the efficiency and effectiveness of our proposed approaches through extensive experiments.

S3I_J16_006 - Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search

With advances in geo-positioning technologies and geo-location services, there

are a rapidly growing amount of spatio-textual objects collected in many applications such as location based services and social networks, in which an object is described

by its spatial location and a set of keywords (terms). Consequently, the study of

spatial keyword search which explores both location and textual description of the

objects has attracted great attention from the commercial organizations and research communities. In the paper, we study two fundamental problems in the spatial keyword

queries: top $k$ spatial keyword search (TOPK-SK), and batch top $k$ spatial

keyword search (BTOPK-SK). Given a set of spatio-textual objects, a query location and a set of query keywords, the TOPK-SK retrieves the closest $k$ objects each of

which contains all keywords in the query. BTOPK-SK is the batch processing of sets

of TOPK-SK queries. Based on the inverted index and the linear quadtree, we propose a novel index structure, called inverted linear quadtree (IL-Quadtree), which is

carefully designed to exploit both spatial and keyword based pruning techniques to

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effectively reduce the search space. An efficient algorithm is then developed to tackle

top $k$ spatial keyword sea- ch. To further enhance the filtering capability of the signature of linear quadtree, we propose a partition based method. In addition, to deal

with BTOPK-SK, we design a new computing paradigm which partition the queries

into groups based on both spatial proximity and the textual relevance between queries. We show that the IL-Quadtree technique can also efficiently support BTOPK-SK.

Comprehensive experiments on real and synthetic data clearly demonstrate the

efficiency of our methods.

S3I_J16_007 - Practical Approximate k Nearest Neighbor Queries with Location

and Query Privacy

In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the

mobile user. In this paper, we study approximate k nearest neighbor (kNN) queries

where the mobile user queries the location-based service (LBS) provider about approximate k nearest points of interest (POIs) on the basis of his current location. We

propose a basic solution and a generic solution for the mobile user to preserve his

location and query privacy in approximate kNN queries. The proposed solutions are mainly built on the Paillier public-key cryptosystem and can provide both location and

query privacy. To preserve query privacy, our basic solution allows the mobile user to

retrieve one type of POIs, for example, approximate k nearest car parks, without

revealing to the LBS provider what type of points is retrieved. Our generic solution can be applied to multiple discrete type attributes of private location-based queries.

Compared with existing solutions for kNN queries with location privacy, our solution

is more efficient. Experiments have shown that our solution is practical for kNN queries.

S3I_J16_008 - Privacy Protection for Wireless Medical Sensor Data In recent years, wireless sensor networks have been widely used in healthcare

applications, such as hospital and home patient monitoring. Wireless medical sensor

networks are more vulnerable to eavesdropping, modification, impersonation and

replaying attacks than the wired networks. A lot of work has been done to secure wireless medical sensor networks. The existing solutions can protect the patient data

during transmission, but cannot stop the inside attack where the administrator of the

patient database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent the inside attack by using multiple data servers to store patient

data. The main contribution of this paper is securely distributing the patient data in

multiple data servers and employing the Paillier and ElGamal cryptosystems to perform statistic analysis on the patient data without compromising the patients'

privacy.

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S3I_J16_009 - Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries over Encrypted Cloud Data

Using cloud computing, individuals can store their data on remote servers and

allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before

uploaded to the cloud. This, however, significantly limits the usability of outsourced

data due to the difficulty of searching over the encrypted data. In this paper, we

address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the

relevance scores and preference factors upon keywords which enable the precise

keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support

complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords.

Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the

security of the proposed schemes in terms of confidentiality of documents, privacy

protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed

schemes. Both the security analysis and experimental results demonstrate that the

proposed schemes can achieve the same security level comparing to the existing ones

and better performance in terms of functionality, query complexity and efficiency.

S3I_J16_010 - Leveraging Data Deduplication to Improve the Performance of

Primary Storage Systems in the Cloud With the explosive growth in data volume, the I/O bottleneck has become an

increasingly daunting challenge for big data analytics in the Cloud. Recent studies

have shown that moderate to high data redundancy clearly exists in primary storage systems in the Cloud. Our experimental studies reveal that data redundancy exhibits a

much higher level of intensity on the I/O path than that on disks due to relatively high

temporal access locality associated with small I/O requests to redundant data.

Moreover, directly applying data deduplication to primary storage systems in the Cloud will likely cause space contention in memory and data fragmentation on disks.

Based on these observations, we propose a performance-oriented I/O deduplication,

called POD, rather than a capacity-oriented I/O deduplication, exemplified by iDedup, to improve the I/O performance of primary storage systems in the Cloud without

sacrificing capacity savings of the latter. POD takes a two-pronged approach to

improving the performance of primary storage systems and minimizing performance overhead of deduplication, namely, a request-based selective deduplication technique,

called Select-Dedupe, to alleviate the data fragmentation and an adaptive memory

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management scheme, called iCache, to ease the memory contention between the

bursty read traffic and the bursty write traffic. We have implemented a prototype of POD as a module in the Linux operating system. The experiments conducted on our

lightweight prototype implementation of POD show that POD significantly

outperforms iDedup in the I/O performance measure by up to 87.9 percent with an average of 58.8 percent. Moreover, our evaluation results also show that POD

achieves comparable or better capacity savings than iDedup.

S3I_J16_011 - Two-Factor Data Security Protection Mechanism for Cloud Storage System

In this paper, we propose a two-factor data security protection mechanism with

factor revocability for cloud storage system. Our system allows a sender to send an encrypted message to a receiver through a cloud storage server. The sender only needs

to know the identity of the receiver but no other information (such as its public key or

its certificate). The receiver needs to possess two things in order to decrypt the ciphertext. The first thing is his/her secret key stored in the computer. The second

thing is a unique personal security device which connects to the computer. It is

impossible to decrypt the ciphertext without either piece. More importantly, once the security device is stolen or lost, this device is revoked. It cannot be used to decrypt

any ciphertext. This can be done by the cloud server which will immediately execute

some algorithms to change the existing ciphertext to be un-decryptable by this device.

This process is completely transparent to the sender. Furthermore, the cloud server cannot decrypt any ciphertext at any time. The security and efficiency analysis show

that our system is not only secure but also practical.

S3I_J16_012 - Providing Privacy-Aware Incentives in Mobile Sensing Systems

Mobile sensing relies on data contributed by users through their mobile device

(e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on

possible privacy leakage. To effectively promote user participation, both incentive and

privacy issues should be addressed. Although incentive and privacy have been

addressed separately in mobile sensing, it is still an open problem to address them simultaneously. In this paper, we propose two credit-based privacy-aware incentive

schemes for mobile sensing systems, where the focus is on privacy protection instead

of on the design of incentive mechanisms. Our schemes enable mobile users to earn credits by contributing data without leaking which data they have contributed, and

ensure that malicious users cannot abuse the system to earn unlimited credits.

Specifically, the first scheme considers scenarios where an online trusted third party (TTP) is available, and relies on the TTP to protect user privacy and prevent abuse

attacks. The second scheme considers scenarios where no online TTP is available. It

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applies blind signature, partially blind signature, and a novel extended Merkle tree

technique to protect user privacy and prevent abuse attacks. Security analysis and cost evaluations show that our schemes are secure and efficient.

S3I_J16_013 - A Simple Message-Optimal Algorithm for Random Sampling from a Distributed Stream

We present a simple, message-optimal algorithm for maintaining a random

sample from a large data stream whose input elements are distributed across multiple

sites that communicate via a central coordinator. At any point in time, the set of elements held by the coordinator represent a uniform random sample from the set of

all the elements observed so far. When compared with prior work, our algorithms

asymptotically improve the total number of messages sent in the system. We present a matching lower bound, showing that our protocol sends the optimal number of

messages up to a constant factor with large probability. We also consider the

important case when the distribution of elements across different sites is non-uniform, and show that for such inputs, our algorithm significantly outperforms prior solutions.

S3I_J16_014 - Multi-Grained Block Management to Enhance the Space Utilization of File Systems on PCM Storages

Phase-change memory (PCM) is a promising candidate as a storage medium to

resolve the performance gap between main memory and storage in battery-powered

mobile computing systems. However, it is more expensive than flash memory, and thus introduces a more serious storage capacity issue for low-cost solutions. This issue

is further exacerbated by the fact that existing file systems are usually designed to

trade space utilization for performance over block-oriented storage devices. In this work, we propose a multi-grained block management strategy to improve the space

utilization of file systems over PCM-based storage systems. By utilizing the byte-

addressability and fast read/write feature of PCM, a methodology is proposed to dynamically allocate multiple sizes of blocks to fit the size of each file, so as to

resolve the space fragmentation issue with minimized space and management

overheads. The space utilization of file systems is analyzed with consideration of

block sizes. A series of experiments was conducted to evaluate the efficacy of the proposed strategy, and the results show that the proposed strategy can significantly

improve the space utilization of file systems.

S3I_J16_015 - Resource-Saving File Management Scheme for Online Video

Provisioning on Content Delivery Networks

Content delivery networks (CDNs) have been widely implemented to provide scalable cloud services. Such networks support resource pooling by allowing virtual

machines or physical servers to be dynamically activated and deactivated according to

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current user demand. This paper examines online video replication and placement

problems in CDNs. An effective video provisioning scheme must simultaneously (i) utilize system resources to reduce total energy consumption and (ii) limit replication

overhead. We propose a scheme called adaptive data placement (ADP) that can

dynamically place and reorganize video replicas among cache servers on subscribers’ arrival and departure. Both the analyses and simulation results show that ADP can

reduce the number of activated cache servers with limited replication overhead. In

addition, ADP's performance is approximate to the optimal solution.

S3I_J16_016 - Inference Attack on Browsing History of Twitter Users Using

Public Click Analytics and Twitter Metadata

Twitter is a popular online social network service for sharing short messages (tweets) among friends. Its users frequently use URL shortening services that provide

(i) a short alias of a long URL for sharing it via tweets and (ii) public click analytics

of shortened URLs. The public click analytics is provided in an aggregated form to preserve the privacy of individual users. In this paper, we propose practical attack

techniques inferring who clicks which shortened URLs on Twitter using the

combination of public information: Twitter metadata and public click analytics. Unlike the conventional browser history stealing attacks, our attacks only demand

publicly available information provided by Twitter and URL shortening services.

Evaluation results show that our attack can compromise Twitter users' privacy with

high accuracy.

S3I_J16_017 - Clustering Data Streams Based on Shared Density between Micro-

Clusters As more and more applications produce streaming data, clustering data streams

has become an important technique for data and knowledge engineering. A typical

approach is to summarize the data stream in real-time with an online process into a large number of so called micro-clusters. Micro-clusters represent local density

estimates by aggregating the information of many data points in a defined area. On

demand, a (modified) conventional clustering algorithm is used in a second offline

step to re-cluster the micro-clusters into larger final clusters. For re-clustering, the centers of the micro-clusters are used as pseudo points with the density estimates used

as their weights. However, information about density in the area between micro-

clusters is not preserved in the online process and re-clustering is based on possibly inaccurate assumptions about the distribution of data within and between micro-

clusters (e.g., uniform or Gaussian). This paper describes DB_STREAM, the first

micro-cluster-based online clustering component that explicitly captures the density between micro-clusters via a shared density graph. The density information in this

graph is then exploited for re-clustering based on actual density between adjacent

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micro-clusters. We discuss the space and time complexity of maintaining the shared

density graph. Experiments on a wide range of synthetic and real data sets highlight that using shared density improves clustering quality over other popular data stream

clustering methods which require the creation of a larger number of smaller micro-

clusters to achieve comparable results.

IEEE-2016 NS2 Project Titles

S3I_NS2_16_001 : Network Topology Tomography Under Multipath Routing

Network topology tomography can infer a tree topology for single-source networks using end-to-end measurements. However, multipath routing, which introduces

multiple paths between end-hosts, violates the tree topology model. In this letter, we

demonstrate that such nontree topologies are also identifiable. We employ graph cuts and show the nontree topology can be decomposed into two identifiable

subtopologies. And by reconnecting the cut paths, the nontree topology can be

recovered after obtaining these subtopologies. To detect the paths that share cuts, we propose a scheme based on measurements of the end-to-end packet arrival order.

Simulation results show that our scheme achieves the desirable detection accuracy.

S3I_NS2_16_002 : HDEER: A Distributed Routing Scheme for Energy-Efficient Networking

The proliferation of new online Internet services has substantially increased the

energy consumption in wired networks, which has become a critical issue for Internet service providers. In this paper, we target the network-wide energy-saving problem by

leveraging speed scaling as the energy-saving strategy. We propose a distributed

routing scheme–HDEER–to improve network energy efficiency in a distributed manner without significantly compromising traffic delay. HDEER is a two-stage

routing scheme where a simple distributed multipath finding algorithm is firstly

performed to guarantee loop-free routing, and then a distributed routing algorithm is executed for energy-efficient routing in each node among the multiple loop-free paths.

We conduct extensive experiments on the NS3 simulator and simulations with real

network topologies in different scales under different traffic scenarios. Experiment

results show that HDEER can reduce network energy consumption with a fair tradeoff between network energy consumption and traffic delay.

S3I_NS2_16_003 : Mobile Coordinated Wireless Sensor Network: An Energy Efficient Scheme for Real-Time Transmissions

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This paper introduces the mobile access coordinated wireless sensor network (MC-

WSN)—a novel energy efficient scheme for time-sensitive applications. In conventional sensor networks with mobile access points (SENMA), the mobile access

points (MAs) traverse the network to collect information directly from individual

sensors. While simplifying the routing process, a major limitation with SENMA is that data transmission is limited by the physical speed of the MAs and their trajectory

length, resulting in low throughput and large delay. In an effort to resolve this

problem, we introduce the MC-WSN architecture, for which a major feature is that:

through active network deployment and topology design, the number of hops from any sensor to the MA can be limited to a pre-specified number. In this paper, we

investigate the optimal topology design that minimizes the average number of hops

from sensor to MA, and provide the throughput analysis under both single-path and multipath routing cases. Moreover, putting MC-WSN in the bigger picture of network

design and development, we provide a unified framework for wireless network

modeling and characterization. Under this general framework, it can be seen that MC-WSN reflects the integration of structure-ensured reliability/efficiency and ad-hoc

enabled flexibility.

S3I_NS2_16_004 : A secure-efficient data collection algorithm based on self-

adaptive sensing model in mobile Internet of vehicles

Existing research on data collection using wireless mobile vehicle network

emphasizes the reliable delivery of information. However, other performance requirements such as life cycle of nodes, stability and security are not set as primary

design objectives. This makes data collection ability of vehicular nodes in real

application environment inferior. By considering the features of nodes in wireless IoV, such as large scales of deployment, volatility and low time delay, an efficient

data collection algorithm is proposed for mobile vehicle network environment. An

adaptive sensing model is designed to establish vehicular data collection protocol. The protocol adopts group management in model communication. The vehicular sensing

node in group can adjust network sensing chain according to sensing distance

threshold with surrounding nodes. It will dynamically choose a combination of

network sensing chains on basis of remaining energy and location characteristics of surrounding nodes. In addition, secure data collection between sensing nodes is

undertaken as well. The simulation and experiments show that the vehicular node can

realize secure and real-time data collection. Moreover, the proposed algorithm is superior in vehicular network life cycle, power consumption and reliability of data

collection by comparing to other algorithms.

S3I_NS2_16_005 : Resisting blackhole attacks on MANETs

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MANET routing protocols are designed based on the assumption that all nodes

cooperate without maliciously disrupting the operation of the routing protocol. AODV is a reactive MANET routing protocol that is vulnerable to a dramatic collapse of

network performance in the presence of blackhole attack. The paper introduces a new

concept of Self-Protocol Trustiness (SPT) in which detecting a malicious intruder is accomplished by complying with the normal protocol behavior and lures the

malicious node to give an implicit avowal of its malicious behavior. We present a

Blackhole Resisting Mechanism (BRM) to resist such attacks that can be incorporated

into any reactive routing protocol. It does not require expensive cryptography or authentication mechanisms, but relies on locally applied timers and thresholds to

classify nodes as malicious. No modifications to the packet formats are needed, so the

overhead is a small amount of calculation at nodes, and no extra communication. Using NS2 simulation, we compare the performance of networks using AODV under

blackhole attacks with and without our mechanism to SAODV, showing that it

significantly reduces the effect of a blackhole attack.

S3I_NS2_16_006 : Secret Common Randomness From Routing Metadata in Ad

Hoc Networks Establishing secret common randomness between two or multiple devices in a

network resides at the root of communication security. In its most frequent form of

key establishment, the problem is traditionally decomposed into a randomness

generation stage (randomness purity is subject to employing often costly true random number generators) and an information-exchange agreement stage, which relies either

on public-key infrastructure or on symmetric encryption (key wrapping). In this paper,

we propose a secret-common-randomness establishment algorithm for ad hoc networks, which works by harvesting randomness directly from the network routing

metadata, thus achieving both pure randomness generation and (implicitly) secret-key

agreement. Our algorithm relies on the route discovery phase of an ad hoc network employing the dynamic source routing protocol, is lightweight, and requires relatively

little communication overhead. The algorithm is evaluated for various network

parameters in an OPNET ad hoc network simulator. Our results show that, in just 10

min, thousands of secret random bits can be generated network-wide, between different pairs in a network of 50 users.

S3I_NS2_16_007 : Queue Stability Analysis in Network Coded Wireless Multicast Network

This letter considers a single hop wireless multicast network. We first introduce a

new two-level queuing system consisting of a main queue and a virtual queue, where each packet in the virtual queue is associated with a user index set. Then, we propose

a network coding based packet scheduling method to maximize the system input rate

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under the queue stability constraint. Our analytical and simulation results demonstrate

the effectiveness of the proposed solution.

S3I_NS2_16_008 : Delay-Energy Tradeoff in Multicast Scheduling for Green

Cellular Systems Multicast transmission based on real-time network state information is a resource-

friendly technique to improve the energy efficiency and reduce the traffic burden for

cellular systems. This paper evaluates the effectiveness of this technique for downlink

transmissions. In particular, a scenario is considered in which multiple mobile users (MUs) asynchronously request to download one common message locally cached at a

base station (BS). Due to the randomness of both the channel conditions and the

request arrivals from the MUs, the BS may choose to intelligently hold the arrived requests, especially when the channel conditions are bad or the number of requests is

small, and then serve them in one shot later via multicasting. Clearly it is of great

interest to balance the delay (incurred by holding the requests) and the energy efficiency (EE, defined as the energy cost per request), and this motivates us to

quantify the fundamental tradeoff for the proposed “hold-then-serve” scheme. For the

scenario with single channel and unit message sizes, it is shown that for a fixed channel bandwidth, the delay-EE tradeoff reduces to judiciously choosing the optimal

stopping rule for when to serve all the arrived requests, where the effect of the

bandwidth on the achievable delay-EE region is discussed further. By using optimal

stopping theory, it is shown that the optimal stopping rule exists for general Markov channel models and request arrival processes. Particularly, for the hard deadline and

proportional delay penalty cases, it is shown that the optimal stopping rule exhibits a

threshold structure, and the corresponding threshold in the former case is time varying while in the latter case it is a constant. Finally, for the more general scenario with

multiple channels and arbitrary message sizes, the optimal scheduling is formulated as

a Markov decision process problem, where some efficient suboptimal scheduling algorithms are proposed.

S3I_NS2_16_009 : Delay Analysis of Social Group Multicast-Aided Content

Dissemination in Cellular System Based on the common interest of mobile users (MUs) in a social group, the

dissemination of content across the social group is studied as a powerful supplement

to conventional cellular communication with the goal of improving the delay performance of the content dissemination process. The content popularity is modeled

by a Zipf distribution to characterize the MUs' different interests in different contents.

The factor of altruism (FA) terminology is introduced for quantifying the willingness of content owners to share their content. We model the dissemination process of a

specific packet by a pure-birth-based Markov chain and evaluate the statistical

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properties of both the network's dissemination delay as well as of the individual user-

delay. Compared to the conventional base station (BS)-aided multicast, our scheme is capable of reducing the average dissemination delay by about 56.5%. Moreover, in

contrast to the BS-aided multicast, increasing the number of MUs in the target social

group is capable of reducing the average individual user-delay by 44.1% relying on our scheme. Furthermore, our scheme is more suitable for disseminating a popular

piece of content.

S3I_NS2_16_010 : End-to-End coding for TCP Although widely used, TCP has many limitations in meeting the throughput and

latency requirements of applications in wireless networks, high-speed data center

networks, and heterogeneous multi-path networks. Instead of relying purely on retransmission upon packet loss, coding has potential to improve the performance of

TCP by ensuring better transmission reliability. Coding has been verified to work well

at the link layer but has not been fully studied at the transport layer. There are many advantages but also challenges in exploiting coding at the transport layer. In this

article, we focus on how to leverage end-to-end coding in TCP. We reveal the

problems TCP faces and the opportunities coding can bring to improve TCP performance. We further analyze the challenges faced when applying the coding

techniques to TCP and present the current applications of coding in TCP.

S3I_NS2_16_011 : Link Allocation for Multiuser Systems With Hybrid RF/FSO Backhaul: Delay-Limited and Delay-Tolerant Designs

In this paper, we consider a cascaded radio frequency (RF) and hybrid RF/free space

optical (FSO) system where several mobile users transmit their data over an RF link to a decode-and-forward relay node (e.g., a small cell base station) and the relay

forwards the information to a destination (e.g., a macro-cell base station) over a

hybrid RF/FSO backhaul link. The relay and the destination employ multiple antennas for transmission and reception over the RF links while each mobile user has a single

antenna. The RF links are orthogonal to the FSO link but half-duplex with respect to

each other, i.e., either the user-relay RF link or the relay-destination RF link is active.

For this communication setup, we derive the optimal fixed and adaptive link allocation policies for sharing the transmission time between the RF links based on

the statistical and instantaneous channel state information (CSI) of the RF and FSO

links, respectively. Thereby, we consider the following two scenarios depending on the delay requirements: 1) delay-limited transmission where the relay has to

immediately forward the packets received from the users to the destination, and 2)

delay-tolerant transmission where the relay is allowed to store the packets received from the users in its buffer and forward them to the destination when the quality of the

relay-destination RF link is favorable. Our numerical results illustrate the

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effectiveness of the proposed communication architecture and link allocation policies,

and their superiority compared to existing schemes, which employ only one type of backhaul link.

S3I_NS2_16_012 : Fair Routing for Overlapped Cooperative Heterogeneous Wireless Sensor Networks

In recent years, as wireless sensor networks (WSNs) are widely diffused, multiple

overlapping WSNs constructed on the same area become more common. In such a

situation, their lifetime is expected to be extended by cooperative packet forwarding. Although some researchers have studied about cooperation in multiple WSNs, most of

them do not consider the heterogeneity in the characteristics of each WSN such as

battery capacity, operation start time, the number of nodes, nodes locations, energy consumption, packet size and/or data transmission timing, and so on. In a

heterogeneous environment, naive lifetime improvement with cooperation may not be

fair. In this paper, we propose a fair cooperative routing method for heterogeneous overlapped WSNs. It introduces an energy pool to maintain the total amount of energy

consumption by cooperative forwarding. The energy pool plays a role of broker for

fair cooperation. Finally, simulation results show the excellent performance of the proposed method.

S3I_NS2_16_013 : Adaptive and Channel-Aware Detection of Selective

Forwarding Attacks in Wireless Sensor Networks Wireless sensor networks (WSNs) are vulnerable to selective forwarding attacks that

can maliciously drop a subset of forwarding packets to degrade network performance

and jeopardize the information integrity. Meanwhile, due to the unstable wireless channel in WSNs, the packet loss rate during the communication of sensor nodes may

be high and vary from time to time. It poses a great challenge to distinguish the

malicious drop and normal packet loss. In this paper, we propose a channel-aware reputation system with adaptive detection threshold (CRS-A) to detect selective

forwarding attacks in WSNs. The CRS-A evaluates the data forwarding behaviors of

sensor nodes, according to the deviation of the monitored packet loss and the

estimated normal loss. To optimize the detection accuracy of CRS-A, we theoretically derive the optimal threshold for forwarding evaluation, which is adaptive to the time-

varied channel condition and the estimated attack probabilities of compromised nodes.

Furthermore, an attack-tolerant data forwarding scheme is developed to collaborate with CRS-A for stimulating the forwarding cooperation of compromised nodes and

improving the data delivery ratio of the network. Extensive simulation results

demonstrate that CRS-A can accurately detect selective forwarding attacks and identify the compromised sensor nodes, while the attack-tolerant data forwarding

scheme can significantly improve the data delivery ratio of the network.

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S3I_NS2_16_014 : Reverse Update: A Consistent Policy Update Scheme for Software-Defined Networking

Policy and path updates are common causes of network instability, leading to service

disruptions or vulnerable intermediate states. In this letter, we propose the reverse update, an update scheme for software-defined networking that guarantees to preserve

properties of flows during the transition time. We prove through a formal model that

the proposal achieves consistent policy updates, in which in-transit packets are always

handled in the next forwarding hops by the same or a more recent policy. The main contributions are: 1) a relaxation of the concept of per-packet-consistency in the data

plane of software-defined networking; and 2) a policy update scheme, proved to be

consistent and efficient. A software-defined networking simulator was developed and validated. The results of our simulations show that the proposed reverse update

scheme is faster and has lower overhead than the current two-phase update proposed

in the literature.

S3I_NS2_16_015 : On the Throughput-Delay Tradeoff in Georouting Networks

We study the scaling properties of a georouting scheme in a wireless multi-hop network of n mobile nodes. Our aim is to increase the network capacity quasi-linearly

with n , while keeping the average delay bounded. In our model, we consider mobile

nodes moving according to an independent identically distributed random walk with

velocity v and transmitting packets to randomly chosen fixed and known destinations. The average packet delivery delay of our scheme is of order 1/v , and it achieves

network capacity of order ({n}/{\log n\log \log n}) . This shows a practical

throughput-delay tradeoff, in particular when compared with the seminal result of Gupta and Kumar, which shows network capacity of order {(n/\log n)}^{1/2} and

negligible delay and the groundbreaking result of Grossglauser and Tse, which

achieves network capacity of order n but with an average delay of order \sqrt {n}/v . The foundation of our improved capacity and delay tradeoff relies on the fact that we

use a mobility model that contains straight-line segments, a model that we consider

more realistic than classic Brownian motions. We confirm the generality of our

analytical results using simulations under various interference models.

S3I_NS2_16_016 : An Efficient Tree-based Self-Organizing Protocol for Internet of Things

Tree networks are widely applied in Sensor Networks of Internet of Things (IoTs). This paper proposes an Efficient Tree-based Self-organizing Protocol (ETSP) for

sensor networks of IoTs. In ETSP, all nodes are divided into two kinds: network

nodes and non-network nodes. Network nodes can broadcast packets to their

neighboring nodes. Non-network nodes collect the broadcasted packets and determine

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whether to join the network. During the self-organizing process, we use different

metrics such as number of child nodes, hop, communication distance and residual energy to reach available sink nodes’ weight, the node with max weight will be

selected as sink node. Non-network nodes can be turned into network nodes when

they join the network successfully. Then a tree-based network can be obtained one layer by one layer. The topology is adjusted dynamically to balance energy

consumption and prolong network lifetime. We conduct experiments with NS2 to

evaluate ETSP. Simulation results show that our proposed protocol can construct a

reliable tree-based network quickly. With the network scale increasing, the self-organization time, average hop and packet loss ratio won’t increase more.

Furthermore, the success rate of packet in ETSP is much higher compared with

AODV and DSDV.

S3I_NS2_16_017 : Modified AODV Routing Protocol to Improve Security and

Performance against Black Hole Attack A Mobile Ad hoc NETwork (MANET) is a collection of autonomous nodes that have

the ability to communicate with each other without having fixed infrastructure or

centralized access point such as a base station. This kind of networks is very susceptible to adversary's malicious attacks, due to the dynamic changes of the

network topology, trusting the nodes to each other, lack of fixed substructure for the

analysis of nodes behaviors and constrained resources. One of these attacks is black

hole attack. In this attack, malicious nodes inject fault routing information to the network and lead all data packets toward themselves, then destroy them all. In this

paper, we propose a solution, which enhances the security of the Ad-hoc On-demand

Distance Vector (AODV) routing protocol to encounter the black hole attacks. Our solution avoids the black hole and the multiple black hole attacks. The simulation

results using the Network Simulator NS2 shows that our protocol provides better

security and better performance in terms of the packet delivery ratio than the AODV routing protocol in the presence of one or multiple black hole attacks with marginal

rise in average end-to-end delay and normalized routing overhead.

S3I_NS2_16_018 : Resisting Blackhole Attacks on MANETs MANET routing protocols are designed based on the assumption that all nodes

cooperate without maliciously disrupting the operation of the routing protocol. AODV

is a reactive MANET routing protocol that is vulnerable to a dramatic collapse of network performance in the presence of blackhole attack. The paper introduces a new

concept of Self-Protocol Trustiness (SPT) in which detecting a malicious intruder is

accomplished by complying with the normal protocol behavior and lures the malicious node to give an implicit avowal of its malicious behavior. We present a

Blackhole Resisting Mechanism (BRM) to resist such attacks that can be incorporated

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into any reactive routing protocol. It does not require expensive cryptography or

authentication mechanisms, but relies on locally applied timers and thresholds to classify nodes as malicious. No modifications to the packet formats are needed, so the

overhead is a small amount of calculation at nodes, and no extra communication.

Using NS2 simulation, we compare the performance of networks using AODV under blackhole attacks with and without our mechanism to SAODV, showing that it

significantly reduces the effect of a blackhole attack.

S3I_NS2_16_019 : Constructing A Shortest Path Overhearing Tree With Maximum Lifetime In WSNs

Secure data collection is an important problem in wireless sensor networks. Different

approaches have been proposed. One of them is overhearing. We investigate the problem of constructing a shortest path overhearing tree with the maximum lifetime.

We propose three approaches. The first one is a polynomial-time heuristic. The

second one uses ILP (Integer Linear Programming) to iteratively find a monitoring node and a parent for each sensor node. The last one optimally solves the problem by

using MINLP (Mixed-Integer Non-Linear Programming). We have implemented the

three approaches using MIDACO solver and MATLAB Intlinprog, and performed extensive simulations using NS2.35. The simulation results show that the average

lifetime of all the network instances achieved by the heuristic approach is 85.69% of

that achieved by the ILP-based approach and 81.05% of that obtained by the MINLP-

based approach, and the performance of the ILP-based approach is almost equivalent to that of the MINLP-based approach.

S3I_NS2_16_020 : Energy-Efficient Adaptive Forwarding Scheme for MANETs Flooding is the simplest way of broadcasting, in which each node in the network

retransmits an incoming message once. Simple flooding technique in wireless Ad-hoc

networks causes the broadcast storm problem. However, this technique is inefficient in terms of resource consumption such as bandwidth and energy. This paper presents a

new hybrid scheme that combines different techniques that collaborate to reduce

overhead and conserve energy. We propose an Energy-Efficient Adaptive Forwarding

Scheme, that utilizes the information of the 1-hop neighbouring radios. In this scheme nodes do not need a positioning system or distance calculation to determine their

location. In addition to the previous works, the proposed protocol divides the network

into different groups based on their transmission-power levels. Therefore, the node which receives HELLO message from different groups is considered a Gateway node.

This node efficiently participates in forwarding RREQ packets and the unnecessary

redundant retransmission is avoided. The performance evaluation of the proposed protocol shows a reduction in the routing overhead and in energy consumption, when

compared with the Pure-Flooding AODV and Dynamic-Power AODV using NS2.

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S3I_NS2_16_021 : Trusted Secure Adhoc On-Demand Multipath Distance Vector Routing in MANET

A mobile ad hoc network (MANET) is a collectionof wireless nodes, which works

well only if those mobile nodes aregood and behave cooperatively. The lack of infrastructuresupport and resource constraint is the key issue that causesdishonest and

non-co-operative nodes. Therefore, MANET isvulnerable to serious attacks. To

reduce the hazards from suchnodes and enhance the security of the network, this

paperextends an Ad hoc On-Demand Multipath Distance Vector(AOMDV) Routing protocol, named as Trust-based Secured Adhoc On-demand Multipath Distance

Vector (TS-AOMDV), whichis based on the nodes' routing behavior. The proposed

TSAOMDVaims at identifying and isolating the attacks such asflooding, black hole, and gray hole attacks in MANET. With thehelp of Intrusion Detection System (IDS)

and trust-based routing, attack identification and isolation are carried out in two

phases ofrouting such as route discovery and data forwarding phase. IDSfacilitates complete routing security by observing both controlpackets and data packets that are

involved in the routeidentification and the data forwarding phases. To improve

therouting performance, the IDS integrates the measured statisticsinto the AOMDV routing protocol for the detection of attackers. This facilitates the TS-AOMDV to

provide better routingperformance and security in MANET. Finally, the Trust

basedSecured AOMDV, TS-AOMDV is compared with the existingAOMDV through

the NS2 based simulation model. Theperformance evaluation reveals that the proposed TS-AOMDVimproves the performance in terms of throughput by

57.1%more than that of an AOMDV under adversary scenario. Thesimulated results

show that the TS-AOMDV outperforms theAOMDV routing protocol.

S3I_NS2_16_022 : QoS and Security in VOIP Networks through Admission

Control Mechanism With the developing understanding of Information Security and digital assets, IT

technology has put on tremendous importance of network admission control (NAC).

In NAC architecture, admission decisions and resource reservations are taken at edge

devices, rather than resources or individual routers within the network. The NAC architecture enables resilient resource reservation, maintaining reservations even after

failures and intra-domain rerouting. Admission Control Networks destiny is based on

IP networks through its Security and Quality of Service (QoS) demands for real time multimedia application via advance resource reservation techniques. To achieve

Security & QoS demands, in real time performance networks, admission control

algorithm decides whether the new traffic flow can be admitted to the network or not. Secure allocation of Peer for multimedia traffic flows with required performance is a

great challenge in resource reservation schemes. In this paper, we have proposed our

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model for VoIP networks in order to achieve security services along with QoS, where

admission control decisions are taken place at edge routers. We have analyzed and argued that the measurement based admission control should be done at edge routers

which employs on-demand probing parallel from both edge routers to secure the

source and destination nodes respectively. In order to achieve Security and QoS for a new call, we choose various probe packet sizes for voice and video calls respectively.

Similarly a technique is adopted to attain a security allocation approach for selecting

an admission control threshold by proposing our admission control algorithm. All

results are tested on NS2 based simulation to evalualate the network performance of edge router based upon network admission control in VoIP traffic.

S3I_NS2_16_023 : An Energy Consumption Evaluation of Reactive and Proactive Routing Protocols in Mobile Ad-hoc Network

In Mobile Ad-hoc NETwork (MANET) each node has the possibility to move freely

in the space and communicate with each other over wireless link without any centralized controller or base station. These characteristics makes MANET useful and

practical in several fields like military scenarios, sensor networks, Rescue operations,

students on campus, etc. but this kind of network still suffers from a number of problems, power consumption is one of the most crucial design concerns in Mobile

Ad-hoc networks as the nodes in MANET have battery limited. In this paper, we will

discuss about the aspect of energy consumption in MANET's routing protocols. A

performance comparison of four routing protocols Dynamic Source Routing (DSR), Ad hoc On-Demand Distance Vector (AODV), Destination-Sequenced Distance

Vector (DSDV) and Optimized Link State Routing (OLSR) with respect to average

energy consumption are explained thoroughly. Then, an evaluation of how the varying parameters of network in diverse scenarios affect the power consumption in these four

protocols is discussed. A detailed simulation model using Network Simulator 2 (NS2)

with different mobility and traffic models is used to study their energy consumption.

S3I_NS2_16_024 : Energy Detection Analytical Model for Handoff Process to

Support Mobile Cloud Computing Environment

Mobile devices play an integral role in our day lives and have brought the revolutionary change in business, education, and entertainment. Moreover, the

emergence of cloud computing technology greatly extended the significance of smart

device. On the other hand, the smart devices experience the problem when obtaining the multiple cloud services during the handoff process. In this paper, we propose an

energy detection (ED) analytical model for handoff process that calculates the energy

consumption for each handoff process in the cloud computing environment. Our ED analytic model is developed to examine the consumed energy for different handoff

processes in cloud computing. The model helps the mobile users to get prior

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information for the status of the mobile when executing the handoff process. To

reconfirm the validity of ED analytical model, we have test programmed in NS2. The results demonstrate that the ED analytical model efficiently detects the energy

consumption of mobile devices during the handoff process in cloud computing

environment.

S3I_NS2_16_025 : Nonsmooth Nonconvex Optimization for Low-Frequency

Geosounding Inversion

A study of the application of nonconvex regularization operators to the electromagnetic sounding inverse problem is presented. A comparison is presented

among three nonconvex regularization algorithms: one smooth usually considered,

two nonsmooth, and a convex one, the total variation (TV) operator. One of the nonsmooth nonconvex regularization methods is a novel implementation based on the

Legendre–Fenchel transform and the Bregman iterative algorithm. The nonconvex

regularization operator is approximated by the convex dual, and the minimization is then implemented considering the equivalence between the Bregman iteration and the

augmented Lagrangian methods. The algorithm is simple and provides for better

models when applied to synthetic data, than those obtained with TV, and other nonconvex smooth regularizers. Results of the application to field data are also

presented, observing that NS2 recovers a model in better agreement with the truth,

compared to those obtained with additional magnetometric resistivity data by other

researchers.

S3I_NS2_16_026 : Hierarchical Location-Based Services for Wireless Sensor

Networks Nowadays Wireless Sensor Networks have attracted worldwide research and

industrial interest, because they can be applied in various areas. Geographic routing

protocols are very suitable to wireless sensor networks because they use location information when they need to route packets. Obviously, location information is

maintained by Location-Based Services provided by network nodes in a distributed

way. The location based services can be classified into two classes: Flooding-Based

and Rendezvous-based location services.In this paper we choose to compare two hierarchical rendezvous location based-services, GLS (Grid Location Service) and

HLS (Hierarchical Location Service) coupled to the GPSR routing protocol (Greedy

Perimeter Stateless Routing).The simulations were performed using NS2 simulator for wireless sensor networks to evaluate the performance and power of the two services in

term of location overhead, the request travel time (RTT) and the query Success ratio

(QSR).This work presents also the scalability performance study of both GLS and HLS, specifically, what happens if the number of nodes N increases. The study will

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focus on three qualitative metrics: The location maintenance cost,the location query

cost and the storage cost.

S3I_NS2_16_027 : Modified AODV Routing Protocol to Improve Security and

Performance against Black Hole Attack A Mobile Ad hoc NETwork (MANET) is a collection of autonomous nodes that have

the ability to communicate with each other without having fixed infrastructure or

centralized access point such as a base station. This kind of networks is very

susceptible to adversary's malicious attacks, due to the dynamic changes of the network topology, trusting the nodes to each other, lack of fixed substructure for the

analysis of nodes behaviors and constrained resources. One of these attacks is black

hole attack. In this attack, malicious nodes inject fault routing information to the network and lead all data packets toward themselves, then destroy them all. In this

paper, we propose a solution, which enhances the security of the Ad-hoc On-demand

Distance Vector (AODV) routing protocol to encounter the black hole attacks. Our solution avoids the black hole and the multiple black hole attacks. The simulation

results using the Network Simulator NS2 shows that our protocol provides better

security and better performance in terms of the packet delivery ratio than the AODV routing protocol in the presence of one or multiple black hole attacks with marginal

rise in average end-to-end delay and normalized routing overhead.

S3I_NS2_16_028 : Attacks against AODV Routing Protocol in Mobile Ad-HocNetworks

A Mobile Ad hoc NETwork (MANET) is much more vulnerable to attack than a

wired network due to the dynamic changes of the network topology, high mobility, limited physical security and lack of centralized administration. Unfortunately, the

routing protocols are designed based on the assumption that all nodes trust each other

and cooperate without maliciously disrupting the operation of routing. This paper analyzes the impact of security attacks on the performance of the AODV routing

protocol. Simulations are setup in the NS-2 network simulator and the performance of

the AODV routing protocol is discussed under black hole, flooding and rushing

attacks. This analysis is provided in terms of performance metrics, such as a packet delivery ratio, the average end-to-end delay and normalized routing load.

S3I_NS2_16_029 : Novel Scheme to Heal MANET in Smart City Network Today's generation has perceived wireless networking prospective applications in

tremendously erratic and vibrant surroundings. Businesses as well as individuals pick

wireless medium as their choice as it facilitates flexibility of location. It's obvious due to its convenience in terms of mobility, portability or even ease of installation at any

preferred location. Mobile network has an intrinsic scalability restraint in terms of

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attainable network capability. The potential challenge of wireless communication in

Smart cities network is, the environs that these communications travel through is changeable. So, the wireless networks which have ability to resolve their own

fragmented communication links will definitely enhance their pervasive recognition.

Due to the expansion of network capability changes are made to the network design and infrastructures giving way to new techniques for system development for this type

of medium. Since it's the beginning there are initial hiccups, despite that, the modern

advances in self-healing wireless networks are good enough in resolving the problem.

The downlinks are repaired by using power conscious steady nodes. Authors have proposed, a self-healing structure and mobile Power aware stable nodes for smart city

networks. Proposed design has been checked using NS2 simulator with existing

schemes and show good results in most of the cases.

S3I_NS2_16_030 : Flow Allocation for Maximum Throughput and Bounded

Delay on Multiple Disjoint Paths for Random Access Wireless Multihop Networks

In this paper, we consider random access, wireless, multi-hop networks, with multi-

packet reception capabilities, where multiple flows are forwarded to the gateways through node disjoint paths. We explore the issue of allocating flow on multiple paths,

exhibiting both intra- and inter-path interference, in order to maximize average

aggregate flow throughput (AAT) and also provide bounded packet delay. A

distributed flow allocation scheme is proposed where allocation of flow on paths is formulated as an optimization problem. Through an illustrative topology it is shown

that the corresponding problem is non-convex. Furthermore, a simple, but accurate

model is employed for the average aggregate throughput achieved by all flows, that captures both intra- and inter-path interference through the SINR model. The

proposed scheme is evaluated through Ns2 simulations of several random wireless

scenarios. Simulation results reveal that, the model employed, accurately captures the AAT observed in the simulated scenarios, even when the assumption of saturated

queues is removed. Simulation results also show that the proposed scheme achieves

significantly higher AAT, for the vast majority of the wireless scenarios explored,

than the following flow allocation schemes: one that assigns flows on paths on a round-robin fashion, one that optimally utilizes the best path only, and another one

that assigns the maximum possible flow on each path. Finally, a variant of the

proposed scheme is explored, where interference for each link is approximated by considering its dominant interfering nodes only.

S3I_NS2_16_031 : Analysis and Comparison of EEEMR Protocol with the Flat Routing Protocols of Wireless Sensor Networks

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Wireless Sensor Communication Networks have more concern on its routing

techniques. Since the WSCNs nodes are battery powered, routing algorithms should assure the concept of energy saving without affecting the other performance metric

like Throughput, Delay and Packet delivery ratio etc. The modified AOMDV called as

Enhanced Energy Efficient Multipath Routing Protocol (EEEMRP) is a proposed algorithm with the concept of crossbreed the AOMDV routing with the Cuckoo

Search algorithm. In this paper, we compare the QoS parameters such as Throughput,

Average Delay and Packet Delivery ratio with Traditional Flat Routing Protocols such

as DSR, DSDV and with AODV. Simulation is performed using NS2 and the results show that the proposed EEEMRP routing protocol is better than DSR, DSDV and

AODV.

S3I_NS2_16_032 : P-LEACH: Energy Efficient Routing Protocol for Wireless

Sensor Networks

Wireless Sensor Network (WSN) are of paramount significance since they are responsible for maintaining the routes in the network, data forwarding, and ensuring

reliable multi-hop communication. The main requirement of a wireless sensor

network is to prolong network energy efficiency and lifetime. Researchers have developed protocols Low Energy Adaptive Clustering Hierarchy (LEACH) and

Power-Efficient Gathering in Sensor Information Systems (PEGASIS) for reducing

energy consumption in the network. However, the existing routing protocols

experience many shortcomings with respect to energy and power consumption. LEACH features the dynamicity but has limitations due to its cluster-based

architecture, while PEGASIS overcomes the limitations of LEACH but lacks

dynamicity. In this paper, we introduce PEGASIS-LEACH (P-LEACH), a near optimal cluster-based chain protocol that is an improvement over PEGASIS and

LEACH both. This protocol uses an energy-efficient routing algorithm to transfer the

data in WSN. To validate the energy effectiveness of P-LEACH, we simulate the performance using Network Simulator (NS2) and MATLAB.

S3I_NS2_16_033 : A Novel Framework to Enhance the Performance of

Contention Based Synchronous MAC Protocol In this paper, We propose a novel framework to improve the end-to-end transmission

delay (E2ETD) and packet delivery ratio (PDR) of existing contention based

synchronous MAC protocols designed for wireless sensor networks, without increasing the duty cycle (DC). This is achieved by partitioning the n deployed sensor

nodes into k disjoint sets (DSs) which are of almost equal size. It then suitably

modifies the cycle structure followed by the existing contention based synchronous MAC protocols by mapping the data transmission process of k existing cycles into

one restructured cycle. To evaluate the performance of this approach, we implement

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RMAC, PRMAC, and CLMAC protocols in the proposed framework using ns2.35

simulator. Results indicate that our scalable framework reduces the E2ETD and increases the PDR significantly at the cost of a very small increase in average energy

consumption.

S3I_NS2_16_034 : Efficient Wireless Multimedia Multicast in Multi-rate Multi-

channel Mesh Networks

Devices in wireless mesh networks can operate on multiple channels and

automatically adjust their transmission rates for the occupied channels. This paper shows how to improve performance-guaranteed multicasting transmission coverage

for wireless multi-hop mesh networks by exploring the transmission opportunity

offered by multiple rates (MR) and multiple channels (MC). Based on the characteristics of transmissions with different rates, we propose and analyze parallel

low-rate transmissions (PLT) and alternative rate transmissions (ART) to explore the

advantages of MRMC in improving the performance and coverage tradeoff under the constraint of limited channel resources. We then apply these new transmission

schemes to improving the WMN multicast experience. Combined with the strategy of

reliable interference-controlled connections, a novel MRMC multicast algorithm (LC-MRMC) is designed to make efficient use of channel and rate resources to greatly

extend wireless multicast coverage with high throughput and short delay performance.

Our NS2 simulation results prove that ART and LC-MRMC achieve improved

wireless transmission quality across much larger areas as compared to other related studies.

S3I_NS2_16_035 - Optimization of Key Predistribution Protocol Based on Supernetworks Theory in Heterogeneous WSN

This work develops an equilibrium model for finding the optimal distribution

strategy to maximize performance of key predistribution protocols in terms of cost,

resilience, connectivity, and lifetime. As an essential attribute of wireless sensor

networks, heterogeneity and its impacts on random key predistribution protocols are first discussed. Using supernetworks theory, the optimal node deployment model is

proposed and illustrated. In order to find the equilibrium performance of our model,

all optimal performance functions are changed into variational inequalities so that this optimization problem can be solved. A small-scale example is presented to illustrate

the applicability of our model.

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S3I_NS2_16_036 - Low-Cost Localization for Multihop Heterogeneous Wireless

Sensor Networks

In this paper, we propose a novel low-cost localization algorithm tailored for

multihop heterogeneous wireless sensor networks (HWSNs) where nodes' transmission capabilities are different. This characteristic, if not taken into account

when designing the localization algorithm, may severely hinder its accuracy.

Assuming different nodes' transmission capabilities, we develop two different approaches to derive the expected hop progress (EHP). Exploiting the latter, we

propose a localization algorithm that is able to accurately locate the sensor nodes

owing to a new low-cost implementation. Furthermore, we develop a correction

mechanism, which complies with the heterogeneous nature of wireless sensor networks (WSNs) to further improve localization accuracy without incurring any

additional costs. Simulations results show that the proposed algorithm, whether

applied with or without correction, outperforms in accuracy the most representative WSN localization algorithms.

S3I_NS2_16_037 - Auction-Based Data Gathering Scheme for Wireless Sensor Networks

This letter proposes a novel data gathering scheme for wireless sensor networks (WSNs) that limits the energy expenditure, and hence, prolongs network lifetime.

Data gathering is modeled as an auction where a node broadcasts its own result only if

it is higher than the maximum already-broadcasted result by other nodes. For a WSN

of 100 nodes, the mathematical and simulation results show that the proposed scheme can save up to 70% of the energy consumption with <;1% performance loss,

compared with the conventional scheme.

S3I_NS2_16_038 - Cost-Aware Activity Scheduling for Compressive Sleeping

Wireless Sensor Networks

In this paper, we consider a compressive sleeping wireless sensor network

(WSN) for monitoring parameters in the sensor field, where only a fraction of sensor

nodes (SNs) are activated to perform the sensing task and their data are gathered at a

fusion center (FC) to estimate all the other SNs' data using the compressive sensing (CS) principle. Typically, research published concerning CS implicitly assume the

sampling costs for all samples are equal and suggest random sampling as an

appropriate approach to achieve good reconstruction accuracy. However, this assumption does not hold for compressive sleeping WSNs, which have significant

variability in sampling cost owing to the different physical conditions at particular

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SNs. To exploit this sampling cost nonuniformity, we propose a cost-aware activity

scheduling approach that minimizes the sampling cost with constraints on the regularized mutual coherence of the equivalent sensing matrix. In addition, for the

case with prior information about the signal support, we extend the proposed approach

to incorporate the prior information by considering an additional constraint on the mean square error (MSE) of the oracle estimator for sparse recovery. Our numerical

experiments demonstrate that, in comparison with other designs in the literature, the

proposed activity scheduling approaches lead to improved tradeoffs between

reconstruction accuracy and sampling cost for compressive sleeping WSNs.

S3I_NS2_16_039 - Wireless Sensor Network Simulation Frameworks: A Tutorial

Review: MATLAB/Simulink bests the rest

A Wireless Sensor Network (WSN) is a distributed set of sensors deployed to

work together for collective sensing and possible data processing. A WSN can be used

to monitor environmental behavior and structural integrity in a variety of application fields, thus becoming an integral part of the consumer electronics of smart buildings

in smart cities. Due to ever-increasing population growth, along with limited natural

resources, smart cities are expected to be the wave of the future. For instance, WSNs are widely used in industrial settings with machine monitoring and play an important

role in monitoring the structural integrity of large buildings and bridges. This article

focuses on existing WSN simulation frameworks that could be integrated with

realtime hardware prototypes. We analyze and compare various such simulation frameworks, and we determine a suitable simulation environment that supports

specific software packages.

S3I_NS2_16_040 - The Impact of Incomplete Secure Connectivity on the

Lifetime of Wireless Sensor Networks

Key predistribution schemes accommodate secure connectivity by establishing

pairwise keys between nodes. However, ensuring security for all communication links

of a wireless sensor network (WSN) is nontrivial due to the memory limitations of the

nodes. If some of the links are not available due to the lack of a primary security association between the transmitter and the receiver, nodes can still send their data to

the base station but probably not via the best route that maximizes the network

lifetime. In this study, we propose a linear programming framework to explore the incomplete secure connectivity problem with respect to its impact on network

lifetime, path length, queue size, and energy dissipation. The numerical results show

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that if any two nodes share a key with a probability of at least 0.3, then we should

expect only a marginal drop (i.e., less than 3.0%) in lifetime as compared to a fully connected network.

S3I_NS2_16_041 - Energy-Efficient Cooperative Relaying for Unmanned Aerial Vehicles

Airborne relaying can extend wireless sensor networks (WSNs) to remote human-unfriendly terrains. However, lossy airborne channels and limited battery of

unmanned aerial vehicles (UAVs) are critical issues, adversely affecting success rate

and network lifetime, especially in real-time applications. We propose an energy-

efficient cooperative relaying scheme which extends network lifetime while guaranteeing the success rate. The optimal transmission schedule of the UAVs is

formulated to minimize the maximum (min-max) energy consumption under

guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimisation techniques. We also propose a computationally efficient

suboptimal algorithm to reduce the scheduling complexity, where energy balancing

and rate adaptation are decoupled and carried out in a recursive alternating manner. Simulation results confirm that the suboptimal algorithm cuts off the complexity by

orders of magnitude with marginal loss of the optimal network yield (throughput) and

lifetime. The proposed suboptimal algorithm can also save energy by 50 percent,

increase network yield by 15 percent, and extend network lifetime by 33 percent, compared to the prior art.

S3I_NS2_16_042 - A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime

Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources

that are being used for huge range of applications where the traditional infrastructure-

based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume

considerable amount of energy while transmitting the information. It is impractical

and sometimes impossible to replace the battery and to maintain longer network life

time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one

such issue, which can reduce the energy consumption dramatically. Low energy

adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold

value and only CHs are allowed to send the information to the base station (BS). But

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in this approach, a super-CH (SCH) is elected among the CHs who can only send the

information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy

inference engine (Mamdani's rule) is used to elect the chance to be the SCH. The

results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node

alive, better stability, and better lifetime.

S3I_NS2_16_043 - Energy profiling in practical sensor networks: Identifying

hidden consumers

Reducing energy consumption of wireless sensor nodes extends battery life and

/ or enables the use of energy harvesting and thus makes feasible many applications that might otherwise be impossible, too costly or require constant maintenance.

However, theoretical approaches proposed to date that minimise WSN energy needs

generally lead to less than expected savings in practice. We examine experiences of tuning the energy profile for two near-production wireless sensor systems and

demonstrate the need for (a) microbenchmark-based energy consumption profiling,

(b) examining start-up costs, and (c) monitoring the nodes during long-term deployments. The tuning exercise resulted in reductions in energy consumption of a)

93% for a multihop Telos-based system (average power 0.029 mW) b) 94.7% for a

single hop Ti- 8051-based system during startup, and c) 39% for a Ti- 8051 system

post start-up. The work reported shows that reducing the energy consumption of a node requires a whole system view, not just measurement of a “typical” sensing cycle.

We give both generic lessons and specific application examples that provide guidance

for practical WSN design and deployment.

S3I_NS2_16_044 - Intercept Behavior Analysis of Industrial Wireless Sensor

Networks in the Presence of Eavesdropping Attack

This paper studies the intercept behavior of an industrial wireless sensor

network (WSN) consisting of a sink node and multiple sensors in the presence of an

eavesdropping attacker, where the sensors transmit their sensed information to the sink node through wireless links. Due to the broadcast nature of radio wave

propagation, the wireless transmission from the sensors to the sink can be readily

overheard by the eavesdropper for interception purposes. In an information-theoretic sense, the secrecy capacity of the wireless transmission is the difference between the

channel capacity of the main link (from sensor to sink) and that of the wiretap link

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(from sensor to eavesdropper). If the secrecy capacity becomes nonpositive due to the

wireless fading effect, the sensor's data transmission could be successfully intercepted by the eavesdropper and an intercept event occurs in this case. However, in industrial

environments, the presence of machinery obstacles, metallic frictions, and engine

vibrations makes the wireless fading fluctuate drastically, resulting in the degradation of the secrecy capacity. As a consequence, an optimal sensor scheduling scheme is

proposed in this paper to protect the legitimate wireless transmission against the

eavesdropping attack, where a sensor with the highest secrecy capacity is scheduled to

transmit its sensed information to the sink. Closed-form expressions of the probability of occurrence of an intercept event (called intercept probability) are derived for the

conventional round-robin scheduling and the proposed optimal scheduling schemes.

Also, an asymptotic intercept probability analysis is conducted to provide an insight into the impact of the sensor scheduling on the wireless security. Numerical results

demonstrate that the proposed sensor scheduling scheme outperforms the

conventional round-robin scheduling in terms of the intercept probability.

S3I_NS2_16_045 - Distributed k-Means Algorithm and Fuzzy c-Means

Algorithm for Sensor Networks Based on Multiagent

This paper is concerned with developing a distributed k-means algorithm and a

distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each

node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is

utilized to exchange the measurement information of the sensors in WSN. To obtain a

faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before

executing the distributed k-means algorithm and the distributed fuzzy c-means

algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group

and large out-group distances, while the proposed distributed fuzzy c-means algorithm

is capable of partitioning the data observed by the nodes into different measure-

dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same

results as that given by the centralized clustering algorithms.

S3I_NS2_16_046 - Game-Theoretic Multi-Channel Multi-Access in Energy

Harvesting Wireless Sensor Networks

Energy harvesting (EH) has been proposed as a promising technology to extend

the lifetime of wireless sensor networks (WSNs) by continuously harvesting

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green/renewable energy. However, the intermittent and random EH process as well as

the complexity in achieving global network information call for efficient energy management and distributed resource optimization. Considering the complex

interactions among individual sensors, we use the game theory to perform distributed

optimization for the general multi-channel multi-access problem in an EH-WSN, where strict delay constraints are imposed for the data transmission. Sensors'

competition for channel access is formulated as a non-cooperative game, which is

proved to be an ordinal potential game that has at least one Nash equilibrium (NE).

Furthermore, all the NE of the game is proved to be Pareto optimal, and Jain's fairness index bound of the NE is theoretically derived. Finally, we design a fully distributed,

online learning algorithm for the multi-channel multi-access in the EH-WSN, which is

proved to converge to the NE of the formulated game. Simulation results validate the effectiveness of the proposed algorithm.

S3I_NS2_16_047 - Non-Parametric and Semi-Parametric RSSI/Distance Modeling for Target Tracking in Wireless Sensor Networks

This paper introduces two main contributions to the wireless sensor network (WSN) society. The first one consists of modeling the relationship between the

distances separating sensors and the received signal strength indicators (RSSIs)

exchanged by these sensors in an indoor WSN. In this context, two models are

determined using a radio-fingerprints database and kernel-based learning methods. The first one is a non-parametric regression model, while the second one is a semi-

parametric regression model that combines the well-known log-distance theoretical

propagation model with a non-linear fluctuation term. As for the second contribution, it consists of tracking a moving target in the network using the estimated

RSSI/distance models. The target's position is estimated by combining acceleration

information and the estimated distances separating the target from sensors having known positions, using either the Kalman filter or the particle filter. A fully

comprehensive study of the choice of parameters of the proposed distance models and

their performances is provided, as well as a study of the performance of the two

proposed tracking methods. Comparisons with recently proposed methods are also provided.

S3I_NS2_16_048 - SRA: A Sensing Radius Adaptation Mechanism for Maximizing Network Lifetime in WSNs

Coverage is an important issue that has been widely discussed in wireless sensor networks (WSNs). However, it is still a big challenge to achieve both purposes

of full coverage and energy balance. This paper considers the area coverage problem

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for a WSN where each sensor has variable sensing radius. To prolong the network

lifetime, a weighted Voronoi diagram (WVD) is proposed as a tool for determining the responsible sensing region of each sensor according to the remaining energy in a

distributed manner. The proposed mechanism, called SRA, mainly consists of three

phases. In the first phase, each sensor and its neighboring nodes cooperatively construct the WVD for identifying the responsible monitoring area. In the second

phase, each sensor adjusts its sensing radius to reduce the overlapping sensing region

such that the purpose of energy conservation can be achieved. In the last phase, the

sensor with the least remaining energy further adjusts its sensing radius with its neighbor for maximizing the network lifetime. Performance evaluation and analysis

reveal that the proposed SRA mechanism outperforms the existing studies in terms of

the network lifetime and the degree of energy balance.

S3I_NS2_16_049 - Neighbor-Aided Spatial-Temporal Compressive Data

Gathering in Wireless Sensor Networks

The integration between data collection methods in wireless sensor networks

(WSNs) and compressive sensing (CS) provides energy efficient paradigms. Single-dimensional CS approaches are inapplicable in spatial and temporal correlated WSNs

while the Kronecker compressive sensing (KCS) model suffers performance

degradation along with the increasing data dimensions. In this letter, a neighbor-aided

compressive sensing (NACS) scheme is proposed for efficient data gathering in spatial and temporal correlated WSNs. During every sensing period, the sensor node

just sends the raw readings within the sensing period to a randomly and uniquely

selected neighbor. Then, the CS measurements created by the neighbor are sent to the sink node directly. The equivalent sensing matrix is proved to satisfy both structured

random matrix (SRM) and generalized KCS models. And, by introducing the idea of

SRM to KCS, the recovery performance of KCS is significantly improved. Simulation results demonstrate that compared with the conventional KCS models, the proposed

NACS model can achieve vastly superior recovery performance and receptions with

much fewer transmissions.

S3I_NS2_16_050 - Charge Redistribution-Aware Power Management for

Supercapacitor-Operated Wireless Sensor Networks

Supercapacitors (SCs) have been used in energy harvesting wireless sensor

networks (WSNs) to relieve the life cycle limitations that most traditional

rechargeable storage devices suffer from. SC-operated WSNs present new challenges for power management due to its high self-discharge and charge redistribution. Power

management algorithms have been developed to reduce self-discharge loss, but few

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studies have focused on charge redistribution loss in SC-operated WSNs. In this

paper, we investigate how SC charge redistribution affects power management in long-term WSN applications, and develop a practical power manager to reduce

redistribution loss by scheduling the workload in a way that maintains a relatively

balanced voltage between the main branch and the delayed branch of an SC. The manager has low computational complexity and yields considerably smaller charge

redistribution loss than other power managers.

S3I_NS2_16_051 - Distributed Sequential Location Estimation of a Gas Source

via Convex Combination in WSNs

Localization of the hazardous gas source plays an important role in the protection of public security, since it can save a lot of time for subsequent rescue

works. For gas source localization (GSL), a large number of gas sensor nodes can be

rapidly deployed to construct a wireless sensor network (WSN) and cover the whole concerned area. Although least-squares (LS) methods can solve the problem of GSL

in WSNs regardless of the distribution of measurement noises, centralized LS

methods are not power efficient and robust since they require the gathering and processing of large-scale measurements on a central node. In this paper, we propose a

novel distributed method for GSL in WSNs, which is performed on a sequence of

sensor nodes successively. Each sensor node in the sequence conducts an individual

estimation and a convex combination. The individual estimation is inspired by the LS formulation of the problem of GSL in WSNs. The proposed method is fully

distributed and computationally efficient, and it does not rely on the absolute location

of the sensor nodes. Extensive simulation results and a set of experimental results demonstrate that the success rate and localization accuracy of the proposed method

are generally higher than those of the trust-region-reflective method.

S3I_NS2_16_052 - Self-Sustainable Communications With RF Energy

Harvesting: Ginibre Point Process Modeling and Analysis

RF-enabled wireless power transfer and energy harvesting has recently

emerged as a promising technique to provision perpetual energy replenishment for

low-power wireless networks. The network devices are replenished by the RF energy

harvested from the transmission of ambient RF transmitters, which offers a practical and promising solution to enable self-sustainable communications. This paper adopts

a stochastic geometry framework based on the Ginibre model to analyze the

performance of self-sustainable communications over cellular networks with general fading channels. Specifically, we consider the point-to-point downlink transmission

between an access point and a battery-free device in the cellular networks, where the

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ambient RF transmitters are randomly distributed following a repulsive point process,

called Ginibre α-determinantal point process (DPP). Two practical RF energy harvesting receiver architectures, namely time-switching and power-splitting, are

investigated. We perform an analytical study on the RF-powered device and derive

the expectation of the RF energy harvesting rate, the energy outage probability and the transmission outage probability over Nakagami-m fading channels. These are

expressed in terms of so-called Fredholm determinants, which we compute efficiently

with modern techniques from numerical analysis. Our analytical results are

corroborated by the numerical simulations, and the efficiency of our approximations is demonstrated. In practice, the accurate simulation of any of the Fredholm determinant

appearing in the manuscript is a matter of seconds. An interesting finding is that a

smaller value of α (corresponding to larger repulsion) yields a better transmission outage performance when the density of the ambient RF transmitters is small.

However, it yields a lower transmission outage probability when the density of the

ambient RF transmitters is large. We also show analytically that the power-splitting architecture outperfor- s the time-switching architecture in terms of transmission

outage performances. Lastly, our analysis provides guidelines for setting the time-

switching and power-splitting coefficients at their optimal values.

S3I_NS2_16_053 - Allocation of Heterogeneous Resources of an IoT Device to

Flexible Services

Internet of Things (IoT) devices can be equipped with multiple heterogeneous

network interfaces. An overwhelmingly large amount of services may demand some

or all of these interfaces’ available resources. Herein, we present a precise mathematical formulation of assigning services to interfaces with heterogeneous

resources in one or more rounds. For reasonable instance sizes, the presented

formulation produces optimal solutions for this computationally hard problem. We prove the NP-Completeness of the problem and develop two algorithms to

approximate the optimal solution for big instance sizes. The first algorithm allocates

the most demanding service requirements first, considering the average cost of

interfaces resources. The second one calculates the demanding resource shares and allocates the most demanding of them first by choosing randomly among equally

demanding shares. Finally, we provide simulation results giving insight into services

splitting over different interfaces for both cases.

S3I_NS2_16_054 - Mobile Demand Profiling for Cellular Cognitive Networking

In the next few years, mobile networks will undergo significant evolutions in

order to accommodate the ever-growing load generated by increasingly pervasive

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smartphones and connected objects. Among those evolutions, cognitive networking

upholds a more dynamic management of network resources that adapts to the significant spatiotemporal fluctuations of the mobile demand. Cognitive networking

techniques root in the capability of mining large amounts of mobile traffic data

collected in the network, so as to understand the current resource utilization in an automated manner. In this paper, we take a first step towards cellular cognitive

networks by proposing a framework that analyzes mobile operator data, builds

profiles of the typical demand, and identifies unusual situations in network-wide

usages. We evaluate our framework on two real-world mobile traffic datasets, and show how it extracts from these a limited number of meaningful mobile demand

profiles. In addition, the proposed framework singles out a large number of outlying

behaviors in both case studies, which are mapped to social events or technical issues in the network.

S3I_NS2_16_055 - Enhanced Indoor Location Tracking Through Body Shadowing Compensation

This paper presents a radio frequency (RF)-based location tracking system that improves its performance by eliminating the shadowing caused by the human body of

the user being tracked. The presence of such a user will influence the RF signal paths

between a body-worn node and the receiving nodes. This influence will vary with the

user's location and orientation and, as a result, will deteriorate the performance regarding location tracking. By using multiple mobile nodes, placed on different parts

of a human body, we exploit the fact that the combination of multiple measured signal

strengths will show less variation caused by the user's body. Another method is to compensate explicitly for the influence of the body by using the user's orientation

toward the fixed infrastructure nodes. Both approaches can be independently

combined and reduce the influence caused by body shadowing, hereby improving the tracking accuracy. The overall system performance is extensively verified on a

building-wide testbed for sensor experiments. The results show a significant

improvement in tracking accuracy. The total improvement in mean accuracy is 38.1%

when using three mobile nodes instead of one and simultaneously compensating for the user's orientation.

S3I_NS2_16_056 - Efficient and Privacy-preserving Polygons Spatial Query Framework for Location-based Services

With the pervasiveness of mobile devices and the development of wireless communication technique, location-based services (LBS) have made our life more

convenient, and the polygons spatial query, which can provide more flexible LBS, has

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attracted considerable interest recently. However, the flourish of polygons spatial

query still faces many challenges including the query information privacy. In this paper, we present an efficient and privacy-preserving polygons spatial query

framework for location-based services, called Polaris. With Polaris, the LBS provider

outsources the encrypted LBS data to cloud server, and the registered user can query any polygon range to get accurate LBS results without divulging his/her query

information to the LBS provider and cloud server. Specifically, an efficient special

polygons spatial query algorithm (SPSQ) over ciphertext is constructed, based on an

improved homomorphic encryption technology over composite order group. With SPSQ, Polaris can search outsourced encrypted LBS data in cloud server by the

encrypted request, and respond the encrypted polygons spatial query results

accurately. Detailed security analysis shows that the proposed Polaris can resist various known security threats. In addition, performance evaluations via

implementing Polaris on smartphone and workstation with real LBS dataset

demonstrate Polaris’ effectiveness in term of real environment.

S3I_NS2_16_057 - A General Privacy-Preserving Auction Mechanism for

Secondary Spectrum Markets

Auctions are among the best-known market-based tools to solve the problem of

dynamic spectrum redistribution. In recent years, a good number of strategy-proof

auction mechanisms have been proposed to improve spectrum utilization and to prevent market manipulation. However, the issue of privacy preservation in spectrum

auctions remains open. On the one hand, truthful bidding reveals bidders' private

valuations of the spectrum. On the other hand, coverage/interference areas of the bidders may be revealed to determine conflicts. In this paper, we present PISA, which

is a PrIvacy preserving and Strategy-proof Auction mechanism for spectrum

allocation. PISA provides protection for both bid privacy and coverage/interference area privacy leveraging a privacy-preserving integer comparison protocol, which is

well applicable in other contexts. We not only theoretically prove the privacy-

preserving properties of PISA, but also extensively evaluate its performance.

Evaluation results show that PISA achieves good spectrum allocation efficiency with light computation and communication overheads.

S3I_NS2_16_058 - Optimized In-Band Full-Duplex MIMO Relay Under Single-Stream Transmission

This paper presents a coherent scheme to optimize an in-band full-duplex multiple-input-multiple-output (MIMO) relay via beamforming and transmit power

allocation in a two-hop single-input-single-output (SISO) link under full channel

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knowledge and perfect hardware assumptions. First, we derive in closed form the

optimal pair of transmit power and receive filter for a fixed transmit filter by unifying the minimum-mean-square-error (MMSE) filtering with the SISO-equivalent power

allocation, as an iterative approach is not guaranteed to converge to global optimum.

Second, we propose a heuristic algorithm to approximate the optimal transmit filter for a fixed receive filter. Furthermore, we study the well-known null-space projection

constraint and derive a singular value decomposition (SVD)-based solution for the

arbitrary-rank self-interference channel by generalizing the optimal solution under the

assumption of rank-1 self-interference channel. Finally, we combine these solutions into a partially iterative algorithm in order to address the global optimization as our

observations justify that some of the aforementioned schemes converge to the optimal

solution under certain criteria. The numerical analysis of the proposed iterative algorithm demonstrates close-to-optimal performance relative to the theoretical upper

bound of the end-to-end link in terms of maximum achievable throughput.

S3I_NS2_16_059 - The Extra Connectivity, Extra Conditional Diagnosability,

and t/m -Diagnosability of Arrangement Graphs

Extra connectivity is an important indicator of the robustness of a

multiprocessor system in presence of failing processors. The g -extra conditional

diagnosability and the t/m -diagnosability are two important diagnostic strategies at

system-level that can significantly enhance the system’s self-diagnosing capability. The g -extra conditional diagnosability is defined under the assumption that every

component of the system removing a set of faulty vertices has more than g vertices.

The t/m -diagnosis strategy can detect up to t faulty processors which might include at most m misdiagnosed processors, where m is typically a small integer number. In

this paper, we analyze the combinatorial properties and fault tolerant ability for an

(n,k) -arrangement graph, denoted by A_{n,k} , a well-known interconnection network proposed for multiprocessor systems. We first establish that the A_{n,k} ’s

one-extra connectivity is (2k-1)break (n-k)-1 ( k\ge 3 , n\ge k+2 ), two-extra

connectivity is (3k-2)(n-k)-3 ( k\ge 4 , n\ge k+2 ), and three-extra connectivity is

(4k-4)(n-k)-4 ( k\ge 4 , n\ge k+2 or k\ge 3 , n\ge k+3 ), respectively. And then, we address the g -extra conditional diagnosability of A_{n,k} under the PMC model for

- formula> 1!\le! g \le 3 . Finally, we determine that the (n,k) -arrangement graph

A_{n,k} is [(2k-1)(n-k)-1]/1 -diagnosable ( k\ge 4 , n\ge k+2 ), [(3k-2)(n-k)-3]/2 -diagnosable ( k\ge 4 , n\ge k+2 ), and [(4k-4)(n-k)-4]/3 -diagnosable ( k\ge 4 , n\ge

k+3 ) under the PMC model, respectively.

S3I_NS2_16_060 - A Cloud-Based Architecture for the Internet of Spectrum

Devices Over Future Wireless Networks

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The dramatic increase in data rates in wireless networks has caused radio

spectrum usage to be an essential and critical issue. Spectrum sharing is widely recognized as an affordable, near-term method to address this issue. This paper first

characterizes the new features of spectrum sharing in future wireless networks,

including heterogeneity in sharing bands, diversity in sharing patterns, crowd intelligence in sharing devices, and hyperdensification in sharing networks. Then, to

harness the benefits of these unique features and promote a vision of spectrum without

bounds and networks without borders, this paper introduces a new concept of the

Internet of spectrum devices (IoSDs) and develops a cloud-based architecture for IoSD over future wireless networks, with the prime aim of building a bridging

network among various spectrum monitoring devices and massive spectrum

utilization devices, and enabling a highly efficient spectrum sharing and management paradigm for future wireless networks. Furthermore, this paper presents a systematic

tutorial on the key enabling techniques of the IoSD, including big spectrum data

analytics, hierarchal spectrum resource optimization, and quality of experience-oriented spectrum service evaluation. In addition, the unresolved research issues are

also presented.

S3I_NS2_16_061 - Optimality of Fast Matching Algorithms for Random

Networks with Applications to Structural Controllability

Network control refers to a very large and diverse set of problems including controllability of linear time-invariant dynamical systems, where the objective is to

select an appropriate input to steer the network to a desired state. There are many

notions of controllability, one of them being structural controllability, which is intimately connected to finding maximum matchings on the underlying network

topology. In this work, we study fast, scalable algorithms for finding maximum

matchings for a large class of random networks. First, we illustrate that degree distribution random networks are realistic models for real networks in terms of

structural controllability. Subsequently, we analyze a popular, fast and practical

heuristic due to Karp and Sipser as well as a simplification of it. For both heuristics,

we establish asymptotic optimality and provide results concerning the asymptotic size of maximum matchings for an extensive class of random networks.

S3I_NS2_16_062 - Cooperative Data Scheduling in Hybrid Vehicular Ad Hoc Networks: VANET as a Software Defined Network

This paper presents the first study on scheduling for cooperative data dissemination in a hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V)

communication environment. We formulate the novel problem of cooperative data

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scheduling (CDS). Each vehicle informs the road-side unit (RSU) the list of its current

neighboring vehicles and the identifiers of the retrieved and newly requested data. The RSU then selects sender and receiver vehicles and corresponding data for V2V

communication, while it simultaneously broadcasts a data item to vehicles that are

instructed to tune into the I2V channel. The goal is to maximize the number of vehicles that retrieve their requested data. We prove that CDS is NP-hard by

constructing a polynomial-time reduction from the Maximum Weighted Independent

Set (MWIS) problem. Scheduling decisions are made by transforming CDS to MWIS

and using a greedy method to approximately solve MWIS. We build a simulation model based on realistic traffic and communication characteristics and demonstrate

the superiority and scalability of the proposed solution. The proposed model and

solution, which are based on the centralized scheduler at the RSU, represent the first known vehicular ad hoc network (VANET) implementation of software defined

network (SDN) concept.

S3I_NS2_16_063 - Doherty Power Amplifier With Extended Bandwidth and

Improved Linearizability Under Carrier-Aggregated Signal Stimuli

This letter proposes a novel output combining network devised to extend a

Doherty power amplifier's (DPA's) radio frequency bandwidth (RFBW), while

maintaining the proper load modulation, and to ensure its linearizability when driven

with intra- and inter-band carrier aggregated (CA) communication signals. Based on the proposed topology, a wideband 20-W DPA was designed and implemented using

packaged GaN transistors. Drain efficiency in excess of 48% was recorded at 6 dB

back-off over the frequency range of 1.72-2.27 GHz. The DPA prototype was successfully linearized when driven with wideband (up to 160 MHz instantaneous

bandwidth) and dual-band (up to 300 MHz carrier separation) modulated stimuli and

achieved an average drain efficiency in excess of 45%.

S3I_NS2_16_064 - Beamforming OFDM Performance Under Joint Phase Noise

and I/Q Imbalance

Phase noise (PHN) and in-phase/quadrature (I/Q) imbalance are two major

radio-frequency (RF) impairments in direct-conversion wireless transceivers.

Beamforming and orthogonal frequency-division multiplexing (OFDM) schemes have been adopted in broadband wireless standards due to their significant performance

gains. In this paper, we analyze the impact of joint I/Q imbalance and PHN on

beamforming OFDM direct-conversion transceivers. We derive an exact normalized-mean-square-error expression (NMSE) and examine several special and asymptotic

cases to gain insights. One such insight is that, asymptotically, for both large signal-

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to-noise ratio (SNR) and the beamforming array size, the PHN and I/Q imbalance

effects become decoupled.

S3I_NS2_16_065 - Review of Active and Reactive Power Sharing Strategies in

Hierarchical Controlled Microgrids

Microgrids consist of multiple parallel-connected distributed generation (DG)

units with coordinated control strategies, which are able to operate in both grid-connected and islanded mode. Microgrids are attracting more and more attention since

they can alleviate the stress of main transmission systems, reduce feeder losses, and

improve system power quality. When the islanded microgrids are concerned, it is

important to maintain system stability and achieve load power sharing among the multiple parallel-connected DG units. However, the poor active and reactive power

sharing problems due to the influence of impedance mismatch of the DG feeders and

the different ratings of the DG units are inevitable when the conventional droop control scheme is adopted. Therefore, the adaptive/improved droop control, network-

based control methods and cost-based droop schemes are compared and summarized

in this paper for active power sharing. Moreover, nonlinear and unbalanced loads could further affect the reactive power sharing when regulating the active power, and

it is difficult to share the reactive power accurately only by using the enhanced virtual

impedance method. Therefore, the hierarchical control strategies are utilized as

supplements of the conventional droop controls and virtual impedance methods. The improved hierarchical control approaches such as the algorithms based on graph

theory, multi-agent system, the gain scheduling method and predictive control have

been proposed to achieve proper reactive power sharing for islanded microgrids and eliminate the effect of the communication delays on hierarchical control. Finally, the

future research trends on islanded microgrids are also discussed in this paper.

S3I_NS2_16_066 - Network Selection and Channel Allocation for Spectrum

Sharing in 5G Heterogeneous Networks

The demand for spectrum resources has increased dramatically with the advent

of modern wireless applications. Spectrum sharing, considered as a critical

mechanism for 5G networks, is envisioned to address spectrum scarcity issue and

achieve high data rate access, and guaranteed the quality of service (QoS). From the licensed network's perspective, the interference caused by all secondary users (SUs)

should be minimized. From secondary networks point of view, there is a need to

assign networks to SUs in such a way that overall interference is reduced, enabling the accommodation of a growing number of SUs. This paper presents a network selection

and channel allocation mechanism in order to increase revenue by accommodating

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more SUs and catering to their preferences, while at the same time, respecting the

primary network operator's policies. An optimization problem is formulated in order to minimize accumulated interference incurred to licensed users and the amount that

SUs have to pay for using the primary network. The aim is to provide SUs with a

specific QoS at a lower price, subject to the interference constraints of each available network with idle channels. Particle swarm optimization and a modified version of the

genetic algorithm are used to solve the optimization problem. Finally, this paper is

supported by extensive simulation results that illustrate the effectiveness of the

proposed methods in finding a near-optimal solution.

S3I_NS2_16_067 - Stability Challenges and Enhancements for Vehicular

Channel Congestion Control Approaches

Channel congestion is one of the major challenges for IEEE 802.11p-based

vehicular networks. Unless controlled, congestion increases with vehicle density, leading to high packet loss and degraded safety application performance. We study

two classes of congestion control algorithms, i.e., reactive state-based and linear

adaptive. In this paper, the reactive state-based approach is represented by the decentralized congestion control framework defined in the European

Telecommunications Standards Institute. The linear adaptive approach is represented

by the LInear MEssage Rate Integrated Control (LIMERIC) algorithm. Both

approaches control safety message transmissions as a function of channel load [i.e., channel busy percentage (CBP)]. A reactive state-based approach uses CBP directly,

defining an appropriate transmission behavior for each CBP value, e.g., via a table

lookup. By contrast, a linear adaptive approach identifies the transmission behavior that drives CBP toward a target channel load. Little is known about the relative

performance of these approaches and any existing comparison is limited by

incomplete implementations or stability anomalies. To address this, this paper makes three main contributions. First, we study and compare the two aforementioned

approaches in terms of channel stability and show that the reactive state-based

approach can be subject to major oscillation. Second, we identify the root causes and

introduce stable reactive algorithms. Finally, we compare the performance of the stable reactive approach with the linear adaptive approach and the legacy IEEE

802.11p. It is shown that the linear adaptive approach still achieves a higher message

throughput for any given vehicle density for the defined performance metrics.

S3I_NS2_16_068 - Performance Modeling and Analysis of the IEEE 802.11p

EDCA Mechanism for VANET

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This paper studies performance modeling of the IEEE 802.11p enhanced

distributed channel access (EDCA) mechanism and develops performance models to analyze the access performance of the IEEE 802.11p EDCA mechanism. A 2-D

Markov chain is first constructed to model the backoff procedure of an access

category (AC) queue and establish a relationship between the transmission probability and collision probability of the AC queue. Then, a 1-D discrete-time Markov chain is

constructed to model the contention period of an AC queue and establish another

relationship between the transmission probability and collision probability of the AC

queue. Unlike most existing work, the 1-D Markov chain is extended to be infinite in modeling the contention period of an AC queue under both saturated and nonsaturated

conditions. The two Markov models take into account all major factors that could

affect the access performance of the IEEE 802.11p EDCA mechanism, including the saturation condition, standard parameters, backoff counter freezing, and internal

collision. Based on the two Markov models, performance models are further derived

to describe the relationships between the parameters of an AC queue and the access performance of the AC queue in terms of the transmission probability, collision

probability, normalized throughput, and average access delay, respectively. The

effectiveness of the performance models are verified through simulation results.

S3I_NS2_16_069 - Non-Intrusive Planning the Roadside Infrastructure for

Vehicular Networks

In this article, we describe a strategy for planning the roadside infrastructure for

vehicular networks based on the global behavior of drivers. Instead of relying on the

trajectories of all vehicles, our proposal relies on the migration ratios of vehicles between urban regions in order to infer the better locations for deploying the roadside

units. By relying on the global behavior of drivers, our strategy does not incur in

privacy concerns. Given a set of α available roadside units, our goal is to select those α-better locations for placing the roadside units in order to maximize the number of

distinct vehicles experiencing at least one V2I contact opportunity. Our results

demonstrate that full knowledge of the vehicle trajectories are not mandatory for

achieving a close-to-optimal deployment performance when we intend to maximize the number of distinct vehicles experiencing (at least one) V2I contact opportunities.

S3I_NS2_16_070 - Enhanced power management scheme for embedded road side units

In this study, a green vehicular ad-hoc network (VANET) infrastructure is suggested. The main players in such an infrastructure are the road side units (RSUs)

which are able to harvest the energy needed for their work from the surrounding

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environment, especially the solar energy. Such a suggestion permits to install the

RSUs in any place without considering the power supply availability and hence, an extensive area is covered by the VANET infrastructure with an improved

performance. To achieve this goal, a new distributed power management scheme

called duty cycle estimation-event driven duty cycling is suggested and installed locally in the RSUs in order to decrease their power consumption and to extend the

lifetime of their batteries. Embedded UBICOM IP2022 network processer platform is

adopted to implement the proposed RSU and the detailed design steps are described,

while the necessary values of the system components such as the number of solar cell panels, battery cells capacity and so on, are tuned to suit the design goals. The

suggested method is compared with other duty cycling methods to show its

effectiveness to build a green VANET infrastructure.

S3I_NS2_16_071 - Communication Scheduling and Control of a Platoon of

Vehicles in VANETs

This paper is concerned with the problem of vehicular platoon control in

vehicular ad hoc networks subject to capacity limitation and random packet dropouts. By introducing binary sequences as the basis of network access scheduling and

modeling random packet dropouts as independent Bernoulli processes, we derive a

closed-form methodology for vehicular platoon control. In particular, an interesting

framework for network access scheduling and platoon control codesign is established based on a set of priority rules for network access control. The resulting platoon

control and scheduling algorithm can resolve network access conflicts in vehicular ad

hoc networks and guarantee string stability and zero steady-state spacing errors. The effectiveness of the method is demonstrated by numerical simulations and

experiments with laboratory-scale Arduino cars.

S3I_NS2_16_072 - Secure and Robust Multi-Constrained QoS Aware Routing

Algorithm for VANETs

Secure QoS routing algorithms are a fundamental part of wireless networks that

aim to provide services with QoS and security guarantees. In vehicular ad hoc

networks (VANETs), vehicles perform routing functions, and at the same time act as

end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the

routing process, and manipulation of the routing control messages. In this paper, we

propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the ant colony optimisation (ACO) technique to compute

feasible routes in VANETs subject to multiple QoS constraints determined by the data

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traffic type. Moreover, we extend the VANET-oriented evolving graph (VoEG) model

to perform plausibility checks on the routing control messages exchanged among vehicles. Simulation results show that the QoS can be guaranteed while applying

security mechanisms to ensure a reliable and robust routing service.

S3I_NS2_16_073 - Data Dissemination With Network Coding in Two-Way

Vehicle-to-Vehicle Networks

Vehicular ad hoc networks (VANETs) can efficiently offer safety-related and

commercial contents for in-vehicle consumption. In this paper, we analyze the

vehicle-to-vehicle (V2V) data dissemination with network coding in two-way road

networks, where the vehicles move in opposite directions. In particular, depending on whether the broadcasting coverage areas overlap or not, two-way data dissemination

is usually carried out in two phases, namely, the encountering phase and the separated

phase. We first derive the probability mass function (pmf) of the dissemination completion time for the encountering disseminators during the encountering phase.

The data dissemination velocity in the separated phase is mathematically derived. We

prove that, without the help of the data dissemination in their own direction, the vehicles cannot recover all original packets from the opposite direction under a scarce

handover condition. Furthermore, the dissemination slope and cache capacity of the

vehicles in the proposed model are also analytically presented. Simulation results are

provided to confirm the accuracy of the developed analytical results.

S3I_NS2_16_074 - Analytical Model and Performance evaluation of Long Term

Evolution for vehicle Safety Services

In traffic jam or dense vehicle environment, vehicular ad-hoc networks

(VANET) can’t meet safety requirement due to serious packet collision. The traditional cellular network solves packet collision, but suffers from long end-to-end

delay. 3GPP Long Term Evolution (LTE) overcomes both drawbacks, thus it may be

used instead of VANET in some extreme environments. We use Markov models with the dynamic scheduling and semipersistent scheduling (SPS) to evaluate how many

idle resources of LTE can be provided for safety services and how safety applications

impact on LTE traditional users. Based on the analysis, we propose to reserve the idle

radio resources in LTE for vehicular safety services (LTE-V). Additionally, we propose the weighted-fair-queueing (WFQ) algorithm to schedule beacons for safety

services using LTE reserved resource. Numerical results verify that the proposed

mechanism can significantly improve the reliability of safety application by borrowing limited LTE bandwidth. We also build NS3 simulation platform to verify

the effectiveness of the proposed Markov models. Finally, the reliability of

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applications including cooperation collision warning, slow vehicle indication and rear-

end collision warning using DSRC with LTE-V are evaluated. The simulation results demonstrate that the stringent QoS requirement of the above three applications can be

satisfied even under heavy traffic.

S3I_NS2_16_075 - An Efficient Conditional Privacy-Preserving Authentication

Scheme for Vehicular Sensor Networks Without

Constructing intelligent and efficient transportation systems for modern

metropolitan areas has become a very important quest for nations possessing

metropolitan cities with ever-increasing populations. A new trend is the development

of smart vehicles with multiple sensors able to dynamically form a temporary vehicular ad hoc network (VANET) or a vehicular sensor network (VSN). Along with

a wireless-enabled roadside unit (RSU) network, drivers in a VSN can efficiently

exchange important or urgent traffic information and make driving decisions accordingly. In order to support secure communication and driver privacy for vehicles

in a VSN, we develop a new identity-based (ID-based) signature based on the elliptic

curve cryptosystem (ECC) and then adopt it to propose a novel conditional privacy-preserving authentication scheme based on our invented ID-based signature. This

scheme provides secure authentication process for messages transmitted between

vehicles and RSUs. A batch message verification mechanism is also supported by the

proposed scheme to increase the message processing throughput of RSUs. To further enhance scheme efficiency, both pairing operation and MapToPoint operation are not

applied in the proposed authentication scheme. In comparison with existing pseudo-

ID-based authentication solutions for VSN, this paper shows that the proposed scheme has better performance in terms of time consumption.

S3I_NS2_16_076 - SCRP: Stable CDS-Based Routing Protocol for Urban Vehicular Ad Hoc Networks

This paper addresses the issue of selecting routing paths with minimum end-to-end delay (E2ED) for nonsafety applications in urban vehicular ad hoc networks

(VANETs). Most existing schemes aim at reducing E2ED via greedy-based

techniques (i.e., shortest path, connectivity, or number of hops), which make them

prone to the local maximum problem and to data congestion, leading to higher E2ED. As a solution, we propose SCRP, which is a distributed routing protocol that

computes E2ED for the entire routing path before sending data messages. To do so,

SCRP builds stable backbones on road segments and connects them at intersections via bridge nodes. These nodes assign weights to road segments based on the collected

information of delay and connectivity. Routes with the lowest aggregated weights are

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selected to forward data packets. Simulation results show that SCRP outperforms

some of the well-known protocols in literature.

S3I_NS2_16_077 - DIVERT: A Distributed Vehicular Traffic Re-routing System

for Congestion Avoidance

Centralized solutions for vehicular traffic re-routing to alleviate congestion

suffer from two intrinsic problems: scalability, as the central server has to perform intensive computation and communication with the vehicles in real-time; and privacy,

as the drivers have to share their location as well as the origins and destinations of

their trips with the server. This article proposes DIVERT, a distributed vehicular re-

routing system for congestion avoidance. DIVERT offloads a large part of the rerouting computation at the vehicles, and thus, the re-routing process becomes

practical in real-time. To take collaborative rerouting decisions, the vehicles exchange

messages over vehicular ad hoc networks. DIVERT is a hybrid system because it still uses a server and Internet communication to determine an accurate global view of the

traffic. In addition, DIVERT balances the user privacy with the re-routing

effectiveness. The simulation results demonstrate that, compared with a centralized system, the proposed hybrid system increases the user privacy by 92% on average. In

terms of average travel time, DIVERT’s performance is slightly less than that of the

centralized system, but it still achieves substantial gains compared to the no re-routing

case. In addition, DIVERT reduces the CPU and network load on the server by 99.99% and 95%, respectively.

S3I_NS2_16_078 - A Threshold Anonymous Authentication Protocol for VANETs

Vehicular ad hoc networks (VANETs) have recently received significant attention in improving traffic safety and efficiency. However, communication trust

and user privacy still present practical concerns to the deployment of VANETs, as

many existing authentication protocols for VANETs either suffer from the heavy workload of downloading the latest revocation list from a remote authority or cannot

allow drivers on the road to decide the trustworthiness of a message when the

authentication on messages is anonymous. In this paper, to cope with these

challenging concerns, we propose a new authentication protocol for VANETs in a decentralized group model by using a new group signature scheme. With the

assistance of the new group signature scheme, the proposed authentication protocol is

featured with threshold authentication, efficient revocation, unforgeability, anonymity, and traceability. In addition, the assisting group signature scheme may

also be of independent interest, as it is characterized by efficient traceability and

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message linkability at the same time. Extensive analyses indicate that our proposed

threshold anonymous authentication protocol is secure, and the verification of messages among vehicles can be accelerated by using batch message processing

techniques.

S3I_NS2_16_079 - A Novel Approach for Improved Vehicular Positioning Using

Cooperative Map Matching and Dynamic Base Station DGPS Concept

In this paper, a novel approach for improving vehicular positioning is

presented. This method is based on the cooperation of the vehicles by communicating

their measured information about their position. This method consists of two steps. In

the first step, we introduce our cooperative map matching method. This map matching method uses the V2V communication in a vehicular ad hoc network (VANET) to

exchange global positioning system (GPS) information between vehicles. Having a

precise road map, vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning.

After that, we have proposed the concept of a dynamic base station DGPS (DDGPS),

which is used by vehicles in the second step to generate and broadcast the GPS pseudorange corrections that can be used by newly arrived vehicles to improve their

positioning. The DDGPS is a decentralized cooperative method that aims to improve

the GPS positioning by estimating and compensating the common error in GPS

pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS

method, the pseudorange corrections are estimated based on the receiver's belief on its

positioning and its uncertainty and then broadcasted to other GPS receivers. The performance of the proposed algorithm has been verified with simulations in several

realistic scenarios.

S3I_NS2_16_080 - Stochastic Modeling of Single-Hop Cluster Stability in

Vehicular Ad Hoc Networks

Node clustering is a potential approach to improve the scalability of networking

protocols in vehicular ad hoc networks (VANETs). High relative vehicle mobility and

frequent network topology changes inflict new challenges on maintaining stable

clusters. As a result, cluster stability is a crucial measure of the efficiency of clustering algorithms for VANETs. This paper presents a stochastic analysis of the

vehicle mobility impact on single-hop cluster stability. A stochastic mobility model is

adopted to capture the time variations of intervehicle distances (distance headways). First, we propose a discrete-time lumped Markov chain to model the time variations

of a system of distance headways. Second, the first passage time analysis is used to

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derive probability distributions of the time periods of the invariant cluster-overlap

state and cluster membership as measures of cluster stability. Third, queueing theory is utilized to model the limiting behaviors of the numbers of common and unclustered

nodes between neighboring clusters. Numerical results are presented to evaluate the

proposed models, which demonstrate a close agreement between analytical and simulation results.

MATLAB IMAGE PROCESSING AND

WIRELESS COMMUNICATION - 2016-

2017

S3I_MAT16_001 - Dynamic Facial Expression Recognition with Atlas Construction and

Sparse Representation

In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific

facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse

groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution

information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different

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applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression

monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates

of the new method are consistently higher than other methods under comparison. S3I_MAT16_002 - Lossless Compression of JPEG Coded Photo Collections

The explosion of digital photos has posed a significant challenge to photo storage and transmission for both personal devices and cloud platforms. In this paper, we propose a

novel lossless compression method to further reduce the size of a set of JPEG coded correlated images without any loss of information. The proposed method jointly removes inter/intra image redundancy in the feature, spatial, and frequency domains. For each collection, we first organize

the images into a pseudo video by minimizing the global prediction cost in the feature domain. We then present a hybrid disparity compensation method to better exploit both the global and

local correlations among the images in the spatial domain. Furthermore, the redundancy between each compensated signal and the corresponding target image is adaptively reduced in the frequency domain. Experimental results demonstrate the effectiveness of the proposed lossless

compression method. Compared with the JPEG coded image collections, our method achieves average bit savings of more than 31%.

S3I_MAT16_003 - Pixel modeling using histograms based on fuzzy partitions for dynamic

background subtraction

We propose a novel pixel-modeling approach for background subtraction using histograms based on strong uniform fuzzy partitions. In the proposed method, the temporal distribution of pixel values is represented by a histogram based on a triangular partition. The

threshold for background segmentation is set adaptively according to the shape of the histogram. Histogram accumulation is controlled adaptively by a fuzzy controller under a supervised

learning framework. Benefiting from the adaptive scheme, with no parameter tuning, the proposed algorithm functions well across a wide spectrum of challenging environments. The performance of the proposed method is evaluated against more than 20 state-of-the-art methods

in complex outdoor environments, particularly in those consisting of highly dynamic backgrounds and camouflaged foregrounds. Experimental results confirm that the proposed

method performs effectively in terms of both the true positive rate and the noise suppression ability. Further, it outperforms other state-of-the-art methods by a significant margin.

S3I_MAT16_004 - Layer-Based Approach for Image Pair Fusion Recently, image pairs, such as noisy and blurred images or infrared and noisy

images, have been considered as a solution to provide high-quality photographs under low lighting conditions. In this paper, a new method for decomposing the image pairs into two layers, i.e., the base layer and the detail layer, is proposed for image pair fusion. In the case of infrared

and noisy images, simple naive fusion leads to unsatisfactory results due to the discrepancies in brightness and image structures between the image pair. To address this problem, a local

contrast-preserving conversion method is first proposed to create a new base layer of the infrared image, which can have visual appearance similar to another base layer, such as the denoised noisy image. Then, a new way of designing three types of detail layers from the given noisy and

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infrared images is presented. To estimate the noise-free and unknown detail layer from the three designed detail layers, the optimization framework is modeled with residual-based sparsity and

patch redundancy priors. To better suppress the noise, an iterative approach that updates the detail layer of the noisy image is adopted via a feedback loop. This proposed layer-based method

can also be applied to fuse another noisy and blurred image pair. The experimental results show that the proposed method is effective for solving the image pair fusion problem.

S3I_MAT16_005 - Adaptive Pairing Reversible Watermarking This letter revisits the pairwise reversible watermarking scheme of Ou et al., 2013.

An adaptive pixel pairing that considers only pixels with similar prediction errors is introduced. This adaptive approach provides an increased number of pixel pairs where both pixels are embedded and decreases the number of shifted pixels. The adaptive pairwise reversible

watermarking outperforms the state-of-the-art low embedding bit-rate schemes proposed so far.

S3I_MAT16_006 - Adaptive Part-Level Model Knowledge Transfer for Gender

Classification In this letter, we propose an adaptive part-level model knowledge transfer approach

for gender classification of facial images based on Fisher vector (FV). Specifically, we first decompose the whole face image into several parts and compute the dense FVs on each face part. An adaptive transfer learning model is then proposed to reduce the discrepancies between the

training data and the testing data for enhancing classification performance. Compared to the existing gender classification methods, the proposed approach is more adaptive to the testing

data, which is quite beneficial to the performance improvement. Extensive experiments on several public domain face data sets clearly demonstrate the effectiveness of the proposed approach.

S3I_MAT16_007 - Patch-Based Video Denoising With Optical Flow Estimation

A novel image sequence denoising algorithm is presented. The proposed approach takes advantage of the selfsimilarity and redundancy of adjacent frames. The algorithm is inspired by fusion algorithms, and as the number of frames increases, it tends to a pure temporal

average. The use of motion compensation by regularized optical flow methods permits robust patch comparison in a spatiotemporal volume. The use of principal component analysis ensures

the correct preservation of fine texture and details. An extensive comparison with the state-of-the-art methods illustrates the superior performance of the proposed approach, with improved texture and detail reconstruction.

S3I_MAT16_008 - Fusion of Quantitative Image and Genomic Biomarkers to Improve

Prognosis Assessment of Early Stage Lung Cancer Patients This study aims to develop a new quantitative image feature analysis scheme and investigate its role along with 2 genomic biomarkers namely, protein expression of the excision

repair cross-complementing 1 (ERCC1) genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting cancer recurrence risk of Stage I non-small-cell lung cancer

(NSCLC) patients after surgery. Methods: By using chest computed tomography images, we developed a computer-aided detection scheme to segment lung tumors and computed tumor-related image features. After feature selection, we trained a Naïve Bayesian network based

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classifier using 8 image features and a Multilayer Perceptron classifier using 2 genomic biomarkers to predict cancer recurrence risk, respectively. Two classifiers were trained and

tested using a dataset with 79 Stage I NSCLC cases, a synthetic minority oversampling technique and a leave-one-case-out validation method. A fusion method was also applied to combine

prediction scores of two classifiers. Results: AUC (areas under ROC curves) values are 0.78±0.06 and 0.68±0.07 when using the image feature and genomic biomarker based classifiers, respectively. AUC value significantly increased to 0.84±0.05 (p<0.05) when fusion of two

classifier-generated prediction scores using an equal weighting factor. Conclusion: A quantitative image feature based classifier yielded significantly higher discriminatory power than

a genomic biomarker based classifier in predicting cancer recurrence risk. Fusion of prediction scores generated by the two classifiers further improved prediction performance. Significance: We demonstrated a new approach that has potential to assist clinicians in more effectively

managing Stage I NSCLC patients to reduce cancer recurrence risk.

S3I_MAT16_009 - Multivideo Object Cosegmentation for Irrelevant Frames Involved

Videos Even though there have been a large amount of previous work on video segmentation

techniques, it is still a challenging task to extract the video objects accurately without interactions, especially for those videos which contain irrelevant frames (frames containing no common targets). In this essay, a novel multivideo object cosegmentation method is raised to

cosegment common or similar objects of relevant frames in different videos, which includes three steps: 1) object proposal generation and clustering within each video; 2) weighted graph

construction and common objects selection; and 3) irrelevant frames detection and pixel-level segmentation refinement. We apply our method on challenging datasets and exhaustive comparison experiments demonstrate the effectiveness of the proposed method.

S3I_MAT16_010 - Multi-Viewpoint Panorama Construction with Wide-Baseline Images

We present a novel image stitching approach, which can produce visually

plausible panoramic images with input taken from different viewpoints. Unlike

previous methods, our approach allows wide baselines between images and non-planar scene structures. Instead of 3D reconstruction, we design a mesh based

framework to optimize alignment and regularity in 2D. By solving a global objective

function consisting of alignment and a set of prior constraints, we construct panoramic images, which are locally as perspective as possible and yet nearly orthogonal in the

global view. We improve composition and achieve good performance on misaligned

area. Experimental results on challenging data demonstrate the effectiveness of the

proposed method.

S3I_MAT16_011 - A Security-Enhanced Alignment-Free Fuzzy Vault-Based

Fingerprint Cryptosystem Using Pair-Polar Minutiae Structures Alignment-free fingerprint cryptosystems perform matching using relative

information between minutiae, e.g., local minutiae structures, is promising, because it

can avoid the recognition errors and information leakage caused by template

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alignment/registration. However, as most local minutiae structures only contain

relative information of a few minutiae in a local region, they are less discriminative than the global minutiae pattern. Besides, the similarity measures for trivially/coarsely

quantized features in the existing work cannot provide a robust way to deal with

nonlinear distortions, a common form of intra-class variation. As a result, the recognition accuracy of current alignment-free fingerprint cryptosystems is

unsatisfying. In this paper, we propose an alignment-free fuzzy vault-based fingerprint

cryptosystem using highly discriminative pair-polar (P-P) minutiae structures. The

fine quantization used in our system can largely retain information about a fingerprint template and enables the direct use of a traditional, well-established minutiae matcher.

In terms of template/key protection, the proposed system fuses cancelable biometrics

and biocryptography. Transforming the P-P minutiae structures before encoding destroys the correlations between them, and can provide privacy-enhancing features,

such as revocability and protection against cross-matching by setting distinct

transformation seeds for different applications. The comparison with other minutiae-based fingerprint cryptosystems shows that the proposed system performs favorably

on selected publicly available databases and has strong security.

S3I_MAT16_012 - Microwave Unmixing With Video Segmentation for Inferring

Broadleaf and Needleleaf Brightness Temperatures and Abundances From

Mixed Forest Observations

Passive microwave sensors have better capability of penetrating forest layers to obtain more information from forest canopy and ground surface. For forest

management, it is useful to study passive microwave signals from forests. Passive

microwave sensors can detect signals from needleleaf, broadleaf, and mixed forests. The observed brightness temperature of a mixed forest can be approximated by a

linear combination of the needleleaf and broadleaf brightness temperatures weighted

by their respective abundances. For a mixed forest observed by an N-band microwave radiometer with horizontal and vertical polarizations, there are 2 N observed

brightness temperatures. It is desirable to infer 4 N + 2 unknowns: 2 N broadleaf

brightness temperatures, 2 N needleleaf brightness temperatures, 1 broadleaf

abundance, and 1 needleleaf abundance. This is a challenging underdetermined problem. In this paper, we devise a novel method that combines microwave unmixing

with video segmentation for inferring broadleaf and needleleaf brightness

temperatures and abundances from mixed forests. We propose an improved Otsu method for video segmentation to infer broadleaf and needleleaf abundances. The

brightness temperatures of needleleaf and broadleaf trees can then be solved by the

nonnegative least squares solution. For our mixed forest unmixing problem, it turns out that the ordinary least squares solution yields the desired positive brightness

temperatures. The experimental results demonstrate that the proposed method is able

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to unmix broadleaf and needleleaf brightness temperatures and abundances well. The

absolute differences between the reconstructed and observed brightness temperatures of the mixed forest are well within 1 K.

S3I_MAT16_013 - 2D Orthogonal Locality Preserving Projection for Image Denoising

Sparse representations using transform-domain techniques are widely used for better

interpretation of the raw data. Orthogonal locality preserving projection (OLPP) is a

linear technique that tries to preserve local structure of data in the transform domain as well. Vectorized nature of OLPP requires high-dimensional data to be converted to

vector format, hence may lose spatial neighborhood information of raw data. On the

other hand, processing 2D data directly, not only preserves spatial information, but also improves the computational efficiency considerably. The 2D OLPP is expected to

learn the transformation from 2D data itself. This paper derives mathematical

foundation for 2D OLPP. The proposed technique is used for image denoising task. Recent state-of-the-art approaches for image denoising work on two major

hypotheses, i.e., non-local self-similarity and sparse linear approximations of the data.

Locality preserving nature of the proposed approach automatically takes care of self-similarity present in the image while inferring sparse basis. A global basis is adequate

for the entire image. The proposed approach outperforms several state-of-the-art

image denoising approaches for gray-scale, color, and texture images.

S3I_MAT16_014 - Exploring the Usefulness of Light Field Cameras for

Biometrics : An Empirical Study on Face and Iris Recognition

A light field sensor can provide useful information in terms of multiple depth (or focus) images, holding additional information that is quite useful for biometric

applications. In this paper, we examine the applicability of a light field camera for

biometric applications by considering two prominently used biometric characteristics: 1) face and 2) iris. To this extent, we employed a Lytro light field camera to construct

two new and relatively large scale databases, for both face and iris biometrics. We

then explore the additional information available from different depth images, which

are rendered by light field camera, in two different manners: 1) by selecting the best focus image from the set of depth images and 2) combining all the depth images using

super-resolution schemes to exploit the supplementary information available within

the set elements. Extensive evaluations are carried out on our newly constructed database, demonstrating the significance of using additional information rendered by a

light field camera to improve the overall performance of the biometric system.

S3I_MAT16_015 - Spectral–Spatial Adaptive Sparse Representation for

Hyperspectral Image Denoising

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In this paper, a novel spectral-spatial adaptive sparse representation (SSASR) method

is proposed for hyperspectral image (HSI) denoising. The proposed SSASR method aims at improving noise-free estimation for noisy HSI by making full use of highly

correlated spectral information and highly similar spatial information via sparse

representation, which consists of the following three steps. First, according to spectral correlation across bands, the HSI is partitioned into several nonoverlapping band

subsets. Each band subset contains multiple continuous bands with highly similar

spectral characteristics. Then, within each band subset, shape-adaptive local regions

consisting of spatially similar pixels are searched in spatial domain. This way, spectral-spatial similar pixels can be grouped. Finally, the highly correlated and

similar spectral-spatial information in each group is effectively used via the joint

sparse coding, in order to generate better noise-free estimation. The proposed SSASR method is evaluated by different objective metrics in both real and simulated

experiments. The numerical and visual comparison results demonstrate the

effectiveness and superiority of the proposed method.

S3I_MAT16_016 - Robust Sclera Recognition System With Novel Sclera

Segmentation and Validation Techniques Sclera blood veins have been investigated recently as a biometric trait which can be

used in a recognition system. The sclera is the white and opaque outer protective part

of the eye. This part of the eye has visible blood veins which are randomly distributed.

This feature makes these blood veins a promising factor for eye recognition. The sclera has an advantage in that it can be captured using a visible-wavelength camera.

Therefore, applications which may involve the sclera are wide ranging. The

contribution of this paper is the design of a robust sclera recognition system with high accuracy. The system comprises of new sclera segmentation and occluded eye

detection methods. We also propose an efficient method for vessel enhancement,

extraction, and binarization. In the feature extraction and matching process stages, we additionally develop an efficient method, that is, orientation, scale, illumination, and

deformation invariant. The obtained results using UBIRIS.v1 and UTIRIS databases

show an advantage in terms of segmentation accuracy and computational complexity

compared with state-of-the-art methods due to Thomas, Oh, Zhou, and Das.

S3I_MAT16_017 - Enhancing Sketch-Based Image Retrieval by Re-Ranking and

Relevance Feedback A sketch-based image retrieval often needs to optimize the tradeoff between

efficiency and precision. Index structures are typically applied to large-scale databases

to realize efficient retrievals. However, the performance can be affected by quantization errors. Moreover, the ambiguousness of user-provided examples may

also degrade the performance, when compared with traditional image retrieval

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methods. Sketch-based image retrieval systems that preserve the index structure are

challenging. In this paper, we propose an effective sketch-based image retrieval approach with re-ranking and relevance feedback schemes. Our approach makes full

use of the semantics in query sketches and the top ranked images of the initial results.

We also apply relevance feedback to find more relevant images for the input query sketch. The integration of the two schemes results in mutual benefits and improves the

performance of the sketch-based image retrieval.

S3I_MAT16_018 - Detection of Moving Objects Using Fuzzy Color Difference Histogram Based Background Subtraction

Detection of moving objects in the presence of complex scenes such as dynamic

background (e.g, swaying vegetation, ripples in water, spouting fountain), illumination variation, and camouflage is a very challenging task. In this context, we

propose a robust background subtraction technique with three contributions. First, we

present the use of color difference histogram (CDH) in the background subtraction algorithm. This is done by measuring the color difference between a pixel and its

neighbors in a small local neighborhood. The use of CDH reduces the number of false

errors due to the non-stationary background, illumination variation and camouflage. Secondly, the color difference is fuzzified with a Gaussian membership function.

Finally, a novel fuzzy color difference histogram (FCDH) is proposed by using fuzzy

c-means (FCM) clustering and exploiting the CDH. The use of FCM clustering

algorithm in CDH reduces the large dimensionality of the histogram bins in the computation and also lessens the effect of intensity variation generated due to the fake

motion or change in illumination of the background. The proposed algorithm is tested

with various complex scenes of some benchmark publicly available video sequences. It exhibits better performance over the state-of-the-art background subtraction

techniques available in the literature in terms of classification accuracy metrics like

MCC and PCC.

S3I_MAT16_019 - A Decomposition Framework for Image Denoising

Algorithms

In this paper, we consider an image decomposition model that provides a novel framework for image denoising. The model computes the components of the image to

be processed in a moving frame that encodes its local geometry (directions of

gradients and level lines). Then, the strategy we develop is to denoise the components of the image in the moving frame in order to preserve its local geometry, which would

have been more affected if processing the image directly. Experiments on a whole

image database tested with several denoising methods show that this framework can provide better results than denoising the image directly, both in terms of Peak signal-

to-noise ratio and Structural similarity index metrics.

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S3I_MAT16_020 - Distance-Based Encryption: How to Embed Fuzziness in Biometric-Based Encryption

We introduce a new encryption notion called distance-based encryption (DBE) to

apply biometrics in identity-based encryption. In this notion, a ciphertext encrypted with a vector and a threshold value can be decrypted with a private key of another

vector, if and only if the distance between these two vectors is less than or equal to the

threshold value. The adopted distance measurement is called Mahalanobis distance,

which is a generalization of Euclidean distance. This novel distance is a useful recognition approach in the pattern recognition and image processing community. The

primary application of this new encryption notion is to incorporate biometric

identities, such as face, as the public identity in an identity-based encryption. In such an application, usually the input biometric identity associated with a private key will

not be exactly the same as the input biometric identity in the encryption phase, even

though they are from the same user. The introduced DBE addresses this problem well as the decryption condition does not require identities to be identical but having small

distance. The closest encryption notion to DBE is the fuzzy identity-based encryption,

but it measures biometric identities using a different distance called an overlap distance (a variant of Hamming distance) that is not widely accepted by the pattern

recognition community, due to its long binary representations. In this paper, we study

this new encryption notion and its constructions. We show how to generically and

efficiently construct such a DBE from an inner product encryption (IPE) with reasonable size of private keys and ciphertexts. We also propose a new IPE scheme

with the shortest private key to build DBE, namely, the need for a short private key.

Finally, we study the encryption efficiency of DBE by splitting our IPE encryption algorithm into offline and online algorithms.

S3I_MAT16_021 - Scalable Feature Matching by Dual Cascaded Scalar Quantization for Image Retrieval

In this paper, we investigate the problem of scalable visual feature matching in large-

scale image search and propose a novel cascaded scalar quantization scheme in dual

resolution. We formulate the visual feature matching as a range-based neighbor search problem and approach it by identifying hyper-cubes with a dual-resolution scalar

quantization strategy. Specifically, for each dimension of the PCA-transformed

feature, scalar quantization is performed at both coarse and fine resolutions. The scalar quantization results at the coarse resolution are cascaded over multiple

dimensions to index an image database. The scalar quantization results over multiple

dimensions at the fine resolution are concatenated into a binary super-vector and stored into the index list for efficient verification. The proposed cascaded scalar

quantization (CSQ) method is free of the costly visual codebook training and thus is

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independent of any image descriptor training set. The index structure of the CSQ is

flexible enough to accommodate new image features and scalable to index large-scale image database. We evaluate our approach on the public benchmark datasets for

large-scale image retrieval. Experimental results demonstrate the competitive retrieval

performance of the proposed method compared with several recent retrieval algorithms on feature quantization.

S3I_MAT16_022 - ACE–An Effective Anti-forensic Contrast Enhancement

Technique Detecting Contrast Enhancement (CE) in images and anti-forensic approaches against

such detectors have gained much attention in multimedia forensics lately. Several

contrast enhancement detectors analyze the first order statistics such as gray-level histogram of images to determine whether an image is CE or not. In order to counter

these detectors various anti-forensic techniques have been proposed. This led to a

technique that utilized second order statistics of images for CE detection. In this letter, we propose an effective anti-forensic approach that performs CE without significant

distortion in both the first and second order statistics of the enhanced image. We

formulate an optimization problem using a variant of the well known Total Variation (TV) norm image restoration formulation. Experiments show that the algorithm

effectively overcomes the first and second order statistics based detectors without loss

in quality of the enhanced image.

S3I_MAT16_023 - Visualization of Tumor Response to Neoadjuvant Therapy for

Rectal Carcinoma by Nonlinear Optical Imaging

The continuing development of nonlinear optical imaging techniques has opened many new windows in biological exploration. In this study, a nonlinear optical

microscopy-multiphoton microscopy (MPM) was expanded to detect tumor response

in rectal carcinoma after neoadjuvant therapy; especially normal tissue, pre- and post-therapeutic cancerous tissues were investigated in order to present more detailed

information and make comparison. It was found that the MPM has ability not only to

directly visualize histopathologic changes in rectal carcinoma, including stromal

fibrosis, colloid response, residual tumors, blood vessel hyperplasia, and inflammatory reaction, which had been proven to have important influence on

estimation of the prognosis and the effect of neoadjuvant treatment, but also to

provide quantitative optical biomarkers including the intensity ratio of SHG over TPEF and collagen orientation index. These results show that the MPM will become a

useful tool for clinicians to determine whether neoadjuvant therapy is effective or

treatment strategy is approximate, and this study may provide the groundwork for further exploration into the application of MPM in a clinical setting.

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S3I_MAT16_024 - Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation

Edge-based active contour models are effective in segmenting images with intensity

inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as in medical images. Traditional edge-stop functions (ESFs) utilize

only gradient information, which fails to stop contour evolution at such boundaries

because of the small gradient magnitudes. To address this problem, we propose a

framework to construct a group of ESFs for edge-based active contour models to segment objects with poorly defined boundaries. In our framework, which

incorporates gradient information as well as probability scores from a standard

classifier, the ESF can be constructed from any classification algorithm and applied to any edge-based model using a level set method. Experiments on medical images using

the distance regularized level set for edge-based active contour models as well as the

k-nearest neighbors and the support vector machine confirm the effectiveness of the proposed approach.

S3I_MAT16_025 - A Combined KFDA Method and GUI Realized for Face Recognition

Traditional face recognition methods such as Principal Components Analysis(PCA),

Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are

linear discriminant methods, but in the real situation, a lot of problems can't be linear discriminated; therefore, researchers proposed face recognition method based on

kernel techniques which can transform the nonlinear problem of inputting space into

the linear problem of high dimensional space. In this paper, we propose a recognition method based on kernel function which combines kernel Fisher Discriminant

Analysis(KFDA) with kernel Principle Components Analysis(KPCA) and use typical

ORL(Olivetti Research Laboratory) face database as our experimental database. There are four key steps: constructing feature subspace, image projection, feature extraction

and image recognition. We found that the recognition accuracy has been greatly

improved by using nonlinear identification method and combined feature extraction

methods. We use MATLAB software as the platform, and use the GUI to demonstrate the process of face recognition in order to achieving human-computer interaction and

making the process and result more intuitive.

S3I_MAT16_026 - A Cost-Effective Minutiae Disk Code For Fingerprint Recognition And Its Implementation

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Fingerprint is one of the unique biometric features for the application of identity

security. Minutiae cylinder code (MCC) constructs a cylinder for each minutia to record the contribution of the neighbor minutiae, which has great performance on

fingerprint recognition. However, the computation time of the MCC is high.

Therefore, we proposed a new disk structure to encode the local structure for each minutia. The proposed minutiae disk code (MDC) clearly illustrates the distribution of

the neighbor minutiae and encodes the neighbor minutiae more efficiently by having

280.08× speed faster than the MCC encoding part on Matlab platform. The proposed

MDC approach has 96.81% recognition rate on FVC2000 and FVC2002 datasets. The hardware implementation can achieve the operating frequency at 111MHz, which can

process 1234 fingerprint images per second with the image size of 255 χ 255 and the

maximum of 64 minutiae, under TSMC 90nm CMOS technology. The hardware implementation has 141.27× speed faster than the MCC method.

S3I_MAT16_027 - A Hands-on Application-Based Tool for STEM Students to Understand Differentiation

The main goal of this project is to illustrate to college students in science, technology,

engineering, and mathematics (STEM) fields some fundamental concepts in calculus. A high-level technical computing language - MATLAB, is the core platform used in

the construction of this project. A graphical user interface (GUI) is designed to

interactively explain the intuition behind a key mathematical concept: differentiation.

The GUI demonstrates how a derivative operation (as a form of kernel) can be applied on one-dimensional (1D) and two-dimensional (2D) images (as a form of vector). The

user can interactively select from a set of predetermined operations and images in

order to show how the selected kernel operates on the corresponding image. Such interactive tools in calculus courses are of great importance and need, especially for

STEM students who seek an intuitive and visual understanding of mathematical

notions that are often presented to them as abstract concepts. In addition to students, instructors can greatly benefit from using such tools to elucidate the use of

fundamental concepts in mathematics in a real world context.

S3I_MAT16_028 - Rotation Invariant Texture Description Using Symmetric Dense Microblock Difference

This letter is devoted to the problem of rotation invariant texture classification. Novel

rotation invariant feature, symmetric dense microblock difference (SDMD), is proposed which captures the information at different orientations and scales. N -fold

symmetry is introduced in the feature design configuration, while retaining the

random structure that provides discriminative power. The symmetry is utilized to achieve a rotation invariance. The SDMD is extracted using an image pyramid and

encoded by the Fisher vector approach resulting in a descriptor which captures

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variations at different resolutions without increasing the dimensionality. The proposed

image representation is combined with the linear SVM classifier. Extensive experiments are conducted on four texture data sets [Brodatz, UMD, UIUC, and

Flickr material data set (FMD)] using standard protocols. The results demonstrate that

our approach outperforms the state of the art in texture classification. The MATLAB code is made available.11

S3I_MAT16_029 - A Novel Image Quality Assessment With Globally and

Locally Consilient Visual Quality Perception Computational models for image quality assessment (IQA) have been developed by

exploring effective features that are consistent with the characteristics of a human

visual system (HVS) for visual quality perception. In this paper, we first reveal that many existing features used in computational IQA methods can hardly characterize

visual quality perception for local image characteristics and various distortion types.

To solve this problem, we propose a new IQA method, called the structural contrast-quality index (SC-QI), by adopting a structural contrast index (SCI), which can well

characterize local and global visual quality perceptions for various image

characteristics with structural-distortion types. In addition to SCI, we devise some other perceptually important features for our SC-QI that can effectively reflect the

characteristics of HVS for contrast sensitivity and chrominance component variation.

Furthermore, we develop a modified SC-QI, called structural contrast distortion

metric (SC-DM), which inherits desirable mathematical properties of valid distance metricability and quasi-convexity. So, it can effectively be used as a distance metric

for image quality optimization problems. Extensive experimental results show that

both SC-QI and SC-DM can very well characterize the HVS's properties of visual quality perception for local image characteristics and various distortion types, which

is a distinctive merit of our methods compared with other IQA methods. As a result,

both SC-QI and SC-DM have better performances with a strong consilience of global and local visual quality perception as well as with much lower computation

complexity, compared with the state-of-the-art IQA methods. The MATLAB source

codes of the proposed SC-QI and SC-DM are publicly available online at

https://sites.google.com/site/sunghobaecv/iqa.

S3I_MAT16_030 - A DCT-based Total JND Profile for Spatio-Temporal and

Foveated Masking Effects In image and video processing fields, DCT-based just noticeable difference (JND)

profiles have effectively been utilized to remove perceptual redundancies in pictures

for compression. In this paper, we solve two problems that are often intrinsic to the conventional DCT-based JND profiles: (i) no foveated masking (FM) JND model has

been incorporated in modeling the DCT-based JND profiles; and (ii) the conventional

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temporal masking (TM) JND models assume that all moving objects in frames can be

well tracked by the eyes and that they are projected on the fovea regions of the eyes, which is not a realistic assumption and may result in poor estimation of JND values

for untracked moving objects (or image regions). To solve these two problems, we

first propose a generalized JND model for joint effects between TM and FM effects. With this model, called the temporal-foveated masking (TFM) JND model, JND

thresholds for any tracked/untracked and moving/still image regions can be

elaborately estimated. Finally, the TFM-JND model is incorporated into a total DCT-

based JND profile with a spatial contrast sensitivity function, luminance masking, and contrast masking JND models. In addition, we propose a JND adjustment method for

our total JND profile to avoid overestimation of JND values for image blocks of fixed

sizes with various image characteristics. To validate the effectiveness of the total JND profile, an experiment involving a subjective distortionvisibility assessment has been

conducted. The experiment results show that the proposed total DCT-based JND

profile yields significant performance improvement with much higher capability of distortion concealment (average 5.6 dB lower PSNR) compared to state-of-the-art

JND profiles. The MATLAB source code of the proposed total DCT-based JND

profile is publicly available online at https://sites.google.com/site/sunghobaecv/jnd

S3I_MAT16_031 - PiCode: a New Picture-Embedding 2D Barcode

Nowadays, 2D barcodes have been widely used as an interface to connect potential

customers and advertisement contents. However, the appearance of a conventional 2D barcode pattern is often too obtrusive for integrating into an aesthetically designed

advertisement. Besides, no human readable information is provided before the

barcode is successfully decoded. This paper proposes a new picture-embedding 2D barcode, called PiCode, which mitigates these two limitations by equipping a

scannable 2D barcode with a picturesque appearance. PiCode is designed with careful

considerations on both the perceptual quality of the embedded image and the decoding robustness of the encoded message. Comparisons with the existing beautified 2D

barcodes show that PiCode achieves one of the best perceptual qualities for the

embedded image, and maintains a better tradeoff between image quality and decoding

robustness in various application conditions. PiCode has been implemented in the MATLAB on a PC and some key building blocks have also been ported to Android

and iOS platforms. Its practicality for real-world applications has been successfully

demonstrated.

S3I_MAT16_032 - OCR Based Feature Extraction and Template Matching

Algorithms for Qatari Number Plate There are several algorithms and methods that could be applied to perform the

character recognition stage of an automatic number plate recognition system;

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however, the constraints of having a high recognition rate and real-time processing

should be taken into consideration. In this paper four algorithms applied to Qatari number plates are presented and compared. The proposed algorithms are based on

feature extraction (vector crossing, zoning, combined zoning and vector crossing) and

template matching techniques. All four proposed algorithms have been implemented and tested using MATLAB. A total of 2790 Qatari binary character images were used

to test the algorithms. Template matching based algorithm showed the highest

recognition rate of 99.5% with an average time of 1.95 ms per character.

S3I_MAT16_033 - HD Qatari ANPR System

Recently, Automatic Number Plate Recognition (ANPR) systems have become

widely used in safety, security, and commercial aspects. The whole ANPR system is based on three main stages: Number Plate Localization (NPL), Character

Segmentation (CS), and Optical Character Recognition (OCR). In recent years, to

provide better recognition rate, High Definition (HD) cameras have started to be used. However, most known techniques for standard definition are not suitable for real-time

HD image processing due to the computationally intensive cost of localizing the

number plate. In this paper, algorithms to implement the three main stages of a high definition ANPR system for Qatari number plates are presented. The algorithms have

been tested using MATLAB and two databases as a proof of concept. Implementation

results have shown that the system is able to process one HD image in 61 ms, with an

accuracy of 98.0% in NPL, 99.75% per character in CS, and 99.5% in OCR.

S3I_MAT16_034 - Template Matching of Aerial Images using GPU

During the last decade, processor architectures have emerged with hundreds and thousands of high speed processing cores in a single chip. These cores can work in

parallel to share a work load for faster execution. This paper presents performance

evaluations on such multicore and many-core devices by mapping a computationally expensive correlation kernel of a template matching process using various

programming models. The work builds a base performance case by a sequential

mapping of the algorithm on an Intel processor. In the second step, the performance of

the algorithm is enhanced by parallel mapping of the kernel on a shared memory multicore machine using OpenMP programming model. Finally, the Normalized

Cross-Correlation (NCC) kernel is scaled to map on a many-core K20 GPU using

CUDA programming model. In all steps, the correctness of the implementation of algorithm is taken care by comparing computed data with reference results from a

high level implementation in MATLAB. The performance results are presented with

various optimization techniques for MATLAB, Sequential, OpenMP and CUDA based implementations. The results show that GPU based implementation achieves

32x and 5x speed-ups respectively to the base case and multicore implementations

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respectively. Moreover, using inter-block sub-sampling on an 8-bit 4000×4000

reference gray-scale image achieves the execution time upto 2.8sec with an error growth less than 20% for the selected templates of size 96×96.

S3I_MAT16_035 - Analysis of Adaptive Filter and ICA for Noise Cancellation from a Video Frame

Noise cancellation algorithms have been frequently applied in many fields including

image/video processing. Adaptive noise cancellation algorithms exploit the

correlation property of noise and remove the noise from the input signal more effectively than non-adaptive algorithms. In this paper different noise cancellation

techniques are applied to de-noise a video frame. Three different variants of gradient

based adaptive filtering algorithms and independent component analysis (ICA) procedure are implemented and compared on the basis of signal to noise ratio (SNR)

and computational time. The common algorithms used in adaptive filters are least

mean square (LMS), normalized least means square (NLMS), and recursive least mean square (RLS). The simulation results demonstrates that NLMS algorithm is

computationally efficient but cannot handle impulsive noise whereas LMS and RLS

can perform better for long duration noise signals. The comparative analysis of adaptive filtering algorithms and ICA shows that ICA can perform better then all three

iterative gradient based algorithms because of its non-iterative nature. For testing and

simulations, three variants of white Gaussian noise (WGN) are used to corrupt the

video frame.

S3I_MAT16_036 - Active Learning Methods for Efficient Hybrid Biophysical

Variable Retrieval Kernel-based machine learning regression algorithms (MLRAs) are potentially

powerful methods for being implemented into operational biophysical variable

retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for

analysis and the possibility of using a large amount of simulated data from radiative

transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques

will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving

optimized biophysical variable estimation with a manageable training data set, and

their implementation into a Matlab-based MLRA toolbox for semiautomatic use. The AL methods were analyzed on their efficiency of improving the estimation accuracy

of the leaf area index and chlorophyll content based on PROSAIL simulations. Each

of the implemented methods outperformed random sampling, improving retrieval accuracy with lower sampling rates. Practically, AL methods open opportunities to

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feed advanced MLRAs with RTM-generated training data for the development of

operational retrieval models.

S3I_MAT16_037 - Development of a Brain-Computer Interface Based on Visual

Stimuli for the Movement of a Robot Joints This paper presents a brain computer interface (BCI) to control a robotic arm by brain

signals from visual stimuli. The following signal processing steps were established;

acquisition of brain signals by electroencephalography (EEG) electrodes; noise

reduction; extraction of signal characteristics and signal classification. Reliable brain signals were obtained by the use of the Emotiv EPOC® commercial hardware. The

OpenViBE® commercial software was used to program the signal processing

algorithms. By using Matlab® together with an Arduino® electronic board, two servo motors were controlled to drive two joints of a 5 degrees-of-freedom robot

commanded by P300-type evoked potential brain signals from visual stimulation

when a subject concentrates on particular images from an image matrix displayed in the computer screen. The experiments were conducted with and without auditive and

visual noise (artifacts) to find out the noise influence in the signal classification

outcome. The obtained experimental results presented an efficiency in the identification stage up to 100% with and without hearing noise conditions. However,

under visual noise conditions a maximum efficiency of 50% was reached. The

experiments for the servomotors control were carried out without noise, reaching an

efficiency of 100% in the identification stage.

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ECE - EMBEDDED IEEE

PROJECTS - 2016-2017

S3I_EMB16_001 - Remote monitoring of photovoltaic systems using embedded system clusters

Remote monitoring of photovoltaic systems is critically important for the users. The

performance of each component existing in these systems should be observable. In this study, a cheap and easily mountable remote monitoring design for low cost

photovoltaic systems located near urban areas is proposed. With this design, it is

aimed to transmit collected information at the remote solar energy station with MPI (Message Passing Interface). A design has been done for a remote monitoring of a

1kW photovoltaic system. With this design, panel and battery voltages, temperature

and humidity can be observed remotely. An embedded system cluster consisting of

single-board computers has been used in the design. This cluster is composed of a center single-board computer and remote node single-board computers as many as the

photovoltaic system count. Collected information is broadcasted over internet using

the single-board computer at the center.

S3I_EMB16_002 - Wearable Noncontact Armband for Mobile ECG Monitoring

System One of the best ways to obtain health information is from an electrocardiogram

(ECG). Through an ECG, characteristics such as patients’ heartbeats, heart conditions,

and heart disease can be analyzed. Unfortunately, most available healthcare devices

do not provide clinical data such as information regarding patients’ heart activities. Many researchers have tried to solve this problem by inventing wearable heart

monitoring systems with a chest strap or wristband, but their performances were not

feasible for practical applications. Thus, the aim of this study is to build a new system to monitor heart activity through ECG signals. The proposed system consists of

capacitive-coupled electrodes embedded in an armband. It is considered to be a

reliable, robust, and low-power-transmission ECG monitoring system. The reliability of this system was achieved by the careful placement of sensors in the armband.

Bluetooth low energy (BLE) was used as the protocol for data transmission; this

protocol was proposed to develop the low-power-transmission system. For robustness,

the proposed system is equipped with analysis capabilities–e.g., real-time heartbeat detection and a filter algorithm to ignore distractions from body movements or noise

from the environment.

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S3I_EMB16_003 - UR-SolarCap: An Open Source Intelligent Auto-Wakeup

Solar Energy Harvesting System for Supercapacitor Based Energy Buffering Energy harvesting systems that couple solar panels with supercapacitor buffers offer

an attractive option for powering computational systems deployed in “field settings,”

where power infrastructure is inaccessible. Supercapacitors offer a particularly compelling advantage over electrochemical batteries for such settings because of their

ability to survive many more charge-discharge cycles. We share UR-SolarCap – a

versatile open source design for such a harvesting system that targets embedded

system applications requiring power in the 1–10 W range. Our system is designed for high efficiency and controllability and, importantly, supports auto-wakeup from a

state of complete energy depletion. This paper summarizes our design methodology,

and the rationale behind our design and configuration decisions. Results from the operation and testing of a system realized with our design demonstrate: (a) an

achievable harvester efficiency of 85%, (b) the ability to maintain sustained operation

over a two week period when the solar panel and buffer are sized appropriately, and (c) a robust auto-wakeup functionality that resumes system operation upon availability

of harvestable energy after a period in which the system has been forced into a

dormant state because of a lack of usable energy. To facilitate the use of the system by researchers exploring embedded system applications in environments that lack a

power infrastructure, our designs are available for download as an archive containing

design schematics, PCB files, firmware code, and a component list for assembly of the

system. Additionally, a limited number of pre-assembled kits are available upon request.

S3I_EMB16_004 - Real-time patient health monitoring and alarming using wireless-sensor-network

The main objective of this research is design and realization of real-time monitoring

and alarming system for patient health, especially for patients suffering from diseases during their normal life. The proposed system has an embedded microcontroller

connected to a set of medical sensors (related to the patient case) and a wireless

communication module (Bluetooth). Each patient is considered as a node in a wireless

sensor network and connected to a central node installed at the medical center through an internet connection. The embedded microcontroller checks if the patient health

status is going well or not by analyzing the scanned medical signals. If the analysis

results are abnormal, the embedded unit uses the patient's phone to transmit these signals directly to the medical center. In this case, the doctor will send medical advice

to the patient to save his/her life. The implemented prototype has been tested and

calibrated with standard devices. The experimental results confirm the effectiveness of the proposed system that is accurate in scanning, clear in monitoring, intelligent in

decision making, reliable in communication, and cheap (about 100 US$).

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S3I_EMB16_005 - Assessment of Robotic Picking Operations Using a 6 Axis Force/Torque Sensor

This letter presents a novel architecture for evaluating the success of picking

operations that are executed by industrial robots. It is formed by a cascade of machine learning algorithms (kNN and SVM) and uses information obtained by a 6 axis

force/torque sensor and, if available, information from the built-in sensors of the

robotic gripper. Beyond measuring the success or failure of the entire operation, this

architecture makes it possible to detect in real-time when an object is slipping during the picking. Therefore, force and torque signatures are collected during the picking

movement of the robot, which is decomposed into five different stages that allows to

characterize distinct levels of success over time. Several trials were performed using an industrial robot with two different grippers for picking a long and flexible object.

The experiments demonstrate the reliability of the proposed approach under different

picking scenarios since, it obtained a testing performance (in terms of accuracy) up to 99.5% of successful identification of the result of the picking operations, considering

an universe of 400 attempts.

S3I_EMB16_006 - Evaluating User Gestures in Rehabilitation from

Electromyographic Signals

One of the strategies being used over the last years to increase the user commitment

and motivation on rehabilitation systems is the use of virtual reality (VR) environments. In addition to contributing to motivation, these systems can simulate

real life activities and provide means to measure and evaluate user performance. The

use of natural interaction devices originally conceived to the game market allowed the development of low-cost and minimally invasive systems. With the advent of

interaction devices based on electromyography, the electromyographic signals of the

user can also be used on the natural interaction process. This work has as goal to verify if, by using a evaluation model, is possible to evaluate user performance in real

time through gesture recognition by means of an electromyography device attached to

a rehabilitation system.

S3I_EMB16_007 - Implementation of ZigBee-VLC system to support light

control network configuration

In this paper, ZigBee-VLC Transmitter and Receiver are designed, implemented and tested. By utilizing the ZigBee-VLC Transmitter and Receiver, commissioning

procedures for light control network configuration are simplified and commissioning

time is drastically reduced. With this configuration, lighting control network configured to use a maximum of 216 lighting is possible. As a result of this research,

the transmitter is complete with ZigBee-VLC features implemented in the Single

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MCU without rising production costs and the 1-board solution receiver including a

ZigBee and VLC functions are implemented. In addition, as a result of the test work using the light control app, dramatically shortening commissioning time, easy lighting

control is possible was confirmed.

S3I_EMB16_008 - Coexistence of ZigBee-Based WBAN and WiFi for Health

Telemonitoring Systems

The development of telemonitoring via wireless body area networks (WBANs) is an

evolving direction in personalized medicine and home-based mobile health. A WBAN consists of small, intelligent medical sensors which collect physiological parameters

such as electrocardiogram, electroencephalography, and blood pressure. The recorded

physiological signals are sent to a coordinator via wireless technologies, and are then transmitted to a healthcare monitoring center. One of the most widely used wireless

technologies in WBANs is ZigBee because it is targeted at applications that require a

low data rate and long battery life. However, ZigBee-based WBANs face severe interference problems in the presence of WiFi networks. This problem is caused by

the fact that most ZigBee channels overlap with WiFi channels, severely affecting the

ability of healthcare monitoring systems to guarantee reliable delivery of physiological signals. To solve this problem, we have developed an algorithm that

controls the load in WiFi networks to guarantee the delay requirement for

physiological signals, especially for emergency messages, in environments with

coexistence of ZigBee-based WBAN and WiFi. Since WiFi applications generate traffic with different delay requirements, we focus only on WiFi traffic that does not

have stringent timing requirements. In this paper, therefore, we propose an adaptive

load control algorithm for ZigBee-based WBAN/WiFi coexistence environments, with the aim of guaranteeing that the delay experienced by ZigBee sensors does not

exceed a maximally tolerable period of time. Simulation results show that our

proposed algorithm guarantees the delay performance of ZigBee-based WBANs by mitigating the effects of WiFi interference in various scenarios.

S3I_EMB16_009 - ZigBee network system for observing operating activities of

work vehicles Observing activities of working vehicles on a work site, such as a factory, is

important in regard to managing the lifetime of vehicles and achieving high

operational availability. However, it is a problem that an administrator cannot completely grasp the activities of a working vehicle. Existing systems cannot cover a

large area, particularly in an indoor environment. A system is proposed for monitoring

operating activities of working vehicles, regardless of whether they are operating indoors or outdoors. The system calculates the activity rate of a vehicle by analyzing

the topology of a network configured by the wireless technology ZigBee. In addition,

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it was experimentally verified that network topology and RSSI can be used to estimate

activities of working vehicles.

S3I_EMB16_010 - The Design of Building Fire Monitoring System Based on

ZigBee-WiFi Networks With the rapid development of wireless communication technology, people's life has

undergone great changes. In recent years, the comfort and safety of the building

environment have become a universal concern. However, building fire is the greatest

threat to building safety. In consideration of the current issues on building security, the design applies the important part, the wireless sensor network technology to

building fire safety monitoring system and establishes the wireless sensor network by

using ZigBee technology and ZigBee-WiFi gateway which transforms ZigBee network into WiFi network, In addition, taking advantage of the ZigBee wireless

sensor network locates a fire place so that the fire information is uploaded to the

handheld terminal and the building security personnel work out the retreat and rescue plan in time. This paper provides a new solution for building fire monitoring system.

S3I_EMB16_011 - A low complex spread spectrum scheme for ZigBee based smart home networks

One of the biggest challenges that consumers and service providers have is

connecting a wide range of consumer electronics in a smart home environment.

Resource planning and bandwidth allocation for these networks in the license free Industrial Scientific Medical (ISM) frequency band can not be guaranteed. In this

paper, we propose improvements for ZigBee physical layer in order to cope with

coexistence issue. A detailed MATLAB/Simulink simulator is developed to achieve our objective. In order to balance the trade-off between multipath effects and receiver

complexity, the spreading gain of the conventional Direct Sequence Spread Spectrum

(DSSS) scheme is limited to 9dB. Unfortunately, this reduces the interference suppression capability of spread spectrum schemes. Here, we propose a low complex

spread spectrum scheme for the ZigBee physical layer. The proposed scheme is

shown to be robust against multipath fading and interference with a low complexity.

S3I_EMB16_012 - Interference-Mitigated ZigBee-Based Advanced Metering

Infrastructure

An interference-mitigated ZigBee-based advanced metering infrastructure (AMI) solution, namely IMM2ZM, has been developed for high-traffics smart metering

(SM). The IMM2ZM incorporates multiradios multichannels network architecture and

features an interference mitigation design by using multiobjective optimization. To evaluate the performance of the network due to interference, the channel-swapping

time (Tcs) has been investigated. Analysis shows that when the sensitivity (PRχ) is

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less than -12 dBm, Tcs increases tremendously. Evaluation shows that there are

significant improvements in the performance of the application-layer transmission rate (σ) and the average delay (D). The improvement figures are σ > ~300% and D > 70%

in a 10-floor building, σ > ~280 % and D > 65% in a 20-floor building, and σ >

~270% and D > 56% in a 30-floor building. Further analysis reveals that IMM2ZM results in typically less than 0.43 s delay for a 30-floor building under interference.

This performance fulfills the latency requirement of less than 0.5 s for SMs in the

USA (Magazine of Department of Energy Communications, USA, 2010). The

IMM2ZM provides a high-traffics interference-mitigated ZigBee AMI solution.

S3I_EMB16_013 - Energy-saving IAQ monitoring ZigBee network using VIKOR

decision making method Indoor Air Quality (IAQ) is an urgent topic nowadays. It is concluded that 90% of

human's life is spent indoor. However, it is commonly known that materials used in

construction or furniture is often detected to release Volatile organic compounds (VOC) which affect IAQ significantly and lead to dizziness, respiratory irritation,

fatigue, asthma and allergic airway disease and even cancer. As a result, IAQ

monitoring system assists of improving IAQ, and wireless sensor network is an efficient method for building up the system network. In this paper, a new ZigBee

network for IAQ monitoring system is designed. A Multi-criteria decision-making

method VIKOR is used to figure out the best parameters of the MAC layer and

CSMA/CA mechanism under this environment. The network designed can achieve 35% improvement of energy saving without affecting the latency and throughput

performance compared with the commonly-used TOPSIS method.

S3I_EMB16_014 - A Mobile ZigBee Module in a Traffic Control System

Time is of the essence when ambulances are utilized to save people's lives, but when

an ambulance needs to pass through a junction, its speed often must be reduced due to traffic. This complicates situations when the patient in the ambulance needs urgent

treatment that can be administered only at a hospital. Due to the unavailability of

advanced medical procedures in an ambulance, there is the possibility for patients to

suffer a loss of life.

S3I_EMB16_015 - Configurable ZigBee-based control system for people with

multiple disabilities in smart homes Nowadays, home appliances manufacturers are increasingly relying on wireless

sensor network and single chip embedded technologies to build smart environment.

Many existing systems are already in the market, however, they were designed without envisioning the need of residents with special needs. This work presents a

framework that enables the integration and control of devices within a smart home

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environment for residents with disabilities. The framework supports the integration of

multiple control devices for different residents with different disabilities. Moreover, the work addresses the safety of the users by providing warnings and notifications in

case of an emergency. A prototype was designed, implemented and tested.

S3I_EMB16_016 - Self-configuration and smart binding control on IoT

applications

The rapid development of wireless communication technology facilitates the

realization of the Internet-of-Things (IoT). Automatic configuration and smart connection system have become relative important issue in accordance with extensive

applications of IoT, and the energy saving concepts. Therefore, this work presents the

integration of ???Automatic Configuration and Wisdom Connection System??? with Wireless Sensor Networks (WSN), IoT and ZigBee technology, to actualize automatic

configuration based on a received signal strength indicator (Received Signal Strength

Indicator, RSSI), lighting auto-configuration area, regional allocation, and sub-areas. The proposed ???Automatic Configuration and Wisdom Connection System???

automatically configures different lightings to the same position within in the range

???3dBm when the RSSI value varies only slightly. The system is configured to the same lighting site within the experimental environment when the sub-area range set

???3dBm. This study presents a significant contribution to new configuration of

objects in Things (Web of Objects), context awareness control, and optimization of

network control platform.

S3I_EMB16_017 - Accurate Wireless Sensor Localization Technique Based on

Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling This paper aims to determine the distance between the mobile sensor node (i.e.,

bicycle) and the anchor node (i.e., coach) in outdoor and indoor environments. Two

approaches were considered to estimate such a distance. The first approach was based on the traditional channel propagation model that used the log-normal shadowing

model (LNSM), while the second approach was based on a proposed hybrid particle

swarm optimization-artificial neural network (PSO-ANN) algorithm to improve the

distance estimation accuracy of the mobile node. The first method estimated the distance according to the LNSM and the measured received signal strength indicator

(RSSI) of the anchor node, which in turn used the ZigBee wireless protocol. The

LNSM parameters were measured based on the RSSI measurements in both outdoor and indoor environments. A feed-forward neural network type and the Levenberg-

Marquardt training algorithm were used to estimate the distance between the mobile

node and the coach. The hybrid PSO-ANN algorithm significantly improved the distance estimation accuracy more than the traditional LNSM method without

additional components. The hybrid PSO-ANN algorithm achieved a mean absolute

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error of 0.022 and 0.208 m for outdoor and indoor environments, respectively. The

effect of anchor node density on localization accuracy was also investigated in the indoor environment.

S3I_EMB16_018 - Design and Evaluation of an Open-Source Wireless Mesh Networking Module for Environmental Monitoring

Wireless mesh networking extends the communication range among cooperating

multiple low-power wireless radio transceivers and is useful for collecting data from

sensors widely distributed over a large area. By integrating an off-the-shelf wireless design, such as the XBee module, development of sensor systems with mesh

networking capability can be accelerated. This study introduces an open-source

wireless mesh network (WMN) module, which integrates the functions of network discovery, automatic routing control, and transmission scheduling. In addition, this

design is open source in order to promote the use of wireless mesh networking for

environmental monitoring applications. Testing of the design and the proposed networking module is reported. The proposed wireless mesh networking module was

evaluated and compared with XBee. The average package delivery ratio and standard

deviation of the proposed WMN module and the XBee are 94.09%, 91.19%, 5.14%, and 10.25%, respectively, in a 20 node experiment. The proposed system was

demonstrated to have the advantages of low-cost combined with high reliability and

performance, and can aid scientists in implementing monitoring applications without

the complications of complex wireless networking issues.

S3I_EMB16_019 - A smart helmet for air quality and hazardous event detection

for the mining industry A smart helmet has been developed that is able to detect of hazardous events in the

mines industry. In the development of helmet, we have considered the three main

types of hazard such as air quality, helmet removal, and collision (miners are struck by an object). The first is the concentration level of the hazardous gases such as CO,

SO2, NO2, and particulate matter. The second hazardous event was classified as a

miner removing the mining helmet off their head. An IR sensor was developed

unsuccessfully but an off-the shelf IR sensor was then used to successfully determine when the helmet is on the miner's head. The third hazardous event is defined as an

event where miners are struck by an object against the head with a force exceeding a

value of 1000 on the HIC (Head Injury Criteria). An accelerometer was used to measure the acceleration of the head and the HIC was calculated in software. The

layout of the visualisation software was completed, however the implementation was

unsuccessful. Tests were successfully done to calibrate the accelerometer. PCB's that were designed and made included a breakout board and a prototype board. A whole

software implementation was done based on Contiki operating system in order to do

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the control of the measuring of sensors and of calculations done with the measured

values. This paper presents the undertaken design detailing solutions to issues raised in previous research.

S3I_EMB16_020 - Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring

Many devices and solutions for remote electrocardiogram (ECG) monitoring have

been proposed in the literature. These solutions typically have a large marginal cost

per added sensor and are not seamlessly integrated with other smart home solutions. Here, we propose an ECG remote monitoring system that is dedicated to non-

technical users in need of long-term health monitoring in residential environments and

is integrated in a broader Internet-of-Things (IoT) infrastructure. Our prototype consists of a complete vertical solution with a series of advantages with respect to the

state of the art, considering both the prototypes with integrated front end and

prototypes realized with off-the-shelf components: 1) ECG prototype sensors with record-low energy per effective number of quantized levels; 2) an architecture

providing low marginal cost per added sensor/user; and 3) the possibility of seamless

integration with other smart home systems through a single IoT infrastructure.

S3I_EMB16_021 - Development of a distributed disaster data and human life

sign probe system

This paper deals with a novel sensor network system designed for gathering disaster information including physical environmental information and potential signals of

survivers. The system consists of numerous sensor probes and a central database

server. The sensor probes organize their own ZigBee network, which is managed by the central database server. The server is connected to the Internet to be able to

provide total disaster information worldwide. In this paper, the authors introduce their

development and show some basic performance test to verify its potential usability.

S3I_EMB16_022 - Characterization of RSS variability for biobot localization

using 802.15.4 Radios

A cyber-physically organized swarm of insect biobots or biological robots can aid first responders in search-and-rescue scenarios after natural disasters or earthquakes

by establishing an under-rubble sensor network. In such a network, the nodes are

represented by the insect biobots equipped with electronic backpacks utilizing a system-on-chip. This application requires effective real-time localization of the

mobile sensor nodes. Radio signal strength (RSS) is a measurement of the received

signal power, and can be used in estimating the distance between two nodes, which then can help localize the biobotic sensor nodes in the future. This paper investigates

RSS variability and its suitability for biobotic localization.

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S3I_EMB16_023 - Evaluation of Ultrasound-Based Sensor to Monitor Respiratory and Nonrespiratory Movement and Timing in Infants

Goal: To describe and validate a noncontacting sensor that used reflected ultrasound

to separately monitor respiratory, nonrespiratory, and caretaker movements of infants. Methods: An in-phase and quadrature (I & Q) detection scheme provided adequate

bandwidth, in conjunction with postdetection filtering, to separate the three types of

movement. The respiratory output was validated by comparing it to the electrical

activity of the diaphragm (Edi) obtained from an infant ventilator in 11 infants. The nonrespiratory movement output was compared to movement detected by miniature

accelerometers attached to the wrists, ankles, and heads of seven additional infants.

Caretaker movement was compared to visual observations annotated in the recordings. Results: The respiratory rate determined by the sensor was equivalent to

that from the Edi signal. The sensor could detect the onset of inspiration significantly

earlier than the Edi signal (23+/-69 ms). Nonrespiratory movement was identified with an agreement of 0.9 with the accelerometers. It potentially interfered with the

respiratory output an average of 4.7+/-4.5% and 14.9+/15% of the time in infants not

requiring or on ventilatory support, respectively. Caretaker movements were identified with 98% sensitivity and specificity. The sensor outputs were independent

of body coverings or position. Conclusion: This single, noncontacting sensor can

independently quantify these three types of movement. Significance: It is feasible to

use the sensor as trigger for synchronizing mechanical ventilators to spontaneous breathing, to quantify overall movement, to determine sleep state, to detect seizures,

and to document the amount and effects of caretaker activity in infants.

S3I_EMB16_024 - Smart real-time healthcare monitoring and tracking system

using GSM/GPS technologies

Health monitoring systems have rapidly evolved recently, and smart systems have been proposed to monitor patient current health conditions, in our proposed and

implemented system, we focus on monitoring the patient's blood pressure, and his

body temperature. Based on last decade statistics of medical records, death rates due

to hypertensive heart disease, shows that the blood pressure is a crucial risk factor for atherosclerosis and ischemic heart diseases; thus, preventive measures should be taken

against high blood pressure which provide the ability to track, trace and save patient's

life at appropriate time is an essential need for mankind. Nowadays, Globalization demands Smart cities, which involves many attributes and services, such as

government services, Intelligent Transportation Systems (ITS), energy, health care,

water and waste. This paper proposes a system architecture for smart healthcare based on GSM and GPS technologies. The objective of this work is providing an effective

application for Real Time Health Monitoring and Tracking. The system will track,

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trace, monitor patients and facilitate taking care of their health; so efficient medical

services could be provided at appropriate time. By Using specific sensors, the data will be captured and compared with a configurable threshold via microcontroller

which is defined by a specialized doctor who follows the patient; in any case of

emergency a short message service (SMS) will be sent to the Doctor's mobile number along with the measured values through GSM module. furthermore, the GPS provides

the position information of the monitored person who is under surveillance all the

time. Moreover, the paper demonstrates the feasibility of realizing a complete end-to-

end smart health system responding to the real health system design requirements by taking in consideration wider vital human health parameters such as respiration rate,

nerves signs ... etc. The system will be able to bridge the gap between pat- ents - in

dramatic health change occasions- and health entities who response and take actions in real time fashion.

S3I_EMB16_025 - Indoor Blind Localization of Smartphones by Means of Sensor Data Fusion

Locating the nodes in wireless sensor networks (WSNs) is currently a very active

area of research due to their increasing number of potential applications. Wireless networks composed of smartphones have gained particular interest, mainly due to the

high availability of such devices. This paper presents a novel algorithm for blind

localization of commercial off-the-shelf smartphones in a WSN. The algorithm uses

acoustic signals and inertial sensors to estimate the sensor positions simultaneously. Estimates of range and direction-of-arrival (DOA) locally obtained in each node are

combined with a maximum likelihood estimator. A tailored optimization algorithm is

also proposed to solve the DOA uncertainty problem. Our proposal obtains low localization errors without considering any reference node nor any prior

synchronization between nodes.

S3I_EMB16_026 - Low-Overhead and High-Precision Prediction Model for

Content-Based Sensor Search in the Internet of Things

A growing number of Internet-connected sensors have already promoted the advance

of sensor search service. Accessing all available objects to find the sought sensor results in huge communication overhead, thus a low-overhead and high-precision

prediction model (LHPM) is proposed to improve the sensor search efficiency. We

design the approximation method to lower the reporting energy cost. Then a multistep prediction method is proposed to accurately estimate the sensor state. Furthermore, a

sensor ranking method is presented to assess the matching probabilities of sensors, so

as to effectively reduce the communication overhead of the search process. Simulation results demonstrate the validity of the proposed prediction model in the area of

content-based sensor search.

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S3I_EMB16_027 - Preprocessing Design in Pyroelectric Infrared Sensor-Based Human-Tracking System: On Sensor Selection and Calibration

This paper presents an information-gain-based sensor selection approach as well as a

sensor sensing probability model-based calibration process for multihuman tracking in distributed binary pyroelectric infrared sensor networks. This research includes three

contributions: 1) choose the subset of sensors that can maximize the mutual

information between sensors and targets; 2) find the sensor sensing probability model

to represent the sensing space for sensor calibration; and 3) provide a factor graph-based message passing scheme for distributed tracking. Our approach can find the

solution for sensor selection to optimize the performance of tracking. The sensing

probability model is efficiently optimized through the calibration process in order to update the parameters of sensor positions and rotations. An application for mobile

calibration and tracking is developed. Simulation and experimental results are

provided to validate the proposed framework.

S3I_EMB16_028 - Lightweight Mashup Middleware for Coal Mine Safety

Monitoring and Control Automation Recently, the frequent coal mine safety accidents have caused serious casualties and

huge economic losses. It is urgent for the global mining industry to increase

operational efficiency and improve overall mining safety. This paper proposes a

lightweight mashup middleware to achieve remote monitoring and control automation of underground physical sensor devices. First, the cluster tree based on ZigBee

Wireless Sensor Network (WSN) is deployed in an underground coal mine, and

propose an Open Service Gateway initiative (OSGi)-based uniform devices access framework. Then, propose a uniform message space and data distribution model, and

also, a lightweight services mashup approach is implemented. With the help of

visualization technology, the graphical user interface of different underground physical sensor devices could be created, which allows the sensors to combine with

other resources easily. Besides, four types of coal mine safety monitoring and control

automation scenarios are illustrated, and the performance has also been measured and

analyzed. It has been proved that our lightweight mashup middleware can reduce the costs efficiently to create coal mine safety monitoring and control automation

applications.

S3I_EMB16_029 - Improving the Locating Precision of an Active WIFI RFID

System to Obtain Traceability of Patients in a Hospital

It is a challenge to integrate RFID technology into the healthcare sector to increase security by obtaining traceability of patients during their hospital stay. In this case,

RFID provides arange of technical architectures for implementing an RFID system.

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The installation or use of the WIFI network available in a hospital is a possible

element in system design since a priori with a correct configuration of RFID components, excellent results in location accuracy can be obtained over other

architectures available in the market. The accuracy of RFID Aeroscout WIFI system

can be improved with the installation of exciters. These are components that assist the localisation engine in calculating the location of an active RFID tag WIFI. The

precision offered by the localisation engine depends on multiple configurable

parameters set by the engineers responsible for the design and development of an

active RFID WIFI system.

S3I_EMB16_030 - Joint access point and user localization using unlabeled WiFi

RSS data This paper investigates the problem of joint estimation of a pedestrian user path and

the available WiFi access point locations. The observations are limited to unlabeled

WiFi received signal strength (RSS) values. The problem is formed as a partially observable Markov decision process and RSS gradients are integrated to estimate and

update the user locations along the path. The RSS data is modeled as a Gaussian

process and gradient vectors are updated for each step based on the motion dynamics. Realistic assumptions and constraints are introduced to model the user's movement

and reduce the computational complexity.

S3I_EMB16_031 - Water Level Meter for Alerting Population about Floods The most important thing immediately before, during and after a disaster occurs is the

dissemination of information, a deployment of devices enabled by IoT (Internet of

Things) could bring benefits in terms of giving to people information opportunely for making decisions in face of this disaster. In this paper, we present a sensor to measure

water level in rivers, lakes, lagoons and streams. For such purpose and to prove our

concept, we designed a pilot project through a micro-model that is constructed with a water level measurement sensor based on a simple open circuit that closes when in

contact with water and experimentally tested into a water container under a controlled

environment. This micro-model is performed on the basis of a programmable

electronic board (Netduino Plus 2), an electronic circuit connected to electrical resistances that are located at a specific height, within a water container, when the

water level rises and reaches the resistors, varies the impedance, this shows the actual

water level and so on for different heights. The information from water level sensor is transmitted via WiFi to a laptop, then this information is also seen in smartphones,

where users can see the water level in rivers. Finally, the micro-model is tested by

experimental tests under a controlled environment and satisfactory results are obtained.

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S3I_EMB16_032 - Brain-controlled devices: the perception-action closed loop

Future neuroprosthetics will be tightly coupled with the user in such a way that the resulting system can replace and restore impaired upper limb functions because

controlled by the same neural signals than their natural counterparts. However, robust

and natural interaction of subjects with sophisticated prostheses over long periods of time remains a major challenge. To tackle this challenge we can get inspiration from

natural motor control, where goal-directed behavior is dynamically modulated by

perceptual feedback resulting from executed actions. Current brain-computer

interfaces (BCI) partly emulate human motor control as they decode cortical correlates of movement parameters -from onset of a movement to directions to

instantaneous velocity- in order to generate the sequence of movements for the

neuroprosthesis. A closer look, though, shows that motor control results from the combined activity of the cerebral cortex, subcortical areas and spinal cord. This

hierarchical organization supports the hypothesis that complex behaviours can be

controlled using the low-dimensional output of a BCI in conjunction with intelligent devices in charge to perform low-level commands. A further component that will

facilitate intuitive and natural control of motor neuroprosthetics is the incorporation of

rich multimodal feedback and neural correlates of perceptual cognitive processes resulting from this feedback. As in natural motor control, these sources of information

can dynamically modulate interaction.

S3I_EMB16_033 - Experimental investigation of remote control via Android smart phone of arduino-based automated irrigation system using moisture

sensor

Climate change because of the greenhouse effect has been authenticated. Fallouts like the 2015 Chennai floods suggest techniques like precision agriculture that

includes automation in the irrigation system are important. This paper suggests an

economical and easy-to-use arduino-based automated irrigation system that utilizes the Android smart phone for remote control. The system design includes a soil

moisture sensor that provides a voltage signal proportional to the moisture content in

the soil which is compared with a predetermined threshold value obtained by

sampling of various soils and specific crops. The outcome of the comparison is that appropriate data are fed to the arduino uno processor. The arduino is linked wirelessly

via the HC-05 module to an Android smart phone. The data received by the Android

smart phone from the arduino is displayed on the User Interface (UI) (S2 terminal application). The UI in the Android smart phone allows the user easy remote control

of the irrigation drive system that involves switching, on and off, of the drive motor

by the arduino, wired to its controller, based on commands from the android smart phone. Studies conducted on a laboratory prototype suggest that the design is viable

and can be easily adopted for real time application.

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S3I_EMB16_034 - MAGIC: Model-Based Actuation for Ground Irrigation Control

Lawns make up the largest irrigated crop by surface area in North America, and

carries with it a demand for over 9 billion gallons of freshwater each day. Despite recent developments in irrigation control and sprinkler technology, state-of-the-art

irrigation systems do nothing to compensate for areas of turf with heterogeneous

water needs. In this work, we overcome the physical limitations of the traditional

irrigation system with the development of a sprinkler node that can sense the local soil moisture, communicate wirelessly, and actuate its own sprinkler based on a centrally-

computed schedule. A model is then developed to compute moisture movement from

runoff, absorption, and diffusion. Integrated with an optimization framework, optimal valve scheduling can be found for each node in the space. In a turf area covering over

10,000ft2, two separate deployments spanning a total of 7 weeks show that MAGIC

can reduce water consumption by 23.4% over traditional campus scheduling, and by 12.3% over state-of-the- art evapotranspiration systems, while substantially improving

conditions for plant health. In addition to environmental, social, and health benefits,

MAGIC is shown to return its investment in 16-18 months based on water consumption alone.

S3I_EMB16_035 - Potential for improving green roof performance through

artificial irrigation Historically extensive green roofs were designed for natural precipitation with a

plant selection focusing on hardy succulents such as sedums that can survive harsh,

water stressed conditions. Although this seems a convenient solution to establish and maintain a green roof system, at a much broader level this does not optimize the

functions and performance of the green roof. In this paper the influence of irrigation

on green roof functions and performance is presented for an extensive green roof by an extensive literature study. Green roof energy saving potential under Sri Lankan

climatic conditions is significant. The average water retention of green roof substrate

under different climatic zone conditions in Sri Lankan context is simulated with

hypothetical twelve extensive green roof types. Results justify the artificial irrigation requirement and provide key directions to develop water balance model considering

locational factors to maintain set soil moisture target.

S3I_EMB16_036 - Dual Sink Efficient Balanced Energy Technique for

Underwater Acoustic Sensor Networks

Underwater Acoustic Sensor Networks are considered to provide efficient monitoring tasks in aquatic environment but due to limited battery resource of sensor

nodes, network lifetime collapses. Energy balancing is the major issue in low network

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lifetime. High energy consumption creates energy holes and ultimately leads to

shorter network lifetime. Therefore, energy consumption must be balanced to increase network life time. To overcome these concerns a technique should be designed that

minimizes the energy consumption and prolong network lifetime. This paper presents

a Dual Sink Efficient and Balanced Energy consumption Technique (DSEBET) for UASNs. DSEBET overcomes the problem of limited network lifetime and high

energy consumption over long distance. Dual sinks underwater model is established.

DSEBET first establishes links between nodes on the basis of their optimum distance

value and then picks relay nodes on the basis of their minimum distance "Nj" value for the transmission of data. In the data transmission phase every nodes have equal

energy levels numbers (ELNs). Long distance nodes from one sink will share their

data to other sink if come in range of sink otherwise they will establish a multi hop path for transmission of data to the respective sink.

IOT BASED IEEE PROJECTS- 2016-2017

ECE – EMBEDDED DEPARTMENT S3I_IOT16_001 - IoT Healthcare Analytics: The Importance of Anomaly Detection Healthcare data is quite rich and often contains human survival related information.

Analyzing healthcare data is of prime importance particularly considering the immense potential of saving human life and improving quality of life. Furthermore, IoT revolution has redefined

modern health care systems and management. IoT offers its greatest promise to deliver excellent progress in healthcare domain. In this talk, proactive healthcare analytics specifically for cardiac disease prevention will be discussed. Anomaly detection plays a prominent role in healthcare

analytics. In fact, the anomalous events are to be accurately detected with low false negative alarms often under high noise (low SNR) condition. An exemplary case of smartphone based

cardiac anomaly detection will be presented..

S3I_IOT16_002 - Effective ways to use Internet of Things in the field of medical and smart health care

The recent advancements in technology and the availability of the Internet make

it possible to connect various devices that can communicate with each other and share data. The Internet of Things (IoT) is a new concept that allows users to connect

various sensors and smart devices to collect real-time data from the environment.

However, it has been observed that a comprehensive platform is still missing in the e-

Health and m-Health architectures to use smartphone sensors to sense and transmit important data related to a patient's health. In this paper, our contribution is twofold.

Firstly, we critically evaluate the existing literature, which discusses the effective

ways to deploy IoT in the field of medical and smart health care. Secondly, we propose a new semantic model for patients' e-Health. The proposed model named as

`k-Healthcare' makes use of 4 layers; the sensor layer, the network layer, the Internet

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layer and the services layer. All layers cooperate with each other effectively and

efficiently to provide a platform for accessing patients' health data using smart phones.

S3I_IOT16_003 - A conceptual framework for IoT-based healthcare system using cloud computing

Internet of Things (IoT) envisions a future in which anything/anyone/anyservice

can be linked by means of appropriate information and communication technologies

which will bring technological revolution in the fields of domestics, smart homes, healthcare systems, goods monitoring and logistics. This paper presents the

applications of IoT and addresses some essential parameters and characteristics of

each of the applications of IoT. In this paper, we have deeply explored the role of IoT in healthcare delivery and its technological aspects that make it a reality and examine

the opportunities. A cloud based conceptual framework has been proposed which will

be beneficial to the healthcare industry implementing IoT healthcare solutions.

S3I_IOT16_004 - The Internet of Things in Healthcare: Potential Applications

and Challenges Exciting new applications of Internet of Things (IoT) technology are arising,

particularly in healthcare, where the leveraging effects can significantly improve

patients' well-being while alleviating the problem of scarce resources. But the hype

around these applications far outpaces the reality. Furthermore, there is a real risk that these leveraging technologies will disassociate caregivers from patients, potentially

resulting in a loss of caring. In this article, the authors review some of the most

promising applications for IoT in healthcare and the significant challenges ahead.

S3I_IOT16_005 - IoT based smart healthcare kit

The paper presents the design and implementation of an IOT-based health monitoring system for emergency medical services which can demonstrate collection,

integration, and interoperation of IoT data flexibly which can provide support to

emergency medical services like Intensive Care Units (ICU), using a INTEL

GALILEO 2ND generation development board. The proposed model enables users to improve health related risks and reduce healthcare costs by collecting, recording,

analyzing and sharing large data streams in real time and efficiently. The idea of this

project came so to reduce the headache of patient to visit to doctor every time he need to check his blood pressure, heart beat rate, temperature etc. With the help of this

proposal the time of both patients and doctors are saved and doctors can also help in

emergency scenario as much as possible. The proposed outcome of the project is to give proper and efficient medical services to patients by connecting and collecting

data information through health status monitors which would include patient's heart

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rate, blood pressure and ECG and sends an emergency alert to patient's doctor with his

current status and full medical information.

Electrical / Power Electronics / Power

Systems / Power Factor Correction -

Project Titles 2016-2017

S3I_PED16_001 - A Compact Coupled Inductor for Interleaved Multiphase DC-

DC Converters A compact coupled inductor structure is proposed for interleaved multiphase

synchronous buck converters used to power computer processors and memories that

require high current and fast current flew rate. The new coupled inductor structure reduces the winding resistor power loss and makes it possible to utilize Ferrite

magnetic material with low core loss. In the letter, several proposed converter

implementations to achieve inverse inductor coupling are illustrated, and inductance

and coupling coefficient variations are studied through a simplified reluctance model and Maxwell magnetic simulation. The inductor structure not only can be extended to

multiple phases but also can be simplified to single phase. The operation of the

inductor is experimentally verified in a two-phase synchronous buck converter at switching frequency of one Mega-Hertz.

S3I_PED16_002 - A Multi-Level Converter with a Floating Bridge for Open-Ended Winding Motor Drive Applications

This paper presents a dual three phase open end winding induction motor

drive. The drive consists of a three phase induction machine with open stator phase

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windings and dual bridge inverter supplied from a single DC voltage source. To

achieve multi-level output voltage waveforms a floating capacitor bank is used for the second of the dual bridges. The capacitor voltage is regulated using redundant

switching states at half of the main dc link voltage. This particular voltage ratio (2:1)

is used to create a multi-level output voltage waveform with three levels. A modified modulation scheme is used to improve the waveform quality of this dual inverter. This

paper also compares the losses in dual inverter system in contrast with single sided

three-level NPC converter. Finally, detailed simulation and experimental results are

presented for the motor drive operating as an open loop v/f controlled motor drive and as a closed loop field oriented motor controller.

S3I_PED16_003 - Adapted NSPWM for Single DC-Link Dual-Inverter Fed

Open-End Motor with Negligible Low-Order Harmonics and Efficiency Enhancement

In this paper, the Near-State PWM (NSPWM), adapted to be implemented in

dual-VSI fed open-end motor, is proposed with the aim of mitigating low-order harmonics (which lead to current THD minimization). Two proposed methods are

studied in detail such as: (a) fixing Phase Angle Displacement (PAD) between two

Voltage Source Inverters (VSIs) to 120° while adjusting Modulation Index (MI); and

(b) fixing MI to the pre-determined value (wherein low-order harmonics are highly mitigated) while adjusting PAD. Furthermore, the proposed approaches enhance

efficiency by limiting the number of commutations within switching interval. The

paper also presents the mathematical approaches to accurately determine low-order harmonic components and switching losses for dual-VSI structure. The experimental

setup, including dual-VSI and open-end induction motor, is assembled in the

laboratory to evaluate performance of the proposed method. Finally, the simulation results, carried out in the MATLAB/Simulink environment, are found to be in a close

agreement with experimental data.

S3I_PED16_004 - Advanced Design and Operation Consideration for Close-connected Winding Permanent Magnet Brushless DC Machine

Advanced design and operation consideration for the close-connected winding

permanent-magnet (PM) brushless DC machine [1] is proposed in this paper, including circulating current elimination, fault tolerant control for the breakdown of

power electronic switch, and sensorless control method. With the finite-element

analysis (FEA), the influence of machine design parameters on circulating current are investigated. By properly controlling the status of power electronic switches, the coils

being connected into the equivalent circuit can be changed to avoid the failure switch.

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The sensorless control is explored by detecting the zero-crossing point of

electromotive force (EMF).

S3I_PED16_005 - An Active Cross-Connected Modular Multilevel Converter

(AC-MMC) for Medium-Voltage Motor Drive This paper presents an active cross-connected modular multilevel converter

(AC-MMC) based on series-connected half-bridge modules. It is intended for

completely enhancing the performance of a medium-voltage motor drive system in the

full speed range from standstill to rated speed under all load conditions. The proposed AC-MMC circuit is characterized by the cross connection of upper and lower arm

middle taps through a branch of series-connected half-bridge converters, which have

an identical voltage and current rating with the sub-modules in upper and lower arms. This cross-connected branch provides a physical power transfer channel for upper and

lower arms. By properly controlling the amount of high-frequency current flowing

through the cross-connected branch, the power balance between the upper and lower arms is achieved even at a zero/low motor speed under constant torque condition.

Meanwhile, no common-mode voltage is introduced in the whole speed range. A

control strategy with focus on submodule capacitor voltage control is also proposed in this paper to guarantee the normal converter operation. Simulation results obtained

from a 4160-V 1-MW model verify the feasibility of the proposal. Experiments on a

downscaled prototype also confirm the validity of the novel circuit and the associated

control strategy.

S3I_PED16_006 - Adaptive Maximum Power Point Tracking Control Algorithm

for Wind Energy Conversion Systems This paper presents an adaptive maximum power point tracking (MPPT)

algorithm for small-scale wind energy conversion systems (WECSs) to harvest more

energy from turbulent wind. The proposed algorithm combines the computational behavior of hill climb search, tip speed ratio, and power signal feedback control

algorithms for its adaptability over wide range of WECSs and fast tracking of

maximum power point. In this paper, the proposed MPPT algorithm is implemented

by using buck– boost featured single-ended primary inductor converter to extract maximum power from full range of wind velocity profile. Evaluation of the proposed

algorithm is done on a laboratory-scaled dc motor drive-based WECS emulator.

TMS320F28335, 32-bit floating point digital signal controller, is used to execute the control schemes of the in-lab experimental setup. Experimental results show that

tracking capability of the proposed algorithm under sudden and gradual fluctuating

wind conditions is efficient and effective.

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S3I_PED16_007 - A Quasi-Z-source Integrated Multi-port Power Converter with

Reduced Capacitance for Switched Reluctance Motor Drives This paper presents a quasi Z-source integrated multiport converter (ZIMPC)

for switched reluctance motor (SRM) drives to reduce the dc link capacitance. In

conventional SRM drives, employing multi-phase asymmetrical H-bridge (ASHB) topology, large capacitors are necessary to absorb the transient energy during phase

current commutation. However, electrolytic capacitors would affect the lifetime, cost

and power density of the drive system. With switch multiplexing technique, a Z-

source integrated multiport power converter is derived to achieve power ripple reduction using relatively small capacitance. Corresponding control method is

designed and developed for the proposed SRM drive. Also, the ZIMPC can boost the

equivalent phase exciting voltage and widen the constant power speed range (CPSR). At last, simulation and experimental results verify the feasibility of the proposed

ZIMPC and its superior performance with smaller capacitance as compared with

ASHB.

S3I_PED16_008 - A Novel Technique for Two-Phase BLDC Motor to Avoid the

Demagnetization Conventional permanent magnet motors operate in both magnetizing (pull)

process and reversible demagnetizing (push) process on the recoil line of magnets.

Therefore, thin surface permanent magnets may easily undergo a risk of

demagnetization at the push process under certain fault conditions, which leads to deterioration of motor performance. Thus, thick magnets, whereas contributing the

significantly high cost, are usually used to minimize this risk in the permanent magnet

motors. In this paper, a novel operation technique, that involves only the pull process, has been proposed for a unique design of two-phase brushless DC (BLDC) motor to

avoid the irreversible demagnetization of the magnets. The motor, operated only in the

pull process, is kept away from the push process of the operation. Therefore, the motor sustains its initial magnetic operating point above the knee point during the

normal operation as well as under the short circuit fault conditions. Finite element

analysis is performed to validate the concept of the proposed technique.

S3I_PED16_009 - An Improved Model Predictive Control Scheme for the PWM

Rectifier-Inverter System Based on Power-Balancing Mechanism

The DC-link voltage fluctuation caused by the change of working state of the load motor has been one of the key issues in the PWM rectifier-inverter system. In

this study, an improved model predictive control (MPC) scheme is proposed to

address this problem. The MPC is applied to both the rectifier stage and the inverter stage in the system. Direct power control is used in the rectifier stage and the direct

torque control is employed in the inverter stage, with the key novelty of the active

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power reference values being defined by both real-time and periodic compensation

power based on the system-level power balance model. Meanwhile, a MPC algorithm based on a two-step prediction is introduced to compensate for the delay of a digital

controller. Comparison has been conducted between the proposed scheme and three

other methods. Simulation and experimental results show that the proposed control scheme exhibits good performance in both the rectifier stage and the inverter stage

with improved dynamic response and suppressed voltage fluctuation of the DC-link

voltage.

S3I_PED16_0010 - Dual Inverter Fed Pole-Phase Modulated Nine-Phase

Induction Motor Drive with Improved Performance

Typical value of rated phase voltage of pole phase modulated multiphase induction motor (PPMMIM) drives with wider speed range is in the order of few

hundreds of kilo volts. This high value of phase voltage for high power density

applications results in higher dc link voltage requirement and switch voltage rating of twolevel multiphase inverter. Further, using multiphase space vector pulse width

modulation (SVPWM) yields less dc link utilization. Methods to increase dc link

utilization using SVPWM with offset value of third harmonic order introduces dominant lower order harmonic currents into phase windings. One more major

problem in high pole mode of PPMMIMs is higher torque pulsation due to decrease in

phase number. To address these problems this paper proposes a dual inverter based

multilevel voltage excitation scheme for nine-phase PPMMIM with 1:3 speed ratio. In four-pole mode a simple phase grouping technique to eliminate lower order harmonic

currents in the phase windings is proposed. In addition each inverter feeding these

phase groups is modulated using carrier based three-phase SVPWM to achieve higher dc link utilization. This paper also proposes a multilevel voltage generation scheme

for twelve-pole mode of operation using carrier phase shifted PWM for inherently

available equal voltage profile coils (EVPCs) with the same dual inverter structure. The torque ripple using phase shifted carriers PWM and single carrier PWM are

compared. Finite element method (FEM) model of ninephase PPMMIM is developed

in Ansys Maxwell twodimension (2-D) and is co-simulated with three threephase dual

inverters in Simplorer environment. Experimental validation is done for linear and over modulation case on 9ФIM fed from three three-phase dual inverters controlled

using Spartan 6 field programmable gate array (FPGA) board programed in VHDL.

S3I_PED16_0011 - Extremely Sparse Parallel AC-Link Universal Power

Converters

Parallel ac-link universal power converters are a relatively new class of power converters that can be configured as dc-dc, dc-ac, ac-dc, and ac-ac. These converters

are extensions of a buck-boost converter in which the current of the inductor is

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alternating and the input and output can have any number of phases with any forms,

voltage amplitude, or frequency. By placing a small capacitor in parallel with the link inductor all the switches can benefit from soft switching. The main limitation of the

parallel ac-link universal power converter is its large number of switches. A three-

phase ac-ac configuration requires 24 unidirectional switches. This paper proposes two topologies based on the parallel ac-link universal power converters that

significantly reduce the number of switches. One of the proposed topologies reduces

the number of switches in a three-phase ac-ac configuration to 16. The other topology

reduces the number of switches to 10. The latter can offer galvanic isolation with only a single-phase high frequency transformer. This paper presents the principles of the

operation of the proposed topologies and evaluates them through simulation and

experiments.

S3I_PED16_0012 - Energy Consumption of Geared DC Motors in Dynamic

Applications: Comparing Modeling Approaches In recent years, many works have appeared which present novel mechanical

designs, control strategies or trajectory planning algorithms for improved energy

efficiency. The actuator model is an essential part of these works, since the optimization of energy consumption strongly depends of the accuracy of this model.

Nevertheless, various authors follow very different approaches, often neglecting

speed- and load-dependent losses and inertias of components such as the motor and

the gearbox. Furthermore, there is no consensus on how negative power affects power consumption. Some authors calculate energy consumption by integrating the electrical

power entirely, by integrating its absolute value, or by integrating only positive

power. This paper assesses how well commonly used models succeed in predicting the energy consumption of an 80 W geared DC motor performing a dynamic task, by

comparing the results they produce to experimental baseline measurements.

S3I_PED16_0013 - Derivation of Dual-Switch Step-Down DC/DC Converters

with Fault-Tolerant Capability

This letter presents a graph theoretic approach to deriving a family of dual-

switch step-down dc/dc converters with fault-tolerant capability. The constraints set in the derivation process ensure minimum additional component is used to achieve fault-

tolerant operation. The operation of converters derived is flexible. Under normal

operating conditions, one of the two switches can serve as a main switch to control the power flow (i.e. single-switch converter operation) and the other switch is in stand-by

mode. When a fault occurs on the main switch, the other switch will be activated to

provide an alternate current path to continue converter operation and maintain output regulation. The fault-tolerant converters are derived by integrating a buck converter

with a buck-boost converter. They share all the components except for the power

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switches. Due to different duty cycles required between the two operating conditions,

a feedback controller is necessary to adjust the duty cycle for tight output regulation. The derivation procedure and experimental results on fault occurence are reported.

The converter derivation approach is able to identify reported topologies and can be

extended to synthesize other topologies with fault-tolerent capability.

S3I_PED16_0014 - Analysis of the Integrated SEPIC-Flyback Converter as a

Single-Stage Single-Switch Power-Factor-Correction LED Driver

This paper proposes a new isolated single-stage single-switch power-factor-correction (S4 PFC) driver for supplying light emitting diodes (LEDs) without

electrolytic capacitor. In the proposed LED driver, the switch turns on under zero

current switching (ZCS) condition. Also, it turns on at a voltage less than its nominal voltage stress and, therefore, the switch capacitive turn-on loss decreases too much

extent. The leakage energy is absorbed, so there are no voltage spikes across the

switch when the switch turns off. In this paper, operating principles of the proposed driver are discussed and design considerations are presented. Also, a laboratory

prototype for supplying a 21W/30V LED module from 220Vrms/50Hz AC mains is

implemented and experimental results are presented to verify the theoretical analysis.

S3I_PED16_0015 - Design and Implementation of a Novel Multilevel DC-AC

Inverter

In this paper, a novel multilevel DC-AC inverter is proposed. The proposed multilevel inverter generates seven levels AC output voltage with the appropriate gate

signals design. Also, the low pass filter is used to reduce the total harmonic distortion

of the sinusoidal output voltage. The switching losses and the voltage stress of power devices can be reduced in the proposed multi-level inverter. The operating principles

of the proposed inverter and the voltage balancing method of input capacitors are

discussed. Finally, a laboratory prototype multilevel inverter with 400 V input voltage and output 220 Vrms /2 kW is implemented. The multilevel inverter is controlled with

sinusoidal pulse-width modulation (SPWM) by TMS320LF2407 digital signal

processor (DSP). Experimental results show that the maximum efficiency is 96.9%

and the full load efficiency is 94.6%.

S3I_PED16_0016 - BLDC Motor Driven Solar PV Array Fed Water Pumping

System Employing Zeta Converter This paper proposes a simple, cost effective and efficient brushless DC

(BLDC) motor drive for solar photovoltaic (SPV) array fed water pumping system. A

zeta converter is utilized in order to extract the maximum available power from the SPV array. The proposed control algorithm eliminates phase current sensors and

adapts a fundamental frequency switching of the voltage source inverter (VSI), thus

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avoiding the power losses due to high frequency switching. No additional control or

circuitry is used for speed control of the BLDC motor. The speed is controlled through a variable DC link voltage of VSI. An appropriate control of zeta converter

through the incremental conductance maximum power point tracking (INC-MPPT)

algorithm offers soft starting of the BLDC motor. The proposed water pumping system is designed and modeled such that the performance is not affected under

dynamic conditions. The suitability of proposed system at practical operating

conditions is demonstrated through simulation results using MATLAB/ Simulink

followed by an experimental validation.

S3I_PED16_0017 - Fault-tolerant Inverter for High-speed 3-Phase BLDC Drives

in Aerospace Applications The fault tolerant control of BLDC motor is of great importance for its

continuous operating capacity even under the faulty situation. Our proposed work

consists of a fault tolerant topology composed of an additional phase leg which contains 6 Triacs act as fault Switching Circuit and a fault protective circuit for the

high-speed low-inductance BLDC motor. This fault protective circuit consists of

Mosfet gate that is driven by using PWM technique. Input DC voltage is given to Buck Boost converter and it working is if the voltage level less than the required

voltage level converter act as boost converter and produce required output. If the Input

voltage level is more converters reduces the voltage obtained from the converter.

Buck Boost converter having switches (Mosfet) which used to isolate fault switches. The output obtained from fault protector circuit is inverted into 3-Phase DC Output.

This 3-Phase Bridge consists of 6 Mosfet which is driven by Digital Pulses. Based on

this pulses 3-Phase motor rotation happens either clockwise or anticlockwise direction. Speed of 3-Phase motor is controlled by Pulse Width Modulation and motor

rotation varies based on percentage of duty cycle that we are giving to it. The method

can achieve safe isolation and reconfiguration to avoid the secondary fault caused by direct switch of the redundant switch and the faulty switch after the fault diagnosis

process.

S3I_PED16_0018 - Integrated DC-DC Converter Design for Electric Vehicle Powertrains

In this paper, an integrated, reconfigurable DC-DC converter for plugin and

hybrid Electric Vehicles (EV) is proposed. The converter integrates functionality for both EV powertrain and charging operation into a single unit. During charging, the

proposed converter functions as a DAB converter, providing galvanic isolation. For

powertrain operation, the converter functions as an interleaved boost converter. During light load powertrain operation, the efficiency of the converter can be further

improved by employing the integrated DAB. The proposed integrated converter does

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not require any extra relays or contactors for charging and powertrain operation. By

using such integration, the overall volume and weight of the power electronics circuits, passives and associated cooling system can be improved. In addition, the

power flow efficiency from EV battery to the high voltage DC bus for the motor

inverter can be improved. The experimental results of the prototype are presented to verify the functionality of the proposed converter.

S3I_PED16_0019 - High Step-Up/Step-Down Soft-Switching Bidirectional DC-

DC Converter with Coupled-Inductor and Voltage Matching Control for Energy Storage Systems

A soft-switching bidirectional DC-DC converter (BDC) with a coupled-

inductor and a voltage doubler cell is proposed for high step-up/step-down voltage conversion applications. A dual-active half-bridge (DAHB) converter is integrated

into a conventional buck-boost BDC to extend the voltage gain dramatically and

decrease switch voltage stresses effectively. The coupled inductor operates not only as a filter inductor of the buck-boost BDC, but also as a transformer of the DAHB

converter. The input voltage of the DAHB converter is shared with the output of the

buck-boost BDC. So PWM control can be adopted to the buck-boost BDC to ensure that the voltage on the two sides of the DAHB converter is always matched. As a

result, the circulating current and conduction losses can be lowered to improve

efficiency. Phase-shift control is adopted to the DAHB converter to regulate the

power flows of the proposed BDC. Moreover, zero-voltage-switching is achieved for all of the active switches to reduce the switching losses. The operational principles

and characteristics of the proposed BDC are presented in detail. The analysis and

performance have been fully validated experimentally on a 40-60V/400V 1kW hardware prototype.

S3I_PED16_0020 - On The Robust Control of Parallel-Cascade DC/DC Buck Converter

This paper presents the design and simulation of robust control for DC/DC

multi-converter based on terms like differential flatness and active disturbance

rejection. The main goal of the control law is to obtain a regulated output voltage for each converter in cascade, and a balance of currents for parallel converters. The

controller must actively reject the endogenous and exogenous disturbances, i.e.

maintaining a robust output. The effectiveness of the proposed controller was verified by computer simulations using MATLAB/Simulink.

S3I_PED16_0021 - Single-Phase Grid Connected Motor Drive System with DC-link Shunt Compensator and Small DC-link Capacitor

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The single-phase diode rectifier system with small DC-link capacitor shows

wide diode conduction time and it improves the grid current harmonics. By shaping the output power, the system meets the grid current harmonics regulation without any

power factor corrector or grid filter inductor. However, the system has torque ripple

and suffers efficiency degradation due to the insufficient DC-link voltage. To solve this problem, this paper proposes the DC-link shunt compensator (DSC) for small

DC-link capacitor systems. DSC is located on DC-node parallel and operates as the

voltage source, improving the system performances. This circuit helps the grid

current-shaping control during grid-connection time, and reduces the flux-weakening current by supplying the energy to the motor during grid-disconnection time. This

paper presents a power control method and the design guideline of DSC. The

feasibility of DSC is verified by simulation and experimental results.

S3I_PED16_0022 - High Performance Solar MPPT Using Switching Ripple

Identification Based on a Lock-In Amplifier Photovoltaic (PV) power converters and Maximum Power Point Tracking

(MPPT) algorithms are required to ensure maximum energy transfer between the PV

panel and the load. The requirements for the MPPT algorithms have increased over the years - the algorithms are required to be increasingly accurate, fast, and versatile,

while reducing the intrusiveness on the overall performance of the PV panel and

converter. The family of Hill-climbing algorithms such as Incremental Conductance

(InCond) and Perturb and Observe (P&O) have gained popularity given their simplicity and accuracy, but they require the injection of a perturbation that changes

the operating point even in steady-state and are prone to errors during changing

environmental conditions. In recent literature, the use of the switching ripple has been proposed to replace the perturbation in the hill-climbing algorithms given its inherent

presence in the system and speed. The constant work towards smaller and faster

ripples presents challenges to the signal detection involved in this kind of algorithm. This paper develops and implements a new InCond MPPT technique based on

switching ripple detection using a digital Lock-In Amplifier (LIA) to extract the

amplitude of the oscillation ripple even in the presence of noise. The use of this

advanced technique allows to push forward the reduction of the ripple in order to virtually eliminate the oscillation in steady-state maximizing the efficiency. The

accurate detection allows for adaptive-step features for fast tracking of changing

environmental conditions while keeping the efficiency at maximum during the steady-state. Detailed mathematical analysis of the proposed technique is provided. Overall,

the use of the proposed LIA allows to push the reduction of the ripple even more

while keeping accuracy and delivering superior performance. Simulations and experimental results are provided for the proposed technique and the InCond

technique in order to validate the proposed approach.

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S3I_PED16_0023 - Zero-sequence Current Suppression for Open-end Winding Induction Motor Drive with Resonant Controller

Open-end winding topology with a common DC source has path for zero-

sequence current. Zero-sequence current needs to be suppressed because it does not contribute to the drive torque but has harmful effects such as loss and torque ripple.

Both inverter and motor have sources of the zero-sequence component. The zero-

sequence source on the inverter side is the voltage error due to dead time in switching.

The zero-sequence source on the motor side is the zero-sequence component of the back EMF which consists of the third harmonic. In this paper, the two zero-sequence

sources are investigated both theoretically and experimentally for an open-end

winding induction motor drive system, and a method to suppress the zero-sequence current suppression is proposed. The voltage error is compensated by feedforward to

the reference voltage. The zero-sequence component of the back EMF is compensated

by a proportional and resonant controller because its frequency is known while its amplitude and phase offset are unknown.

S3I_PED16_0024 - High Performance Model Predictive Technique for MPPT of Gird-tied Photovoltaic System Using Impedance-Source Inverter

This paper presents a maximum power point tracking (MPPT) method using

model predictive technique for a grid-tied photovoltaic harvesting energy system. A

single-stage grid-tied Z-source inverter is used for extracting the maximum available power and feeding the power to the grid. The proposed technique predicts the future

behavior of the photovoltaic side voltage and current by using a digital observer

which estimates the parameters of the photovoltaic module. The proposed method features simultaneously reduction in oscillation around maximum power point (MPP)

and fast convergence by adaptively changing the perturbation size using the predicted

model of the system. The experimental results demonstrate fast tracking response under dynamic weather condition, small steady state error, small oscillation around

MPP at steady state, and high MPPT efficacy for wide range of solar irradiance levels.

Additionally, the grid side current total harmonics distortion (THD) meets the IEEE-

519 standards.

S3I_PED16_0025 - New Equivalent Circuit of the IPM-type BLDC Motor for

Calculation of Shaft Voltage by Considering Electric and Magnetic Fields Fast switching and common-mode voltage of the space vector pulse width

modulation inverter output create several parasitic capacitances according to the

geometry of the motor. The windings emit the electric and magnetic flux inside of the motor. They create capacitance links and electromotive force (EMF) in the shaft,

respectively. Those capacitance links and EMF generate a current through two

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bearings of the shaft and directly reduces the bearing lifetime. In this study, we

propose a new design of equivalent circuit taking into account all parasitic capacitor components.

S3I_PED16_0026 - Grid Current Shaping Method with DC-link Shunt Compensator for Three-Phase Diode Rectifier-Fed Motor Drive System

This paper proposes grid current shaping method using DC-link shunt

compensator for three-phase diode rectifier system without electrolytic DC-link

capacitor. In the proposed method, the diode rectifier system can satisfy the grid regulation IEC61000-3-12 without any power factor correction circuit or heavy grid

filter inductor. The proposed grid current shaping method can be realized by applying

DSC with reduced-rating components and without electrolytic capacitor, which is connected parallel to the DC-link. Since DSC has no electrolytic capacitor, the system

with DSC has high circuit reliability. Furthermore, DSC can enhance the system

efficiency, especially in flux-weakening area, since the motor drive inverter recovers the reduced modulation index which has been spent in the additional control for small

passive components. This paper presents the control method for DSC and analyzes the

effect of proposed method. The feasibility of proposed method is verified by simulation and experimental results.

S3I_PED16_0027 - High Performance Predictive Control of Quasi Impedance

Source Inverter The quasi-Z-source inverter (qZSI) has attracted much attention for motor

drives and renewable energy applications due to its capability to boost or buck in a

single converter stage. However, this capability is associated with different challenges related to the closed loop control of currents, control the DC capacitor voltage,

produce three-phase AC output current with high dynamic performance and obtain

continuous and low ripple input current. This paper presents a predictive control strategy for a three-phase qZSI that fulfills these requirements without adding any

additional layers of control loops. The approach is to improve the overall performance

of the converter with a switching strategy that reduces inverter switching losses. The

proposed controller implements a discrete-time model of the qZSI to predict the future behavior of the circuit variables for each switching state, along with a set of multi-

objective control variables all in one cost function. The quasi impedance network and

the AC load are considered together when designing the controller in order to obtain stability of the impedance network with a step change in the output reference. A

detailed comparative investigation between the proposed controller and the

conventional PI controller is presented to prove the superiority of the proposed method over the conventional control method. Simulation and experimental results are

presented.

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S3I_PED16_0028 - Improved Fault-Tolerant Control for Brushless Permanent Magnet Motor Drives With Defective Hall Sensors

Brushless permanent magnet motor drives based on Hall sensors have received

significant attention in recent years. In this area, the faults of Hall sensors become a new concern and several fault-tolerant control (FTC) methods have been proposed.

However, most of the state-of-the-art FTC methods require some time to reconstruct

the correct Hall sensor signals, which results in significant transient currents and

speed dip during fault diagnostic process (FDP). In this paper, a new and improved FTC scheme based on FDP and vector-tracking observer is proposed. A method to

identify the duration of FDP is proposed based on the analysis of acceleration

estimation and the fault diagnosis results. During FDP, the method defaults to an open-loop observer control, which removes the undesirable current/torque transient.

After that, the close-loop observer is re-enabled and the motor operation is restored.

The proposed FTC is demonstrated in detailed simulations and experimentally on 120° brushless dc motor drives and sinusoidal PM motor drives. For both types of

drives, a significant improvement is achieved in steady state and transient operation

with faults of up to two Hall sensors, which has not been possible with available alternative FTC approaches (unless a sensorless control is used).

S3I_PED16_0029 - Quasi-Resonant (QR) Controller with Adaptive Switching

Frequency Reduction Scheme for Flyback Converter A quasi-resonant (QR) controller with an adaptive frequency reduction scheme

has been developed for a flyback converter. While maintaining the valley switching,

the QR controller reduces the switching frequency for lighter load by skipping some valleys to reduce the power loss and thereby achieve better light load efficiency. If the

QR controller cannot detect any valley due to the damped oscillation of switch

voltage, the valley switching is given up and the non-valley switching is employed to keep reducing the switching frequency for lighter load. The proposed QR controller

has been implemented in a 0.35-μm 700-V BCDMOS process and applied to a 40-W

flyback converter. The power efficiency of the flyback converter is improved by upto

3.0-% when the proposed QR controller is employed compared to the one employing the conventional QR controller.

S3I_PED16_0030 - Self-correction of Commutation Point for High-speed Sensorless BLDC Motor With Low Inductance and Nonideal Back EMF

This paper presents a novel self-correction method of commutation point for

high-speed sensorless brushless dc (BLDC) motors with low inductance and nonideal back electromotive force (EMF) in order to achieve low steady-state loss of

magnetically suspended control moment gyro (MSCMG). The commutation point

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before correction is obtained by detecting the phase of EMF zero-crossing point and

then delaying 30 electrical degrees. Since the speed variation is small between adjacent commutation points, the difference of the nonenergized phase’s terminal

voltage between the beginning and the end of commutation is mainly related to the

commutation error. A novel control method based on model-free adaptive control is proposed, and the delay degree is corrected by the controller in real time. Both the

simulation and experimental results show that the proposed correction method can

achieve ideal commutation effect within the entire operating speed range.

S3I_PED16_0031 - Performance Analysis of the Computational Implementation

of a Simplified PV Model and MPPT Algorithm

In many research centers around the world, had been researched models that accurately represent the PV modules operation. In this context, this paper presents a

modeling of photovoltaic modules, which aims to simplify the simulation of

photovoltaic systems in MATLAB®/Simulink. The model in question has not been very explored in the literature and, therefore, this paper has the purpose of evaluating

its results connecting the model to a boost converter, being its main function the

Maximum Power Point Tracking applying one of the most known methods, the P&O (Perturb & Observe). Furthermore, this converter elevates the voltage generated by

the photovoltaic modules, in order to connect the PV array to the grid through an

inverter. This paper also presents a good correlation between the theoretical and

practical results from the proposed modeling and high efficiency of the implemented MPPT algorithm as well.

S3I_PED16_0032 - Model Predictive Control Scheme of Five-Leg AC-DC-AC

Converter-Fed Induction Motor Drive AC-DC-AC converter-fed induction motor drive is mainly realized by back-to-

back three-phase converters. However, fault in a single semiconductor switch will

make it inoperative. To enable continued controllable operation in case of the faults

occurring in the converter, the five-leg converter with a shared leg between the grid and load sides is a possible solution. However, this topology poses an inherent two-

objective control problem because its grid and load sides should be controlled

simultaneously. More importantly, the potential increase of the shared-leg current may destroy the converters. In this paper, a new control scheme based on the FCS-MPC

combined with intrinsic characteristic of the five-leg converter is proposed for

independent control of the rectifier and inverter subsystem with the shared-leg overcurrent constraint. The condition for independent control is analyzed. In order to

give a complete evaluation of the proposed control scheme, the conventional PWM

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control scheme is conducted for comparison. Experimental results are provided to

validate the effectiveness of the proposed scheme.

Education

Final year IEEE 2016-2017 PROJECTS TITLES (IEEE 2016 papers) For ME,M.Tech,BE,B.Tech (.net,java,matlab,vlsi,ns2,mech,power elc & power sys)