2013 2014 ieee projects titles with abstract

TYPICAL SOFT TECHNOLOGIES

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MOBILE COMPUTING

1. ALERT: An Anonymous Location-Based Efficient Routing Protocol in

MANETs

Abstract :

Mobile Ad Hoc Networks (MANETs) use anonymous routing protocols that hide node

identities and/or routes from outside observers in order to provide anonymity protection.

However, existing anonymous routing protocols relying on either hop-by-hop encryption or

redundant traffic, either generate high cost or cannot provide full anonymity protection to data

sources, destinations, and routes. The high cost exacerbates the inherent resource constraint

problem in MANETs especially in multimedia wireless applications. To offer high anonymity

protection at a low cost, we propose an Anonymous Location-based Efficient Routing proTocol

(ALERT). ALERT dynamically partitions the network field into zones and randomly chooses

nodes in zones as intermediate relay nodes, which form a nontraceable anonymous route. In

addition, it hides the data initiator/receiver among many initiators/receivers to strengthen source

and destination anonymity protection. Thus, ALERT offers anonymity protection to sources,

destinations, and routes. It also has strategies to effectively counter intersection and timing

attacks. We theoretically analyze ALERT in terms of anonymity and efficiency. Experimental

results exhibit consistency with the theoretical analysis, and show that ALERT achieves better

route anonymity protection and lower cost compared to other anonymous routing protocols.

Also, ALERT achieves comparable routing efficiency to the GPSR geographical routing

protocol.


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2. DSS: Distributed SINR-Based Scheduling Algorithm for Multihop

Wireless Networks

Abstract :

The problem of developing distributed scheduling algorithms for high throughput in

multihop wireless networks has been extensively studied in recent years. The design of a

distributed low-complexity scheduling algorithm becomes even more challenging when taking

into account a physical interference model, which requires the SINR at a receiver to be checked

when making scheduling decisions. To do so, we need to check whether a transmission failure is

caused by interference due to simultaneous transmissions from distant nodes. In this paper, we

propose a scheduling algorithm under a physical interference model, which is amenable to

distributed implementation with 802.11 CSMA technologies. The proposed scheduling algorithm

is shown to achieve throughput optimality. We present two variations of the algorithm to

enhance the delay performance and to reduce the control overhead, respectively, while retaining

throughput optimality.

3. Toward Accurate Mobile Sensor Network Localization in Noisy

Environments

Abstract :

The node localization problem in mobile sensor networks has received significant attention.

Recently, particle filters adapted from robotics have produced good localization accuracies in

conventional settings. In spite of these successes, state-of-theart solutions suffer significantly

when used in challenging indoor and mobile environments characterized by a high degree of

radio signal irregularity. New solutions are needed to address these challenges. We propose a

fuzzy logic-based approach for mobile node localization in challenging environments.

Localization is formulated as a fuzzy multilateration problem. For sparse networks with few

available anchors, we propose a fuzzy grid-prediction scheme. The fuzzy logic-based

localization scheme is implemented in a simulator and compared to state-of-the-art solutions.


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Extensive simulation results demonstrate improvements in the localization accuracy from 20 to

40 percent when the radio irregularity is high. A hardware implementation running on Epic

motes and transported by iRobot mobile hosts confirms simulation results and extends them to

the real world.

4. Adaptive Duty Cycle Control with Queue Management in Wireless Sensor

Networks

Abstract :

This paper proposes a control-based approach to the duty cycle adaptation for wireless sensor

networks. The proposed method controls the duty cycle through the queue management to

achieve high-performance under variable traffic rates. To have energy efficiency while

minimizing the delay, we design a feedback controller, which adapts the sleep time to the traffic

change dynamically by constraining the queue length at a predetermined value. In addition, we

propose an efficient synchronization scheme using an active pattern, which represents the active

time slot schedule for synchronization among sensor nodes, without affecting neighboring

schedules. Based on the control theory, we analyze the adaptation behavior of the proposed

controller and demonstrate system stability. The simulation results show that the proposed

method outperforms existing schemes by achieving more power savings while minimizing the

delay.

5. Cooperative Packet Delivery in Hybrid Wireless Mobile Networks: A

Coalitional Game Approach

Abstract :

We consider the problem of cooperative packet delivery to mobile nodes in a hybrid wireless

mobile network, where both infrastructure-based and infrastructure-less (i.e., ad hoc mode or

peer-to-peer mode) communications are used. We propose a solution based on a coalition


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formation among mobile nodes to cooperatively deliver packets among these mobile nodes in the

same coalition. A coalitional game is developed to analyze the behavior of the rational mobile

nodes for cooperative packet delivery. A group of mobile nodes makes a decision to join or to

leave a coalition based on their individual payoffs. The individual payoff of each mobile node is

a function of the average delivery delay for packets transmitted to the mobile node from a base

station and the cost incurred by this mobile node for relaying packets to other mobile nodes. To

find the payoff of each mobile node, a Markov chain model is formulated and the expected cost

and packet delivery delay are obtained when the mobile node is in a coalition. Since both the

expected cost and packet delivery delay depend on the probability that each mobile node will

help other mobile nodes in the same coalition to forward packets to the destination mobile node

in the same coalition, a bargaining game is used to find the optimal helping probabilities. After

the payoff of each mobile node is obtained, we find the solutions of the coalitional game which

are the stable coalitions. A distributed algorithm is presented to obtain the stable coalitions and a

Markov-chain-based analysis is used to evaluate the stable coalitional structures obtained from

the distributed algorithm. Performance evaluation results show that when the stable coalitions are

formed, the mobile nodes achieve a nonzero payoff (i.e., utility is higher than the cost). With a

coalition formation, the mobile nodes achieve higher payoff than that when each mobile node

acts alone.

6. VAPR: Void-Aware Pressure Routing for Underwater Sensor Networks

Abstract :

Underwater mobile sensor networks have recently been proposed as a way to explore and

observe the ocean, providing 4D (space and time) monitoring of underwater environments. We

consider a specialized geographic routing problem called pressure routing that directs a packet to

any sonobuoy on the surface based on depth information available from on-board pressure

gauges. The main challenge of pressure routing in sparse underwater networks has been the

efficient handling of 3D voids. In this respect, it was recently proven that the greedy stateless

perimeter routing method, very popular in 2D networks, cannot be extended to void recovery in

3D networks. Available heuristics for 3D void recovery require expensive flooding. In this paper,


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we propose a Void-Aware Pressure Routing (VAPR) protocol that uses sequence number, hop

count and depth information embedded in periodic beacons to set up nexthop direction and to

build a directional trail to the closest sonobuoy. Using this trail, opportunistic directional

forwarding can be efficiently performed even in the presence of voids. The contribution of this

paper is twofold: 1) a robust soft-state routing protocol that supports opportunistic directional

forwarding; and 2) a new framework to attain loop freedom in static and mobile underwater

networks to guarantee packet delivery. Extensive simulation results show that VAPR

outperforms existing solutions.

7. DCIM: Distributed Cache Invalidation Method for Maintaining Cache

Consistency in Wireless Mobile Networks

Abstract :

This paper proposes distributed cache invalidation mechanism (DCIM), a client-based cache

consistency scheme that is implemented on top of a previously proposed architecture for caching

data items in mobile ad hoc networks (MANETs), namely COACS, where special nodes cache

the queries and the addresses of the nodes that store the responses to these queries. We have also

previously proposed a server-based consistency scheme, named SSUM, whereas in this paper,

we introduce DCIM that is totally client-based. DCIM is a pull-based algorithm that implements

adaptive time to live (TTL), piggybacking, and prefetching, and provides near strong consistency

capabilities. Cached data items are assigned adaptive TTL values that correspond to their update

rates at the data source, where items with expired TTL values are grouped in validation requests

to the data source to refresh them, whereas unexpired ones but with high request rates are

prefetched from the server. In this paper, DCIM is analyzed to assess the delay and bandwidth

gains (or costs) when compared to polling every time and push-based schemes. DCIM was also

implemented using ns2, and compared against client-based and server-based schemes to assess

its performance experimentally. The consistency ratio, delay, and overhead traffic are reported

versus several variables, where DCIM showed to be superior when compared to the other

systems.


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8. Cross-Layer Minimum-Delay Scheduling and Maximum-Throughput

Resource Allocation for Multiuser Cognitive Networks

Abstract :

A cognitive network is considered that consists of a base station (BS) communicating with

multiple primary and secondary users. Each secondary user can access only one of the

orthogonal primary channels. A model is considered in which the primary users can tolerate a

certain average delay. A special case is also considered in which the primary users do not suffer

from any delay. A novel cross-layer scheme is proposed in which the BS performs successive

interference cancellation and thus a secondary user can coexist with an active primary user

without adversely affecting its transmission. A scheduling algorithm is proposed that minimizes

the average packet delay of the secondary user under constraints on the average power

transmitted by the secondary user and the average packet delay of the primary user. A resource

allocation algorithm is also proposed to assign the secondary users’ channels such that the total

throughput of the network is maximized. Our results indicate that the network throughput

increases significantly by increasing the number of transmitted packets of the secondary users

and/or by allowing a small delay for the primary user packets.

9. Scheduling Partition for Order Optimal Capacity in Large-Scale Wireless

Networks

Abstract :

The capacity scaling property specifies the change of network throughput when network size

increases. It serves as an essential performance metric in large-scale wireless networks. Existing

results have been obtained based on the assumption of using a globally planned link transmission

schedule in the network, which is however not feasible in large wireless networks due to the

scheduling complexity. The gap between the well-known capacity results and the infeasible


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assumption on link scheduling potentially undermines our understanding of the achievable

network capacity. In this paper, we propose the scheduling partition methodology that

decomposes a large network into small autonomous scheduling zones and implements a localized

scheduling algorithm independently in each partition. We prove the sufficient and the necessary

conditions for the scheduling partition approach to achieve the same order of capacity as the

widely assumed global scheduling strategy. In comparison to the network dimension ffiffiffi n p ,

scheduling partition size �ðrðnÞÞ is sufficient to obtain the optimal capacity scaling, where rðnÞ

is the node transmission radius and much smaller than ffiffiffi n p . We finally propose a

distributed partition protocol and a localized scheduling algorithm as our scheduling solution for

maximum capacity in large wireless networks.

10. Video On-Demand Streaming in Cognitive Wireless Mesh Networks

Abstract :

Cognitive radio (CR), which enables dynamic access of underutilized licensed spectrums, is a

promising technology for more efficient spectrum utilization. Since cognitive radio enables the

access of larger amount of spectrum, it can be used to build wireless mesh networks with higher

network capacity, and thus provide better quality of services for high bit-rate applications. In this

paper, we study the multisource video on-demand application in multi-interface cognitive

wireless mesh networks. Given a video request, we find a joint multipath routing and spectrum

allocation for the session to minimize its total bandwidth cost in the network, and therefore

maximize the number of sessions the network can support. We propose both distributed and

centralized routing and channel allocation algorithms to solve the problem. Simulation results

show that our algorithms increase the maximum number of concurrent sessions that can be

supported in the network, and also improve each session’s performance with regard to spectrum

mobility.


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11. Relay Selection for Geographical Forwarding in Sleep-Wake Cycling

Wireless Sensor Networks

Abstract :

Our work is motivated by geographical forwarding of sporadic alarm packets to a base

station in a wireless sensor network (WSN), where the nodes are sleep-wake cycling periodically

and asynchronously. We seek to develop local forwarding algorithms that can be tuned so as to

tradeoff the end-to-end delay against a total cost, such as the hop count or total energy. Our

approach is to solve, at each forwarding node enroute to the sink, the local forwarding problem

of minimizing one-hop waiting delay subject to a lower bound constraint on a suitable reward

offered by the next-hop relay; the constraint serves to tune the tradeoff. The reward metric used

for the local problem is based on the end-to-end total cost objective (for instance, when the total

cost is hop count, we choose to use the progress toward sink made by a relay as the reward). The

forwarding node, to begin with, is uncertain about the number of relays, their wake-up times, and

the reward values, but knows the probability distributions of these quantities. At each relay

wake-up instant, when a relay reveals its reward value, the forwarding node’s problem is to

forward the packet or to wait for further relays to wake-up. In terms of the operations research

literature, our work can be considered as a variant of the asset selling problem. We formulate our

local forwarding problem as a partially observable Markov decision process (POMDP) and

obtain inner and outer bounds for the optimal policy. Motivated by the computational complexity

involved in the policies derived out of these bounds, we formulate an alternate simplified model,

the optimal policy for which is a simple threshold rule. We provide simulation results to compare

the performance of the inner and outer bound policies against the simple policy, and also against

the optimal policy when the source knows the exact number of relays. Observing the good

performance and the ease of implementation of the simple policy, we apply it to our motivating

problem, i.e., local geographical routing of sporadic alarm packets in a large WSN. We compare

the end-to-end performance (i.e., average total delay and average total cost) obtained by the

simple policy, when used for local geographical forwarding, against that obtained by the globally

optimal forwarding algorithm proposed by Kim.


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12. Adaptive Position Update for Geographic Routing in Mobile Ad Hoc

Networks

Abstract :

In geographic routing, nodes need to maintain up-to-date positions of their immediate

neighbors for making effective forwarding decisions. Periodic broadcasting of beacon packets

that contain the geographic location coordinates of the nodes is a popular method used by most

geographic routing protocols to maintain neighbor positions. We contend and demonstrate that

periodic beaconing regardless of the node mobility and traffic patterns in the network is not

attractive from both update cost and routing performance points of view. We propose the

Adaptive Position Update (APU) strategy for geographic routing, which dynamically adjusts the

frequency of position updates based on the mobility dynamics of the nodes and the forwarding

patterns in the network. APU is based on two simple principles: 1) nodes whose movements are

harder to predict update their positions more frequently (and vice versa), and (ii) nodes closer to

forwarding paths update their positions more frequently (and vice versa). Our theoretical

analysis, which is validated by NS2 simulations of a well-known geographic routing protocol,

Greedy Perimeter Stateless Routing Protocol (GPSR), shows that APU can significantly reduce

the update cost and improve the routing performance in terms of packet delivery ratio and

average end-to-end delay in comparison with periodic beaconing and other recently proposed

updating schemes. The benefits of APU are further confirmed by undertaking evaluations in

realistic network scenarios, which account for localization error, realistic radio propagation, and

sparse network.

13. Channel Allocation and Routing in Hybrid Multichannel Multiradio

Wireless Mesh Networks

Abstract :


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Many efforts have been devoted to maximizing network throughput in a multichannel

multiradio wireless mesh network. Most current solutions are based on either purely static or

purely dynamic channel allocation approaches. In this paper, we propose a hybrid multichannel

multiradio wireless mesh networking architecture, where each mesh node has both static and

dynamic interfaces. We first present an Adaptive Dynamic Channel Allocation protocol

(ADCA), which considers optimization for both throughput and delay in the channel assignment.

In addition, we also propose an Interference and Congestion Aware Routing protocol (ICAR) in

the hybrid network with both static and dynamic links, which balances the channel usage in the

network. Our simulation results show that compared to previous works, ADCA reduces the

packet delay considerably without degrading the network throughput. The hybrid architecture

shows much better adaptivity to changing traffic than purely static architecture without dramatic

increase in overhead, and achieves lower delay than existing approaches for hybrid networks.

14. Toward Privacy Preserving and Collusion Resistance in a Location Proof

Updating System

Abstract :

Today’s location-sensitive service relies on user’s mobile device to determine the current

location. This allows malicious users to access a restricted resource or provide bogus alibis by

cheating on their locations. To address this issue, we propose A Privacy-Preserving LocAtion

proof Updating System (APPLAUS) in which colocated Bluetooth enabled mobile devices

mutually generate location proofs and send updates to a location proof server. Periodically

changed pseudonyms are used by the mobile devices to protect source location privacy from

each other, and from the untrusted location proof server. We also develop user-centric location

privacy model in which individual users evaluate their location privacy levels and decide

whether and when to accept the location proof requests. In order to defend against colluding

attacks, we also present betweenness ranking-based and correlation clustering-based approaches

for outlier detection. APPLAUS can be implemented with existing network infrastructure, and

can be easily deployed in Bluetooth enabled mobile devices with little computation or power


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cost. Extensive experimental results show that APPLAUS can effectively provide location

proofs, significantly preserve the source location privacy, and effectively detect colluding

attacks.

15. SSD: A Robust RF Location Fingerprint Addressing Mobile Devices’

Heterogeneity

Abstract :

Fingerprint-based methods are widely adopted for indoor localization purpose because of

their cost-effectiveness compared to other infrastructure-based positioning systems. However,

the popular location fingerprint, Received Signal Strength (RSS), is observed to differ

significantly across different devices’ hardware even under the same wireless conditions. We

derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD),

and verify its performance experimentally using a number of different mobile devices with

heterogeneous hardware. Our experiments have also considered both Wi-Fi and Bluetooth

devices, as well as both Access-Point(AP)-based localization and Mobile-Node (MN)-assisted

localization. We present the results of two well-known localization algorithms (K Nearest

Neighbor and Bayesian Inference) when our proposed fingerprint is used, and demonstrate its

robustness when the testing device differs from the training device. We also compare these SSD-

based localization algorithms’ performance against that of two other approaches in the literature

that are designed to mitigate the effects of mobile node hardware variations, and show that SSD-

based algorithms have better accuracy.

16. EMAP: Expedite Message Authentication Protocol for Vehicular Ad Hoc

Networks

Abstract :


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Vehicular ad hoc networks (VANETs) adopt the Public Key Infrastructure (PKI) and

Certificate Revocation Lists (CRLs) for their security. In any PKI system, the authentication of a

received message is performed by checking if the certificate of the sender is included in the

current CRL, and verifying the authenticity of the certificate and signature of the sender. In this

paper, we propose an Expedite Message Authentication Protocol (EMAP) for VANETs, which

replaces the time-consuming CRL checking process by an efficient revocation checking process.

The revocation check process in EMAP uses a keyed Hash Message Authentication Code

ðHMACÞ, where the key used in calculating theHMAC is shared only between nonrevoked On-

Board Units (OBUs). In addition, EMAP uses a novel probabilistic key distribution, which

enables nonrevoked OBUs to securely share and update a secret key. EMAP can significantly

decrease the message loss ratio due to the message verification delay compared with the

conventional authentication methods employing CRL. By conducting security analysis and

performance evaluation,EMAP is demonstrated to be secure and efficient.

17. Channel Assignment for Throughput Optimization in Multichannel

Multiradio Wireless Mesh Networks Using Network Coding

Abstract :

Compared to single-hop networks such as WiFi, multihop infrastructure wireless mesh

networks (WMNs) can potentially embrace the broadcast benefits of a wireless medium in a

more flexible manner. Rather than being point-to-point, links in the WMNs may originate from a

single node and reach more than one other node. Nodes located farther than a one-hop distance

and overhearing such transmissions may opportunistically help relay packets for previous hops.

This phenomenon is called opportunistic overhearing/ listening. With multiple radios, a node can

also improve its capacity by transmitting over multiple radios simultaneously using orthogonal

channels. Capitalizing on these potential advantages requires effective routing and efficient

mapping of channels to radios (channel assignment (CA)). While efficient channel assignment

can greatly reduce interference from nearby transmitters, effective routing can potentially relieve

congestion on paths to the infrastructure. Routing, however, requires that only packets pertaining


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to a particular connection be routed on a predetermined route. Random network coding (RNC)

breaks this constraint by allowing nodes to randomly mix packets overheard so far before

forwarding. A relay node thus only needs to know how many packets, and not which packets, it

should send. We mathematically formulate the joint problem of random network coding, channel

assignment, and broadcast link scheduling, taking into account opportunistic overhearing, the

interference constraints, the coding constraints, the number of orthogonal channels, the number

of radios per node, and fairness among unicast connections. Based on this formulation, we

develop a suboptimal, auction-based solution for overall network throughput optimization.

Performance evaluation results show that our algorithm can effectively exploit multiple radios

and channels and can cope with fairness issues arising from auctions. Our algorithm also shows

promising gains over traditional routing solutions in which various channel assignment strategies

are used.

18. Content Sharing over Smartphone-Based Delay-Tolerant Networks

Abstract :

With the growing number of smartphone users, peer-to-peer ad hoc content sharing is

expected to occur more often. Thus, new content sharing mechanisms should be developed as

traditional data delivery schemes are not efficient for content sharing due to the sporadic

connectivity between smartphones. To accomplish data delivery in such challenging

environments, researchers have proposed the use of store-carry-forward protocols, in which a

node stores a message and carries it until a forwarding opportunity arises through an encounter

with other nodes. Most previous works in this field have focused on the prediction of whether

two nodes would encounter each other, without considering the place and time of the encounter.

In this paper, we propose discover-predict-deliver as an efficient content sharing scheme for

delay-tolerant smartphone networks. In our proposed scheme, contents are shared using the

mobility information of individuals. Specifically, our approach employs a mobility learning

algorithm to identify places indoors and outdoors. A hidden Markov model is used to predict an

individual’s future mobility information. Evaluation based on real traces indicates that with the


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proposed approach, 87 percent of contents can be correctly discovered and delivered within 2

hours when the content is available only in 30 percent of nodes in the network. We implement a

sample application on commercial smartphones, and we validate its efficiency to analyze the

practical feasibility of the content sharing application. Our system approximately results in a 2

percent CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at most.

19. Discovery and Verification of Neighbor Positions in Mobile Ad Hoc

Networks

Abstract :

A growing number of ad hoc networking protocols and location-aware services require that

mobile nodes learn the position of their neighbors. However, such a process can be easily abused

or disrupted by adversarial nodes. In absence of a priori trusted nodes, the discovery and

verification of neighbor positions presents challenges that have been scarcely investigated in the

literature. In this paper, we address this open issue by proposing a fully distributed cooperative

solution that is robust against independent and colluding adversaries, and can be impaired only

by an overwhelming presence of adversaries. Results show that our protocol can thwart more

than 99 percent of the attacks under the best possible conditions for the adversaries, with

minimal false positive rates.

20. Mobile Relay Configuration in Data-Intensive Wireless Sensor Networks

Abstract :

Wireless Sensor Networks (WSNs) are increasingly used in data-intensive applications such

as microclimate monitoring, precision agriculture, and audio/video surveillance. A key challenge

faced by data-intensive WSNs is to transmit all the data generated within an application’s

lifetime to the base station despite the fact that sensor nodes have limited power supplies. We

propose using lowcost disposable mobile relays to reduce the energy consumption of data-


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intensive WSNs. Our approach differs from previous work in two main aspects. First, it does not

require complex motion planning of mobile nodes, so it can be implemented on a number of low-

cost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility

and wireless transmissions into a holistic optimization framework. Our framework consists of

three main algorithms. The first algorithm computes an optimal routing tree assuming no nodes

can move. The second algorithm improves the topology of the routing tree by greedily adding

new nodes exploiting mobility of the newly added nodes. The third algorithm improves the

routing tree by relocating its nodes without changing its topology. This iterative algorithm

converges on the optimal position for each node given the constraint that the routing tree

topology does not change. We present efficient distributed implementations for each algorithm

that require only limited, localized synchronization. Because we do not necessarily compute an

optimal topology, our final routing tree is not necessarily optimal. However, our simulation

results show that our algorithms significantly outperform the best existing solutions.

21. Vampire Attacks: Draining Life from Wireless Ad Hoc Sensor Networks

Abstract :

Ad hoc low-power wireless networks are an exciting research direction in sensing and

pervasive computing. Prior security work in this area has focused primarily on denial of

communication at the routing or medium access control levels. This paper explores resource

depletion attacks at the routing protocol layer, which permanently disable networks by quickly

draining nodes’ battery power. These ―Vampire‖ attacks are not specific to any specific protocol,

but rather rely on the properties of many popular classes of routing protocols. We find that all

examined protocols are susceptible to Vampire attacks, which are devastating, difficult to detect,

and are easy to carry out using as few as one malicious insider sending only protocol-compliant

messages. In the worst case, a single Vampire can increase network-wide energy usage by a

factor of OðNÞ, where N in the number of network nodes. We discuss methods to mitigate these

types of attacks, including a new proof-of-concept protocol that provably bounds the damage

caused by Vampires during the packet forwarding phase.


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CLOUD COMPUTING

1. Optimal Multiserver Configuration for Profit Maximization in Cloud

Computing

Abstract :

As cloud computing becomes more and more popular, understanding the economics of cloud

computing becomes critically important. To maximize the profit, a service provider should

understand both service charges and business costs, and how they are determined by the

characteristics of the applications and the configuration of a multiserver system. The problem of

optimal multiserver configuration for profit maximization in a cloud computing environment is

studied. Our pricing model takes such factors into considerations as the amount of a service, the

workload of an application environment, the configuration of a multiserver system, the service-

level agreement, the satisfaction of a consumer, the quality of a service, the penalty of a low-

quality service, the cost of renting, the cost of energy consumption, and a service provider’s

margin and profit. Our approach is to treat a multiserver system as an M/M/m queuing model,

such that our optimization problem can be formulated and solved analytically. Two server speed

and power consumption models are considered, namely, the idle-speed model and the constant-

speed model. The probability density function of the waiting time of a newly arrived service

request is derived. The expected service charge to a service request is calculated. The expected

net business gain in one unit of time is obtained. Numerical calculations of the optimal server

size and the optimal server speed are demonstrated.


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2. Efficient Resource Mapping Framework over Networked Clouds via

Iterated Local Search-Based Request Partitioning

Abstract :

The cloud represents a computing paradigm where shared configurable resources are

provided as a service over the Internet. Adding intra- or intercloud communication resources to

the resource mix leads to a networked cloud computing environment. Following the cloud

infrastructure as a Service paradigm and in order to create a flexible management framework, it

is of paramount importance to address efficiently the resource mapping problem within this

context. To deal with the inherent complexity and scalability issue of the resource mapping

problem across different administrative domains, in this paper a hierarchical framework is

described. First, a novel request partitioning approach based on Iterated Local Search is

introduced that facilitates the cost-efficient and online splitting of user requests among eligible

cloud service providers (CPs) within a networked cloud environment. Following and capitalizing

on the outcome of the request partitioning phase, the embedding phase—where the actual

mapping of requested virtual to physical resources is performed can be realized through the use

of a distributed intracloud resource mapping approach that allows for efficient and balanced

allocation of cloud resources. Finally, a thorough evaluation of the proposed overall framework

on a simulated networked cloud environment is provided and critically compared against an

exact request partitioning solution as well as another common intradomain virtual resource

embedding solution.


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3. Harnessing the Cloud for Securely Outsourcing Large-Scale Systems of

Linear Equations

Abstract :

Cloud computing economically enables customers with limited computational resources to

outsource large-scale computations to the cloud. However, how to protect customers’

confidential data involved in the computations then becomes a major security concern. In this

paper, we present a secure outsourcing mechanism for solving large-scale systems of linear

equations (LE) in cloud. Because applying traditional approaches like Gaussian elimination or

LU decomposition (aka. direct method) to such large-scale LEs would be prohibitively

expensive, we build the secure LE outsourcing mechanism via a completely different approach—

iterative method, which is much easier to implement in practice and only demands relatively

simpler matrix-vector operations. Specifically, our mechanism enables a customer to securely

harness the cloud for iteratively finding successive approximations to the LE solution, while

keeping both the sensitive input and output of the computation private. For robust cheating

detection, we further explore the algebraic property of matrix-vector operations and propose an

efficient result verification mechanism, which allows the customer to verify all answers received

from previous iterative approximations in one batch with high probability. Thorough security

analysis and prototype experiments on Amazon EC2 demonstrate the validity and practicality of

our proposed design.

4. QoS Ranking Prediction for Cloud Services

Abstract :

Cloud computing is becoming popular. Building high-quality cloud applications is a critical

research problem. QoS rankings provide valuable information for making optimal cloud service

selection from a set of functionally equivalent service candidates. To obtain QoS values, real-


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world invocations on the service candidates are usually required. To avoid the time-consuming

and expensive real-world service invocations, this paper proposes a QoS ranking prediction

framework for cloud services by taking advantage of the past service usage experiences of other

consumers. Our proposed framework requires no additional invocations of cloud services when

making QoS ranking prediction. Two personalized QoS ranking prediction approaches are

proposed to predict the QoS rankings directly. Comprehensive experiments are conducted

employing real-world QoS data, including 300 distributed users and 500 realworld web services

all over the world. The experimental results show that our approaches outperform other

competing approaches.

5. Cloudy with a Chance of Cost Savings

Abstract :

Cloud-based hosting is claimed to possess many advantages over traditional in-house (on-

premise) hosting such as better scalability, ease of management, and cost savings. It is not

difficult to understand how cloud-based hosting can be used to address some of the existing

limitations and extend the capabilities of many types of applications. However, one of the most

important questions is whether cloud-based hosting will be economically feasible for my

application if migrated into the cloud. It is not straightforward to answer this question because it

is not clear how my application will benefit from the claimed advantages, and, in turn, be able to

convert them into tangible cost savings. Within cloud-based hosting offerings, there is a wide

range of hosting options one can choose from, each impacting the cost in a different way.

Answering these questions requires an in-depth understanding of the cost implications of all the

possible choices specific to my circumstances. In this study, we identify a diverse set of key

factors affecting the costs of deployment choices. Using benchmarks representing two different

applications (TPC-W and TPC-E) we investigate the evolution of costs for different deployment


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choices. We consider important application characteristics such as workload intensity, growth

rate, traffic size, storage, and software license to understand their impact on the overall costs. We

also discuss the impact of workload variance and cloud elasticity, and certain cost factors that are

subjective in nature.

6. Error-Tolerant Resource Allocation and Payment Minimization for Cloud

System

Abstract :

With virtual machine (VM) technology being increasingly mature, compute resources in

cloud systems can be partitioned in fine granularity and allocated on demand. We make three

contributions in this paper: 1) We formulate a deadline-driven resource allocation problem based

on the cloud environment facilitated with VM resource isolation technology, and also propose a

novel solution with polynomial time, which could minimize users’ payment in terms of their

expected deadlines. 2) By analyzing the upper bound of task execution length based on the

possibly inaccurate workload prediction, we further propose an error-tolerant method to

guarantee task’s completion within its deadline. 3) We validate its effectiveness over a real VM-

facilitated cluster environment under different levels of competition. In our experiment, by

tuning algorithmic input deadline based on our derived bound, task execution length can always

be limited within its deadline in the sufficient-supply situation; the mean execution length still

keeps 70 percent as high as userspecified deadline under the severe competition. Under the

original-deadline-based solution, about 52.5 percent of tasks are completed within 0.95-1.0 as

high as their deadlines, which still conforms to the deadline-guaranteed requirement. Only 20

percent of tasks violate deadlines, yet most (17.5 percent) are still finished within 1.05 times of

deadlines.


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7. Mona: Secure Multi-Owner Data Sharing for Dynamic Groups in the

Cloud

Abstract :

With the character of low maintenance, cloud computing provides an economical and

efficient solution for sharing group resource among cloud users. Unfortunately, sharing data in a

multi-owner manner while preserving data and identity privacy from an untrusted cloud is still a

challenging issue, due to the frequent change of the membership. In this paper, we propose a

secure multiowner data sharing scheme, named Mona, for dynamic groups in the cloud. By

leveraging group signature and dynamic broadcast encryption techniques, any cloud user can

anonymously share data with others. Meanwhile, the storage overhead and encryption

computation cost of our scheme are independent with the number of revoked users. In addition,

we analyze the security of our scheme with rigorous proofs, and demonstrate the efficiency of

our scheme in experiments.

8. A New Disk I/O Model of Virtualized Cloud Environment

Abstract :

In a traditional virtualized cloud environment, using asynchronous I/O in the guest file

system and synchronous I/O in the host file system to handle an asynchronous user disk write

exhibits several drawbacks, such as performance disturbance among different guests and

consistency maintenance across guest failures. To improve these issues, this paper introduces a

novel disk I/O model for virtualized cloud system called HypeGear, where the guest file system

uses synchronous operations to deal with the guest write request and the host file system

performs asynchronous operations to write the data to the hard disk. A prototype system is

implemented on the Xen hypervisor and our experimental results verify that this new model has


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many advantages over the conventional asynchronous-synchronous model. We also evaluate the

overhead of asynchronous I/O at host, which is brought by our new model. The result

demonstrates that it enforces little cost on host layer.

9. On Data Staging Algorithms for Shared Data Accesses in Clouds

Abstract :

In this paper, we study the strategies for efficiently achieving data staging and caching on a

set of vantage sites in a cloud system with a minimum cost. Unlike the traditional research, we

do not intend to identify the access patterns to facilitate the future requests. Instead, with such a

kind of information presumably known in advance, our goal is to efficiently stage the shared data

items to predetermined sites at advocated time instants to align with the patterns while

minimizing the monetary costs for caching and transmitting the requested data items. To this

end, we follow the cost and network models in [1] and extend the analysis to multiple data items,

each with single or multiple copies. Our results show that under homogeneous cost model, when

the ratio of transmission cost and caching cost is low, a single copy of each data item can

efficiently serve all the user requests. While in multicopy situation, we also consider the tradeoff

between the transmission cost and caching cost by controlling the upper bounds of transmissions

and copies. The upper bound can be given either on per-item basis or on all-item basis. We

present efficient optimal solutions based on dynamic programming techniques to all these cases

provided that the upper bound is polynomially bounded by the number of service requests and

the number of distinct data items. In addition to the homogeneous cost model, we also briefly

discuss this problem under a heterogeneous cost model with some simple yet practical

restrictions and present a 2-approximation algorithm to the general case. We validate our

findings by implementing a data staging solver, whereby conducting extensive simulation studies

on the behaviors of the algorithms.


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10. Dynamic Optimization of Multiattribute Resource Allocation in Self-

Organizing Clouds

Abstract :

By leveraging virtual machine (VM) technology which provides performance and fault isolation,

cloud resources can be provisioned on demand in a fine grained, multiplexed manner rather than

in monolithic pieces. By integrating volunteer computing into cloud architectures, we envision a

gigantic self-organizing cloud (SOC) being formed to reap the huge potential of untapped

commodity computing power over the Internet. Toward this new architecture where each

participant may autonomously act as both resource consumer and provider, we propose a fully

distributed, VM-multiplexing resource allocation scheme to manage decentralized resources. Our

approach not only achieves maximized resource utilization using the proportional share model

(PSM), but also delivers provably and adaptively optimal execution efficiency. We also design a

novel multiattribute range query protocol for locating qualified nodes. Contrary to existing

solutions which often generate bulky messages per request, our protocol produces only one

lightweight query message per task on the Content Addressable Network (CAN). It works

effectively to find for each task its qualified resources under a randomized policy that mitigates

the contention among requesters. We show the SOC with our optimized algorithms can make an

improvement by 15-60 percent in system throughput than a P2P Grid model. Our solution also

exhibits fairly high adaptability in a dynamic node-churning environment.


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11. Scalable and Secure Sharing of Personal Health Records in Cloud

Computing Using Attribute-Based Encryption

Abstract :

Personal health record (PHR) is an emerging patient-centric model of health information

exchange, which is often outsourced to be stored at a third party, such as cloud providers.

However, there have been wide privacy concerns as personal health information could be

exposed to those third party servers and to unauthorized parties. To assure the patients’ control

over access to their own PHRs, it is a promising method to encrypt the PHRs before outsourcing.

Yet, issues such as risks of privacy exposure, scalability in key management, flexible access, and

efficient user revocation, have remained the most important challenges toward achieving fine-

grained, cryptographically enforced data access control. In this paper, we propose a novel

patient-centric framework and a suite of mechanisms for data access control to PHRs stored in

semitrusted servers. To achieve fine-grained and scalable data access control for PHRs, we

leverage attribute-based encryption (ABE) techniques to encrypt each patient’s PHR file.

Different from previous works in secure data outsourcing, we focus on the multiple data owner

scenario, and divide the users in the PHR system into multiple security domains that greatly

reduces the key management complexity for owners and users. A high degree of patient privacy

is guaranteed simultaneously by exploiting multiauthority ABE. Our scheme also enables

dynamic modification of access policies or file attributes, supports efficient on-demand

user/attribute revocation and break-glass access under emergency scenarios. Extensive analytical

and experimental results are presented which show the security, scalability, and efficiency of our

proposed scheme.


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PARALLEL AND DISTRIBUTED SYSTEMS

1. A Truthful Dynamic Workflow Scheduling Mechanism for Commercial

Multicloud Environments

Abstract :

The ultimate goal of cloud providers by providing resources is increasing their revenues. This

goal leads to a selfish behavior that negatively affects the users of a commercial multicloud

environment. In this paper, we introduce a pricing model and a truthful mechanism for

scheduling single tasks considering two objectives: monetary cost and completion time. With

respect to the social cost of the mechanism, i.e., minimizing the completion time and monetary

cost, we extend the mechanism for dynamic scheduling of scientific workflows. We theoretically

analyze the truthfulness and the efficiency of the mechanism and present extensive experimental

results showing significant impact of the selfish behavior of the cloud providers on the efficiency

of the whole system. The experiments conducted using real-world and synthetic workflow

applications demonstrate that our solutions dominate in most cases the Pareto-optimal solutions

estimated by two classical multiobjective evolutionary algorithms.

2. Anchor: A Versatile and Efficient Framework for Resource Management

in the Cloud

Abstract :

We present Anchor, a general resource management architecture that uses the stable

matching framework to decouple policies from mechanisms when mapping virtual machines to

physical servers. In Anchor, clients and operators are able to express a variety of distinct

resource management policies as they deem fit, and these policies are captured as preferences in

the stable matching framework. The highlight of Anchor is a new many-to-one stable matching

theory that efficiently matches VMs with heterogeneous resource needs to servers, using both

offline and online algorithms. Our theoretical analyses show the convergence and optimality of


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the algorithm. Our experiments with a prototype implementation on a 20-node server cluster, as

well as large-scale simulations based on real-world workload traces, demonstrate that the

architecture is able to realize a diverse set of policy objectives with good performance and

practicality.

3. A Highly Practical Approach toward Achieving Minimum Data Sets

Storage Cost in the Cloud

Abstract :

Massive computation power and storage capacity of cloud computing systems allow

scientists to deploy computation and data intensive applications without infrastructure

investment, where large application data sets can be stored in the cloud. Based on the pay-as-

you-go model, storage strategies and benchmarking approaches have been developed for cost-

effectively storing large volume of generated application data sets in the cloud. However, they

are either insufficiently cost-effective for the storage or impractical to be used at runtime. In this

paper, toward achieving the minimum cost benchmark, we propose a novel highly costeffective

and practical storage strategy that can automatically decide whether a generated data set should

be stored or not at runtime in the cloud. The main focus of this strategy is the local-optimization

for the tradeoff between computation and storage, while secondarily also taking users’ (optional)

preferences on storage into consideration. Both theoretical analysis and simulations conducted on

general (random) data sets as well as specific real world applications with Amazon’s cost model

show that the costeffectiveness of our strategy is close to or even the same as the minimum cost

benchmark, and the efficiency is very high for practical runtime utilization in the cloud.

4. Toward Fine-Grained, Unsupervised, Scalable Performance Diagnosis for

Production Cloud Computing Systems

Abstract :


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Performance diagnosis is labor intensive in production cloud computing systems. Such

systems typically face many realworld challenges, which the existing diagnosis techniques for

such distributed systems cannot effectively solve. An efficient, unsupervised diagnosis tool for

locating fine-grained performance anomalies is still lacking in production cloud computing

systems. This paper proposes CloudDiag to bridge this gap. Combining a statistical technique

and a fast matrix recovery algorithm, CloudDiag can efficiently pinpoint fine-grained causes of

the performance problems, which does not require any domain-specific knowledge to the target

system. CloudDiag has been applied in a practical production cloud computing systems to

diagnose performance problems. We demonstrate the effectiveness of CloudDiag in three real-

world case studies.

5. Scalable and Accurate Graph Clustering and Community Structure

Detection

Abstract :

One of the most useful measures of cluster quality is the modularity of the partition, which

measures the difference between the number of the edges joining vertices from the same cluster

and the expected number of such edges in a random graph. In this paper, we show that the

problem of finding a partition maximizing the modularity of a given graph G can be reduced to a

minimum weighted cut (MWC) problem on a complete graph with the same vertices as G. We

then show that the resulting minimum cut problem can be efficiently solved by adapting existing

graph partitioning techniques. Our algorithm finds clusterings of a comparable quality and is

much faster than the existing clustering algorithms.

6. Load Rebalancing for Distributed File Systems in Clouds

Abstract :

Distributed file systems are key building blocks for cloud computing applications based on

the MapReduce programming paradigm. In such file systems, nodes simultaneously serve


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computing and storage functions; a file is partitioned into a number of chunks allocated in

distinct nodes so that MapReduce tasks can be performed in parallel over the nodes. However, in

a cloud computing environment, failure is the norm, and nodes may be upgraded, replaced, and

added in the system. Files can also be dynamically created, deleted, and appended. This results in

load imbalance in a distributed file system; that is, the file chunks are not distributed as

uniformly as possible among the nodes. Emerging distributed file systems in production systems

strongly depend on a central node for chunk reallocation. This dependence is clearly inadequate

in a large-scale, failure-prone environment because the central load balancer is put under

considerable workload that is linearly scaled with the system size, and may thus become the

performance bottleneck and the single point of failure. In this paper, a fully distributed load

rebalancing algorithm is presented to cope with the load imbalance problem. Our algorithm is

compared against a centralized approach in a production system and a competing distributed

solution presented in the literature. The simulation results indicate that our proposal is

comparable with the existing centralized approach and considerably outperforms the prior

distributed algorithm in terms of load imbalance factor, movement cost, and algorithmic

overhead. The performance of our proposal implemented in the Hadoop distributed file system is

further investigated in a cluster environment.

7. SPOC: A Secure and Privacy-Preserving Opportunistic Computing

Framework for Mobile-Healthcare Emergency

Abstract :

With the pervasiveness of smart phones and the advance of wireless body sensor networks

(BSNs), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare provider

into a pervasive environment for better health monitoring, has attracted considerable interest

recently. However, the flourish of m-Healthcare still faces many challenges including

information security and privacy preservation. In this paper, we propose a secure and privacy-

preserving opportunistic computing framework, called SPOC, for m-Healthcare emergency.

With SPOC, smart phone resources including computing power and energy can be


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opportunistically gathered to process the computing-intensive personal health information (PHI)

during m-Healthcare emergency with minimal privacy disclosure. In specific, to leverage the

PHI privacy disclosure and the high reliability of PHI process and transmission in m-Healthcare

emergency, we introduce an efficient user-centric privacy access control in SPOC framework,

which is based on an attribute-based access control and a new privacy-preserving scalar product

computation (PPSPC) technique, and allows a medical user to decide who can participate in the

opportunistic computing to assist in processing his overwhelming PHI data. Detailed security

analysis shows that the proposed SPOC framework can efficiently achieve user-centric privacy

access control in m- Healthcare emergency. In addition, performance evaluations via extensive

simulations demonstrate the SPOC’s effectiveness in term of providing high-reliable-PHI

process and transmission while minimizing the privacy disclosure during m-Healthcare

emergency.

8. Improve Efficiency and Reliability in Single-Hop WSNs with Transmit-

Only Nodes

Abstract :

Wireless Sensor Networks (WSNs) will play a significant role at the ―edge‖ of the future

―Internet of Things.‖ In particular, WSNs with transmit-only nodes are attracting more attention

due to their advantages in supporting applications requiring dense and long-lasting deployment at

a very low cost and energy consumption. However, the lack of receivers in transmit-only nodes

renders most existing MAC protocols invalid. Based on our previous study on WSNs with pure

transmit-only nodes, this work proposes a simple, yet cost effective and powerful single-hop

hybrid WSN cluster architecture that contains not only transmit-only nodes but also standard

nodes (with transceivers). Along with the hybrid architecture, this work also proposes a new

MAC layer protocol framework called Robust Asynchronous Resource Estimation (RARE) that

efficiently and reliably manages the densely deployed single-hop hybrid cluster in a self-

organized fashion. Through analysis and extensive simulations, the proposed framework is

shown to meet or exceed the needs of most applications in terms of the data delivery probability,


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QoS differentiation, system capacity, energy consumption, and reliability. To the best of our

knowledge, this work is the first that brings reliable scheduling to WSNs containing both

nonsynchronized transmit-only nodes and standard nodes.

9. Optimal Client-Server Assignment for Internet Distributed Systems

Abstract :

We investigate an underlying mathematical model and algorithms for optimizing the

performance of a class of distributed systems over the Internet. Such a system consists of a large

number of clients who communicate with each other indirectly via a number of intermediate

servers. Optimizing the overall performance of such a system then can be formulated as a client-

server assignment problem whose aim is to assign the clients to the servers in such a way to

satisfy some prespecified requirements on the communication cost and load balancing. We show

that 1) the total communication load and load balancing are two opposing metrics, and

consequently, their tradeoff is inherent in this class of distributed systems; 2) in general, finding

the optimal client-server assignment for some prespecified requirements on the total load and

load balancing is NP-hard, and therefore; 3) we propose a heuristic via relaxed convex

optimization for finding the approximate solution. Our simulation results indicate that the

proposed algorithm produces superior performance than other heuristics, including the popular

Normalized Cuts algorithm.

10. Fast Channel Zapping with Destination-Oriented Multicast for IP Video

Delivery

Abstract :

Channel zapping time is a critical quality of experience (QoE) metric for IP-based video

delivery systems such as IPTV. An interesting zapping acceleration scheme based on time-

shifted subchannels (TSS) was recently proposed, which can ensure a zapping delay bound as


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well as maintain the picture quality during zapping. However, the behaviors of the TSS-based

scheme have not been fully studied yet. Furthermore, the existing TSS-based implementation

adopts the traditional IP multicast, which is not scalable for a large-scale distributed system.

Corresponding to such issues, this paper makes contributions in two aspects. First, we resort to

theoretical analysis to understand the fundamental properties of the TSS-based service model.

We show that there exists an optimal subchannel data rate which minimizes the redundant traffic

transmitted over subchannels. Moreover, we reveal a start-up effect, where the existing operation

pattern in the TSS-based model could violate the zapping delay bound. With a solution proposed

to resolve the start-up effect, we rigorously prove that a zapping delay bound equal to the

subchannel time shift is guaranteed by the updated TSS-based model. Second, we propose a

destination-oriented-multicast (DOM) assisted zapping acceleration (DAZA) scheme for a

scalable TSS-based implementation, where a subscriber can seamlessly migrate from a

subchannel to the main channel after zapping without any control message exchange over the

network. Moreover, the subchannel selection in DAZA is independent of the zapping request

signaling delay, resulting in improved robustness and reduced messaging overhead in a

distributed environment. We implement DAZA in ns-2 and multicast an MPEG-4 video stream

over a practical network topology. Extensive simulation results are presented to demonstrate the

validity of our analysis and DAZA scheme.

11. Cluster-Based Certificate Revocation with Vindication Capability for

Mobile Ad Hoc Networks

Abstract :

Mobile ad hoc networks (MANETs) have attracted much attention due to their mobility and

ease of deployment. However, the wireless and dynamic natures render them more vulnerable to

various types of security attacks than the wired networks. The major challenge is to guarantee

secure network services. To meet this challenge, certificate revocation is an important integral

component to secure network communications. In this paper, we focus on the issue of certificate

revocation to isolate attackers from further participating in network activities. For quick and


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accurate certificate revocation, we propose the Cluster-based Certificate Revocation with

Vindication Capability (CCRVC) scheme. In particular, to improve the reliability of the scheme,

we recover the warned nodes to take part in the certificate revocation process; to enhance the

accuracy, we propose the threshold-based mechanism to assess and vindicate warned nodes as

legitimate nodes or not, before recovering them. The performances of our scheme are evaluated

by both numerical and simulation analysis. Extensive results demonstrate that the proposed

certificate revocation scheme is effective and efficient to guarantee secure communications in

mobile ad hoc networks.

12. A Secure Protocol for Spontaneous Wireless Ad Hoc Networks Creation

Abstract :

This paper presents a secure protocol for spontaneous wireless ad hoc networks which uses

an hybrid symmetric/ asymmetric scheme and the trust between users in order to exchange the

initial data and to exchange the secret keys that will be used to encrypt the data. Trust is based on

the first visual contact between users. Our proposal is a complete self-configured secure protocol

that is able to create the network and share secure services without any infrastructure. The

network allows sharing resources and offering new services among users in a secure

environment. The protocol includes all functions needed to operate without any external support.

We have designed and developed it in devices with limited resources. Network creation stages

are detailed and the communication, protocol messages, and network management are explained.

Our proposal has been implemented in order to test the protocol procedure and performance.

Finally, we compare the protocol with other spontaneous ad hoc network protocols in order to

highlight its features and we provide a security analysis of the system.


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13. Dynamic Resource Allocation Using Virtual Machines for Cloud

Computing Environment

Abstract :

Cloud computing allows business customers to scale up and down their resource usage based

on needs. Many of the touted gains in the cloud model come from resource multiplexing through

virtualization technology. In this paper, we present a system that uses virtualization technology

to allocate data center resources dynamically based on application demands and support green

computing by optimizing the number of servers in use. We introduce the concept of ―skewness‖

to measure the unevenness in the multidimensional resource utilization of a server. By

minimizing skewness, we can combine different types of workloads nicely and improve the

overall utilization of server resources. We develop a set of heuristics that prevent overload in the

system effectively while saving energy used. Trace driven simulation and experiment results

demonstrate that our algorithm achieves good performance.

14. High Performance Resource Allocation Strategies for Computational

Economies

Abstract :

Utility computing models have long been the focus of academic research, and with the recent

success of commercial cloud providers, computation and storage is finally being realized as the

fifth utility. Computational economies are often proposed as an efficient means of resource

allocation, however adoption has been limited due to a lack of performance and high overheads.

In this paper, we address the performance limitations of existing economic allocation models by

defining strategies to reduce the failure and reallocation rate, increase occupancy and thereby

increase the obtainable utilization of the system. The high-performance resource utilization

strategies presented can be used by market participants without requiring dramatic changes to the

allocation protocol. The strategies considered include overbooking, advanced reservation, just-

in-time bidding, and using substitute providers for service delivery. The proposed strategies have


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been implemented in a distributed metascheduler and evaluated with respect to Grid and cloud

deployments. Several diverse synthetic workloads have been used to quantity both the

performance benefits and economic implications of these strategies.

15. A Privacy Leakage Upper Bound Constraint-Based Approach for Cost-

Effective Privacy Preserving of Intermediate Data Sets in Cloud

Abstract :

Cloud computing provides massive computation power and storage capacity which enable

users to deploy computation and data-intensive applications without infrastructure investment.

Along the processing of such applications, a large volume of intermediate data sets will be

generated, and often stored to save the cost of recomputing them. However, preserving the

privacy of intermediate data sets becomes a challenging problem because adversaries may

recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting

ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But

we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because

it is very time consuming and costly for data-intensive applications to en/decrypt data sets

frequently while performing any operation on them. In this paper, we propose a novel upper

bound privacy leakage constraint-based approach to identify which intermediate data sets need to

be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy

requirements of data holders can still be satisfied. Evaluation results demonstrate that the

privacy-preserving cost of intermediate data sets can be significantly reduced with our approach

over existing ones where all data sets are encrypted.


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16. A Secure Payment Scheme with Low Communication and Processing

Overhead for Multihop Wireless Networks

Abstract :

We propose RACE, a report-based payment scheme for multihop wireless networks to

stimulate node cooperation, regulate packet transmission, and enforce fairness. The nodes submit

lightweight payment reports (instead of receipts) to the accounting center (AC) and temporarily

store undeniable security tokens called Evidences. The reports contain the alleged charges and

rewards without security proofs, e.g., signatures. The AC can verify the payment by investigating

the consistency of the reports, and clear the payment of the fair reports with almost no processing

overhead or cryptographic operations. For cheating reports, the Evidences are requested to

identify and evict the cheating nodes that submit incorrect reports. Instead of requesting the

Evidences from all the nodes participating in the cheating reports, RACE can identify the

cheating nodes with requesting few Evidences. Moreover, Evidence aggregation technique is

used to reduce the Evidences’ storage area. Our analytical and simulation results demonstrate

that RACE requires much less communication and processing overhead than the existing receipt-

based schemes with acceptable payment clearance delay and storage area. This is essential for

the effective implementation of a payment scheme because it uses micropayment and the

overhead cost should be much less than the payment value. Moreover, RACE can secure the

payment and precisely identify the cheating nodes without false accusations.

17. Mobi-Sync: Efficient Time Synchronization for Mobile Underwater Sensor

Networks

Abstract :

Time synchronization is an important requirement for many services provided by distributed

networks. A lot of time synchronization protocols have been proposed for terrestrial Wireless

Sensor Networks (WSNs). However, none of them can be directly applied to Underwater Sensor


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Networks (UWSNs). A synchronization algorithm forUWSNs must consider additional factors

such as long propagation delays from the use of acoustic communication and sensor node

mobility. These unique challenges make the accuracy of synchronization procedures for UWSNs

even more critical. Time synchronization solutions specifically designed for UWSNs are needed

to satisfy these new requirements. This paper proposes Mobi-Sync, a novel time synchronization

scheme for mobile underwater sensor networks. Mobi-Sync distinguishes itself from previous

approaches for terrestrial WSN by considering spatial correlation among the mobility patterns of

neighboring UWSNs nodes. This enables Mobi-Sync to accurately estimate the long dynamic

propagation delays. Simulation results show that Mobi-Sync outperforms existing schemes in

both accuracy and energy efficiency.

18. Detection and Localization of Multiple Spoofing Attackers in Wireless

Networks

Abstract :

Wireless spoofing attacks are easy to launch and can significantly impact the performance of

networks. Although the identity of a node can be verified through cryptographic authentication,

conventional security approaches are not always desirable because of their overhead

requirements. In this paper, we propose to use spatial information, a physical property associated

with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting

spoofing attacks; 2) determining the number of attackers when multiple adversaries

masquerading as the same node identity; and 3) localizing multiple adversaries. We propose to

use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to

detect the spoofing attacks. We then formulate the problem of determining the number of

attackers as a multiclass detection problem. Cluster-based mechanisms are developed to

determine the number of attackers. When the training data are available, we explore using the

Support Vector Machines (SVM) method to further improve the accuracy of determining the

number of attackers. In addition, we developed an integrated detection and localization system


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that can localize the positions of multiple attackers. We evaluated our techniques through two

testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real

office buildings. Our experimental results show that our proposed methods can achieve over 90

percent Hit Rate and Precision when determining the number of attackers. Our localization

results using a representative set of algorithms provide strong evidence of high accuracy of

localizing multiple adversaries.

KNOWLEDGE AND DATA ENGINEERING

1. Crowdsourced Trace Similarity with Smartphones

Abstract :

Smartphones are nowadays equipped with a number of sensors, such as WiFi, GPS,

accelerometers, etc. This capability allows smartphone users to easily engage in crowdsourced

computing services, which contribute to the solution of complex problems in a distributed

manner. In this work, we leverage such a computing paradigm to solve efficiently the following

problem: comparing a query trace Q against a crowd of traces generated and stored on

distributed smartphones. Our proposed framework, coined SmartTraceþ, provides an effective

solution without disclosing any part of the crowd traces to the query processor. SmartTraceþ,

relies on an in-situ data storage model and intelligent top-K query processing algorithms that

exploit distributed trajectory similarity measures, resilient to spatial and temporal noise, in order

to derive the most relevant answers to Q. We evaluate our algorithms on both synthetic and real

workloads. We describe our prototype system developed on the Android OS. The solution is

deployed over our own SmartLab testbed of 25 smartphones. Our study reveals that

computations over SmartTraceþ result in substantial energy conservation; in addition, results can

be computed faster than competitive approaches.


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2. Incentive Compatible Privacy-Preserving Data Analysis

Abstract :

In many cases, competing parties who have private data may collaboratively conduct

privacy-preserving distributed data analysis (PPDA) tasks to learn beneficial data models or

analysis results. Most often, the competing parties have different incentives. Although certain

PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is

impossible to verify whether participating parties are truthful about their private input data.

Unless proper incentives are set, current PPDA techniques cannot prevent participating parties

from modifying their private inputs. This raises the question of how to design incentive

compatible privacy-preserving data analysis techniques that motivate participating parties to

provide truthful inputs. In this paper, we first develop key theorems, then base on these

theorems, we analyze certain important privacy-preserving data analysis tasks that could be

conducted in a way that telling the truth is the best choice for any participating party.

3. On Identifying Critical Nuggets of Information during Classification Tasks

Abstract :

In large databases, there may exist critical nuggets—small collections of records or instances

that contain domain-specific important information. This information can be used for future

decision making such as labeling of critical, unlabeled data records and improving classification

results by reducing false positive and false negative errors. This work introduces the idea of

critical nuggets, proposes an innovative domain-independent method to measure criticality,

suggests a heuristic to reduce the search space for finding critical nuggets, and isolates and

validates critical nuggets from some real-world data sets. It seems that only a few subsets may

qualify to be critical nuggets, underlying the importance of finding them. The proposed

methodology can detect them. This work also identifies certain properties of critical nuggets and

provides experimental validation of the properties. Experimental results also helped validate that

critical nuggets can assist in improving classification accuracies in real-world data sets.


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4. Failure-Aware Cascaded Suppression in Wireless Sensor Networks

Abstract :

Wireless sensor networks are widely used to continuously collect data from the environment.

Because of energy constraints on battery-powered nodes, it is critical to minimize

communication. Suppression has been proposed as a way to reduce communication by using

predictive models to suppress reporting of predictable data. However, in the presence of

communication failures, missing data are difficult to interpret because these could have been

either suppressed or lost in transmission. There is no existing solution for handling failures for

general, spatiotemporal suppression that uses cascading. While cascading further reduces

communication, it makes failure handling difficult, because nodes can act on incomplete or

incorrect information and in turn affect other nodes. We propose a cascaded suppression

framework that exploits both temporal and spatial data correlation to reduce communication, and

applies coding theory and Bayesian inference to recover missing data resulted from suppression

and communication failures. Experiment results show that cascaded suppression significantly

reduces communication cost and improves missing data recovery compared to existing

approaches.

5. Optimal Route Queries with Arbitrary Order Constraints

Abstract :

Given a set of spatial points DS, each of which is associated with categorical information,

e.g., restaurant, pub, etc., the optimal route query finds the shortest path that starts from the

query point (e.g., a home or hotel), and covers a user-specified set of categories (e.g., {pub,

restaurant, museum}). The user may also specify partial order constraints between different

categories, e.g., a restaurant must be visited before a pub. Previous work has focused on a special

case where the query contains the total order of all categories to be visited (e.g., museum !

restaurant ! pub). For the general scenario without such a total order, the only known solution


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reduces the problem to multiple, total-order optimal route queries. As we show in this paper, this

naı¨ve approach incurs a significant amount of repeated computations, and, thus, is not scalable

to large data sets. Motivated by this, we propose novel solutions to the general optimal route

query, based on two different methodologies, namely backward search and forward search. In

addition, we discuss how the proposed methods can be adapted to answer a variant of the optimal

route queries, in which the route only needs to cover a subset of the given categories. Extensive

experiments, using both real and synthetic data sets, confirm that the proposed solutions are

efficient and practical, and outperform existing methods by large margins.

6. Co-Occurrence-Based Diffusion for Expert Search on the Web

Abstract :

Expert search has been studied in different contexts, e.g., enterprises, academic communities.

We examine a general expert search problem: searching experts on the web, where millions of

webpages and thousands of names are considered. It has mainly two challenging issues: 1)

webpages could be of varying quality and full of noises; 2) The expertise evidences scattered in

webpages are usually vague and ambiguous. We propose to leverage the large amount of co-

occurrence information to assess relevance and reputation of a person name for a query topic.

The co-occurrence structure is modeled using a hypergraph, on which a heat diffusion based

ranking algorithm is proposed. Query keywords are regarded as heat sources, and a person name

which has strong connection with the query (i.e., frequently co-occur with query keywords and

co-occur with other names related to query keywords) will receive most of the heat, thus being

ranked high. Experiments on the ClueWeb09 web collection show that our algorithm is effective

for retrieving experts and outperforms baseline algorithms significantly. This work would be

regarded as one step toward addressing the more general entity search problem without

sophisticated NLP techniques.


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7. Clustering Uncertain Data Based on Probability Distribution Similarity

Abstract :

Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts

significant challenges on both modeling similarity between uncertain objects and developing

efficient computational methods. The previous methods extend traditional partitioning clustering

methods like k-means and density-based clustering methods like DBSCAN to uncertain data,

thus rely on geometric distances between objects. Such methods cannot handle uncertain objects

that are geometrically indistinguishable, such as products with the same mean but very different

variances in customer ratings. Surprisingly, probability distributions, which are essential

characteristics of uncertain objects, have not been considered in measuring similarity between

uncertain objects. In this paper, we systematically model uncertain objects in both continuous

and discrete domains, where an uncertain object is modeled as a continuous and discrete random

variable, respectively. We use the well-known Kullback-Leibler divergence to measure similarity

between uncertain objects in both the continuous and discrete cases, and integrate it into

partitioning and density-based clustering methods to cluster uncertain objects. Nevertheless, a

naı¨ve implementation is very costly. Particularly, computing exact KL divergence in the

continuous case is very costly or even infeasible. To tackle the problem, we estimate KL

divergence in the continuous case by kernel density estimation and employ the fast Gauss

transform technique to further speed up the computation. Our extensive experiment results verify

the effectiveness, efficiency, and scalability of our approaches.

8. PMSE: A Personalized Mobile Search Engine

Abstract :

We propose a personalized mobile search engine (PMSE) that captures the users' preferences

in the form of concepts by mining their clickthrough data. Due to the importance of location

information in mobile search, PMSE classifies these concepts into content concepts and location

concepts. In addition, users' locations (positioned by GPS) are used to supplement the location


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concepts in PMSE. The user preferences are organized in an ontology-based, multifacet user

profile, which are used to adapt a personalized ranking function for rank adaptation of future

search results. To characterize the diversity of the concepts associated with a query and their

relevances to the user's need, four entropies are introduced to balance the weights between the

content and location facets. Based on the client-server model, we also present a detailed

architecture and design for implementation of PMSE. In our design, the client collects and stores

locally the clickthrough data to protect privacy, whereas heavy tasks such as concept extraction,

training, and reranking are performed at the PMSE server. Moreover, we address the privacy

issue by restricting the information in the user profile exposed to the PMSE server with two

privacy parameters. We prototype PMSE on the Google Android platform. Experimental results

show that PMSE significantly improves the precision comparing to the baseline.

9. Discovering Temporal Change Patterns in the Presence of Taxonomies

Abstract :

Frequent itemset mining is a widely exploratory technique that focuses on discovering

recurrent correlations among data. The steadfast evolution of markets and business environments

prompts the need of data mining algorithms to discover significant correlation changes in order

to reactively suit product and service provision to customer needs. Change mining, in the context

of frequent itemsets, focuses on detecting and reporting significant changes in the set of mined

itemsets from one time period to another. The discovery of frequent generalized itemsets, i.e.,

itemsets that 1) frequently occur in the source data, and 2) provide a high-level abstraction of the

mined knowledge, issues new challenges in the analysis of itemsets that become rare, and thus

are no longer extracted, from a certain point. This paper proposes a novel kind of dynamic

pattern, namely the HIstory GENeralized Pattern (HIGEN), that represents the evolution of an

itemset in consecutive time periods, by reporting the information about its frequent

generalizations characterized by minimal redundancy (i.e., minimum level of abstraction) in case

it becomes infrequent in a certain time period. To address HIGEN mining, it proposes HIGEN

MINER, an algorithm that focuses on avoiding itemset mining followed by postprocessing by

exploiting a support-driven itemset generalization approach. To focus the attention on the


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minimally redundant frequent generalizations and thus reduce the amount of the generated

patterns, the discovery of a smart subset of HIGENs, namely the NONREDUNDANT HIGENs,

is addressed as well. Experiments performed on both real and synthetic datasets show the

efficiency and the effectiveness of the proposed approach as well as its usefulness in a real

application context.

10. Spatial Approximate String Search

Abstract :

This work deals with the approximate string search in large spatial databases. Specifically,

we investigate range queries augmented with a string similarity search predicate in both

euclidean space and road networks. We dub this query the spatial approximate string (SAS)

query. In euclidean space, we propose an approximate solution, the MHR-tree, which embeds

min-wise signatures into an R-tree. The min-wise signature for an index node u keeps a concise

representation of the union of q-grams from strings under the subtree of u. We analyze the

pruning functionality of such signatures based on the set resemblance between the query string

and the q-grams from the subtrees of index nodes. We also discuss how to estimate the

selectivity of a SAS query in euclidean space, for which we present a novel adaptive algorithm to

find balanced partitions using both the spatial and string information stored in the tree. For

queries on road networks, we propose a novel exact method, RSASSOL, which significantly

outperforms the baseline algorithm in practice. The RSASSOL combines the q-gram-based

inverted lists and the reference nodes based pruning. Extensive experiments on large real data

sets demonstrate the efficiency and effectiveness of our approaches.


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11. Robust Module-Based Data Management

Abstract :

The current trend for building an ontology-based data management system (DMS) is to

capitalize on efforts made to design a preexisting well-established DMS (a reference system).

The method amounts to extracting from the reference DMS a piece of schema relevant to the

new application needs—a module—, possibly personalizing it with extra constraints w.r.t. the

application under construction, and then managing a data set using the resulting schema. In this

paper, we extend the existing definitions of modules and we introduce novel properties of

robustness that provide means for checking easily that a robust module-based DMS evolves

safely w.r.t. both the schema and the data of the reference DMS. We carry out our investigations

in the setting of description logics which underlie modern ontology languages, like RDFS, OWL,

and OWL2 from W3C. Notably, we focus on the DL-liteA dialect of the DL-lite family, which

encompasses the foundations of the QL profile of OWL2 (i.e., DL-liteR): the W3C

recommendation for efficiently managing large data sets.

12. Protecting Sensitive Labels in Social Network Data Anonymization

Abstract :

Privacy is one of the major concerns when publishing or sharing social network data for

social science research and business analysis. Recently, researchers have developed privacy

models similar to k-anonymity to prevent node reidentification through structure information.

However, even when these privacy models are enforced, an attacker may still be able to infer

one’s private information if a group of nodes largely share the same sensitive labels (i.e.,

attributes). In other words, the label-node relationship is not well protected by pure structure


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anonymization methods. Furthermore, existing approaches, which rely on edge editing or node

clustering, may significantly alter key graph properties. In this paper, we define a k-degree-l-

diversity anonymity model that considers the protection of structural information as well as

sensitive labels of individuals. We further propose a novel anonymization methodology based on

adding noise nodes. We develop a new algorithm by adding noise nodes into the original graph

with the consideration of introducing the least distortion to graph properties. Most importantly,

we provide a rigorous analysis of the theoretical bounds on the number of noise nodes added and

their impacts on an important graph property. We conduct extensive experiments to evaluate the

effectiveness of the proposed technique.

13. A Proxy-Based Approach to Continuous Location-Based Spatial Queries in

Mobile Environments

Abstract :

Caching valid regions of spatial queries at mobile clients is effective in reducing the number

of queries submitted by mobile clients and query load on the server. However, mobile clients

suffer from longer waiting time for the server to compute valid regions. We propose in this paper

a proxy-based approach to continuous nearest-neighbor (NN) and window queries. The proxy

creates estimated valid regions (EVRs) for mobile clients by exploiting spatial and temporal

locality of spatial queries. For NN queries, we devise two new algorithms to accelerate EVR

growth, leading the proxy to build effective EVRs even when the cache size is small. On the

other hand, we propose to represent the EVRs of window queries in the form of vectors, called

estimated window vectors (EWVs), to achieve larger estimated valid regions. This novel

representation and the associated creation algorithm result in more effective EVRs of window

queries. In addition, due to the distinct characteristics, we use separate index structures, namely

EVR-tree and grid index, for NN queries and window queries, respectively. To further increase

efficiency, we develop algorithms to exploit the results of NN queries to aid grid index growth,


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benefiting EWV creation of window queries. Similarly, the grid index is utilized to support NN

query answering and EVR updating. We conduct several experiments for performance

evaluation. The experimental results show that the proposed approach significantly outperforms

the existing proxy-based approaches.

14. A Fast Clustering-Based Feature Subset Selection Algorithm for High-

Dimensional Data

Abstract :

Feature selection involves identifying a subset of the most useful features that produces

compatible results as the original entire set of features. A feature selection algorithm may be

evaluated from both the efficiency and effectiveness points of view. While the efficiency

concerns the time required to find a subset of features, the effectiveness is related to the

quality of the subset of features. Based on these criteria, a fast clustering-based feature

selection algorithm (FAST) is proposed and experimentally evaluated in this paper. The FAST

algorithm works in two steps. In the first step, features are divided into clusters by using

graph-theoretic clustering methods. In the second step, the most representative feature that is

strongly related to target classes is selected from each cluster to form a subset of features.

Features in different clusters are relatively independent, the clustering-based strategy of FAST

has a high probability of producing a subset of useful and independent features. To ensure the

efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method.

The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical

study. Extensive experiments are carried out to compare FAST and several representative

feature selection algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with

respect to four types of well-known classifiers, namely, the probabilitybased Naive Bayes, the

tree-based C4.5, the instance-based IB1, and the rule-based RIPPER before and after feature

selection. The results, on 35 publicly available real-world high-dimensional image,


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microarray, and text data, demonstrate that the FAST not only produces smaller subsets of

features but also improves the performances of the four types of classifiers.

15. Ranking on Data Manifold with Sink Points

Abstract :

Ranking is an important problem in various applications, such as Information Retrieval (IR),

natural language processing, computational biology, and social sciences. Many ranking

approaches have been proposed to rank objects according to their degrees of relevance or

importance. Beyond these two goals, diversity has also been recognized as a crucial criterion in

ranking. Top ranked results are expected to convey as little redundant information as possible,

and cover as many aspects as possible. However, existing ranking approaches either take no

account of diversity, or handle it separately with some heuristics. In this paper, we introduce a

novel approach, Manifold Ranking with Sink Points (MRSPs), to address diversity as well as

relevance and importance in ranking. Specifically, our approach uses a manifold ranking process

over the data manifold, which can naturally find the most relevant and important data objects.

Meanwhile, by turning ranked objects into sink points on data manifold, we can effectively

prevent redundant objects from receiving a high rank. MRSP not only shows a nice convergence

property, but also has an interesting and satisfying optimization explanation. We applied MRSP

on two application tasks, update summarization and query recommendation, where diversity is of

great concern in ranking. Experimental results on both tasks present a strong empirical

performance of MRSP as compared to existing ranking approaches.


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16. Tweet Analysis for Real-Time Event Detection and Earthquake Reporting

System Development

Abstract :

Twitter has received much attention recently. An important characteristic of Twitter is its

real-time nature. We investigate the real-time interaction of events such as earthquakes in Twitter

and propose an algorithm to monitor tweets and to detect a target event. To detect a target event,

we devise a classifier of tweets based on features such as the keywords in a tweet, the number of

words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the

target event that can find the center of the event location. We regard each Twitter user as a

sensor and apply particle filtering, which are widely used for location estimation. The particle

filter works better than other comparable methods for estimating the locations of target events.

As an application, we develop an earthquake reporting system for use in Japan. Because of the

numerous earthquakes and the large number of Twitter users throughout the country, we can

detect an earthquake with high probability (93 percent of earthquakes of Japan Meteorological

Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our

system detects earthquakes promptly and notification is delivered much faster than JMA

broadcast announcements.

17. Clustering Sentence-Level Text Using a Novel Fuzzy Relational Clustering

Algorithm

Abstract :

In comparison with hard clustering methods, in which a pattern belongs to a single cluster,

fuzzy clustering algorithms allow patterns to belong to all clusters with differing degrees of

membership. This is important in domains such as sentence clustering, since a sentence is likely

to be related to more than one theme or topic present within a document or set of documents.


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However, because most sentence similarity measures do not represent sentences in a common

metric space, conventional fuzzy clustering approaches based on prototypes or mixtures of

Gaussians are generally not applicable to sentence clustering. This paper presents a novel fuzzy

clustering algorithm that operates on relational input data; i.e., data in the form of a square

matrix of pairwise similarities between data objects. The algorithm uses a graph representation of

the data, and operates in an Expectation-Maximization framework in which the graph centrality

of an object in the graph is interpreted as a likelihood. Results of applying the algorithm to

sentence clustering tasks demonstrate that the algorithm is capable of identifying overlapping

clusters of semantically related sentences, and that it is therefore of potential use in a variety of

text mining tasks. We also include results of applying the algorithm to benchmark data sets in

several other domains.

18. Distributed Processing of Probabilistic Top-k Queries in Wireless Sensor

Networks

Abstract :

In this paper, we introduce the notion of sufficient set and necessary set for distributed

processing of probabilistic top-k queries in cluster-based wireless sensor networks. These two

concepts have very nice properties that can facilitate localized data pruning in clusters.

Accordingly, we develop a suite of algorithms, namely, sufficient set-based (SSB), necessary set-

based (NSB), and boundary-based (BB), for intercluster query processing with bounded rounds

of communications. Moreover, in responding to dynamic changes of data distribution in the

network, we develop an adaptive algorithm that dynamically switches among the three proposed

algorithms to minimize the transmission cost. We show the applicability of sufficient set and

necessary set to wireless sensor networks with both two-tier hierarchical and tree-structured

network topologies. Experimental results show that the proposed algorithms reduce data

transmissions significantly and incur only small constant rounds of data communications. The


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experimental results also demonstrate the superiority of the adaptive algorithm, which achieves a

near-optimal performance under various conditions.

19. A Survey of XML Tree Patterns

Abstract :

With XML becoming a ubiquitous language for data interoperability purposes in various

domains, efficiently querying XML data is a critical issue. This has lead to the design of

algebraic frameworks based on tree-shaped patterns akin to the tree-structured data model of

XML. Tree patterns are graphic representations of queries over data trees. They are actually

matched against an input data tree to answer a query. Since the turn of the 21st century, an

astounding research effort has been focusing on tree pattern models and matching optimization

(a primordial issue). This paper is a comprehensive survey of these topics, in which we outline

and compare the various features of tree patterns. We also review and discuss the two main

families of approaches for optimizing tree pattern matching, namely pattern tree minimization

and holistic matching. We finally present actual tree pattern-based developments, to provide a

global overview of this significant research topic.


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20. Automatic Semantic Content Extraction in Videos Using a Fuzzy Ontology

and Rule-Based Model

Abstract :

Recent increase in the use of video-based applications has revealed the need for extracting

the content in videos. Raw data and low-level features alone are not sufficient to fulfill the user

’s needs; that is, a deeper understanding of the content at the semantic level is required.

Currently, manual techniques, which are inefficient, subjective and costly in time and limit the

querying capabilities, are being used to bridge the gap between low-level representative features

and high-level semantic content. Here, we propose a semantic content extraction system that

allows the user to query and retrieve objects, events, and concepts that are extracted

automatically. We introduce an ontology-based fuzzy video semantic content model that uses

spatial/temporal relations in event and concept definitions. This metaontology definition

provides a wide-domain applicable rule construction standard that allows the user to construct an

ontology for a given domain. In addition to domain ontologies, we use additional rule definitions

(without using ontology) to lower spatial relation computation cost and to be able to define some

complex situations more effectively. The proposed framework has been fully implemented and

tested on three different domains. We have obtained satisfactory precision and recall rates for

object, event and concept extraction.


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NETWORK AND SECURITY

1. A Distributed Control Law for Load Balancing in Content Delivery

Networks

Abstract :

In this paper, we face the challenging issue of defining and implementing an effective law for

load balancing in Content Delivery Networks (CDNs). We base our proposal on a formal study

of a CDN system, carried out through the exploitation of a fluid flow model characterization of

the network of servers. Starting from such characterization, we derive and prove a lemma about

the network queues equilibrium. This result is then leveraged in order to devise a novel

distributed and time-continuous algorithm for load balancing, which is also reformulated in a

time-discrete version. The discrete formulation of the proposed balancing law is eventually

discussed in terms of its actual implementation in a real-world scenario. Finally, the overall

approach is validated by means of simulations.

2. A Low-Complexity Congestion Control and Scheduling Algorithm for

Multihop Wireless Networks With Order-Optimal Per-Flow Delay

Abstract :

Quantifying the end-to-end delay performance in multihop wireless networks is a well-

known challenging problem. In this paper, we propose a new joint congestion control and

scheduling algorithm for multihop wireless networks with fixed-route flows operated under a

general interference model with interference degree . Our proposed algorithm not only achieves a


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provable throughput guarantee (which is close to at least of the system capacity region), but also

leads to explicit upper bounds on the end-to-end delay of every flow. Our end-to-end delay and

throughput bounds are in simple and closed forms, and they explicitly quantify the tradeoff

between throughput and delay of every flow. Furthermore, the per-flow end-to-end delay bound

increases linearly with the number of hops that the flow passes through, which is order-optimal

with respect to the number of hops. Unlike traditional solutions based on the back-pressure

algorithm, our proposed algorithm combines window-based flow control with a new rate-based

distributed scheduling algorithm. A key contribution of our work is to use a novel stochastic

dominance approach to bound the corresponding per-flow throughput and delay, which

otherwise are often intractable in these types of systems. Our proposed algorithm is fully

distributed and requires a low per-node complexity that does not increase with the network size.

Hence, it can be easily implemented in practice.

3. A Utility Maximization Framework for Fair and Efficient Multicasting in

Multicarrier Wireless Cellular Networks

Abstract :

Multicast/broadcast is regarded as an efficient technique for wireless cellular networks to

transmit a large volume of common data to multiple mobile users simultaneously. To guarantee

the quality of service for each mobile user in such single-hop multicasting, the base-station

transmitter usually adapts its data rate to the worst channel condition among all users in a

multicast group. On one hand, increasing the number of users in a multicast group leads to a

more efficient utilization of spectrum bandwidth, as users in the same group can be served

together. On the other hand, too many users in a group may lead to unacceptably low data rate at

which the base station can transmit. Hence, a natural question that arises is how to efficiently and

fairly transmit to a large number of users requiring the same message. This paper endeavors to

answer this question by studying the problem of multicasting over multicarriers in wireless

orthogonal frequency division multiplexing (OFDM) cellular systems. Using a unified utility


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maximization framework, we investigate this problem in two typical scenarios: namely, when

users experience roughly equal path losses and when they experience different path losses,

respectively. Through theoretical analysis, we obtain optimal multicast schemes satisfying

various throughput-fairness requirements in these two cases. In particular, we show that the

conventional multicast scheme is optimal in the equal-path-loss case regardless of the utility

function adopted. When users experience different path losses, the group multicast scheme,

which divides the users almost equally into many multicast groups and multicasts to different

groups of users over nonoverlapping subcarriers, is optimal.

4. ABC: Adaptive Binary Cuttings for Multidimensional Packet

Classification

Abstract :

Decision tree-based packet classification algorithms are easy to implement and allow the

tradeoff between storage and throughput. However, the memory consumption of these

algorithms remains quite high when high throughput is required. The Adaptive Binary Cuttings

(ABC) algorithm exploits another degree of freedom to make the decision tree adapt to the

geometric distribution of the filters. The three variations of the adaptive cutting procedure

produce a set of different-sized cuts at each decision step, with the goal to balance the

distribution of filters and to reduce the filter duplication effect. The ABC algorithm uses stronger

and more straightforward criteria for decision tree construction. Coupled with an efficient node

encoding scheme, it enables a smaller, shorter, and well-balanced decision tree. The hardware-

oriented implementation of each variation is proposed and evaluated extensively to demonstrate

its scalability and sensitivity to different configurations. The results show that the ABC

algorithm significantly outperforms the other decision tree-based algorithms. It can sustain more

than 10-Gb/s throughput and is the only algorithm among the existing well-known packet

classification algorithms that can compete with TCAMs in terms of the storage efficiency.


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5. Achieving Efficient Flooding by Utilizing Link Correlation in Wireless

Sensor Networks

Abstract :

Although existing flooding protocols can provide efficient and reliable communication in

wireless sensor networks on some level, further performance improvement has been hampered

by the assumption of link independence, which requires costly acknowledgments (ACKs) from

every receiver. In this paper, we present collective flooding (CF), which exploits the link

correlation to achieve flooding reliability using the concept of collective ACKs. CF requires only

1-hop information at each node, making the design highly distributed and scalable with low

complexity. We evaluate CF extensively in real-world settings, using three different types of

testbeds: a single-hop network with 20 MICAz nodes, a multihop network with 37 nodes, and a

linear outdoor network with 48 nodes along a 326-m-long bridge. System evaluation and

extensive simulation show that CF achieves the same reliability as state-of-the-art solutions

while reducing the total number of packet transmission and the dissemination delay by 30%–

50% and 35%–50%, respectively.

6. An Empirical Interference Modeling for Link Reliability Assessment in

Wireless Networks

Abstract :

In recent years, it has been widely believed in the community that the link reliability is

strongly related to received signal strength indicator (RSSI) [or signal-to-interference-plus-noise

ratio (SINR)] and external interference makes it unpredictable, which is different from the

previous understanding that there is no tight relationship between the link reliability and RSSI


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(or SINR), but multipath fading causes the unpredictability. However, both cannot fully explain

why the unpredictability appears in the link state. In this paper, we unravel the following

questions: 1) What causes frame losses that are directly related to intermediate link states? 2) Is

RSSI or SINR a right criterion to represent the link reliability? 3) Is there a better measure to

assess the link reliability? We first configured a testbed for performing a real measurement study

to identify the causes of frame losses, and observed that link reliability depends on an intraframe

SINR distribution, not a single value of RSSI (or SINR). We also learned that an RSSI value is

not always a good indicator to estimate the link state. We then conducted a further investigation

on the intraframe SINR distribution and the relationship between the SINR and link reliability

with the ns-2 simulator. Based on these results, we finally propose an interference modeling

framework for estimating link states in the presence of wireless interferences. We envision that

the framework can be used for developing link-aware protocols to achieve their optimal

performance in a hostile wireless environment.

7. Back-Pressure-Based Packet-by-Packet Adaptive Routing in

Communication Networks

Abstract :

Back-pressure-based adaptive routing algorithms where each packet is routed along a

possibly different path have been extensively studied in the literature. However, such algorithms

typically result in poor delay performance and involve high implementation complexity. In this

paper, we develop a new adaptive routing algorithm built upon the widely studied back-pressure

algorithm. We decouple the routing and scheduling components of the algorithm by designing a

probabilistic routing table that is used to route packets to per-destination queues. The scheduling

decisions in the case of wireless networks are made using counters called shadow queues. The

results are also extended to the case of networks that employ simple forms of network coding. In

that case, our algorithm provides a low-complexity solution to optimally exploit the routing–

coding tradeoff.


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8. Centralized and Distributed Protocols for Tracker-Based Dynamic Swarm

Management

Abstract :

With BitTorrent, efficient peer upload utilization is achieved by splitting contents into many

small pieces, each of which may be downloaded from different peers within the same swarm.

Unfortunately, piece and bandwidth availability may cause the file-sharing efficiency to degrade

in small swarms with few participating peers. Using extensive measurements, we identi- fied

hundreds of thousands of torrents with several small swarms for which reallocating peers among

swarms and/or modifying the peer behavior could significantly improve the system performance.

Motivated by this observation, we propose a centralized and a distributed protocol for dynamic

swarm management. The centralized protocol (CSM) manages the swarms of peers at minimal

tracker overhead. The distributed protocol (DSM) manages the swarms of peers while ensuring

load fairness among the trackers. Both protocols achieve their performance improvements by

identifying and merging small swarms and allow load sharing for large torrents. Our evaluations

are based on measurement data collected during eight days from over 700 trackers worldwide,

which collectively maintain state information about 2.8 million unique torrents. We find that

CSM and DSM can achieve most of the performance gains of dynamic swarm management.

These gains are estimated to be up to 40% on average for small torrents.


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9. Combined Optimal Control of Activation and Transmission in Delay-

Tolerant Networks

Abstract :

Performance of a delay-tolerant network has strong dependence on the nodes participating in

data transportation. Such networks often face several resource constraints especially related to

energy. Energy is consumed not only in data transmission, but also in listening and in several

signaling activities. On one hand these activities enhance the system’s performance while on the

other hand, they consume a significant amount of energy even when they do not involve actual

node transmission. Accordingly, in order to use energy efficiently, one may have to limit not

only the amount of transmissions, but also the amount of nodes that are active at each time.

Therefore, we study two coupled problems: 1) the activation problem that determines when a

mobile will turn on in order to receive packets; and 2) the problemof regulating the beaconing.

We derive optimal energy management strategies by formulating the problem as an optimal

control one, which we then explicitly solve. We also validate our findings through extensive

simulations that are based on contact traces.

10. Complexity Analysis and Algorithm Design for Advance Bandwidth

Scheduling in Dedicated Networks

Abstract :

An increasing number of high-performance networks provision dedicated channels through

circuit switching or MPLS/GMPLS techniques to support large data transfer. The link

bandwidths in such networks are typically shared by multiple users through advance reservation,

resulting in varying bandwidth availability in future time. Developing efficient scheduling

algorithms for advance bandwidth reservation has become a critical task to improve the


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utilization of network resources and meet the transport requirements of application users. We

consider an exhaustive combination of different path and bandwidth constraints and formulate

four types of advance bandwidth scheduling problems, with the same objective to minimize the

data transfer end time for a given transfer request with a prespecified data size: 1) fixed path with

fixed bandwidth (FPFB); 2) fixed path with variable bandwidth (FPVB); 3) variable path with

fixed bandwidth (VPFB); and 4) variable path with variable bandwidth (VPVB). For VPFB and

VPVB, we further consider two subcases where the path switching delay is negligible or

nonnegligible. We propose an optimal algorithm for each of these scheduling problems except

for FPVB and VPVB with nonnegligible path switching delay, which are proven to be NP-

complete and nonapproximable, and then tackled by heuristics. The performance superiority of

these heuristics is verified by extensive experimental results in a large set of simulated networks

in comparison to optimal and greedy strategies.

11. Distortion-Aware Scalable Video Streaming to Multinetwork Clients

Abstract :

We consider the problem of scalable video streaming from a server to multinetwork clients

over heterogeneous access networks, with the goal of minimizing the distortion of the received

videos. This problem has numerous applications including: 1) mobile devices connecting to

multiple licensed and ISM bands, and 2) cognitive multiradio devices employing spectrum

bonding. In this paper, we ascertain how to optimally determine which video packets to transmit

over each access network. We present models to capture the network conditions and video

characteristics and develop an integer program for deterministic packet scheduling. Solving the

integer program exactly is typically not computationally tractable, so we develop heuristic

algorithms for deterministic packet scheduling, as well as convex optimization problems for

randomized packet scheduling. We carry out a thorough study of the tradeoff between

performance and computational complexity and propose a convex programming-based algorithm


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that yields good performance while being suitable for real-time applications. We conduct

extensive trace-driven simulations to evaluate the proposed algorithms using real network

conditions and scalable video streams. The simulation results show that the proposed convex

programming-based algorithm: 1) outperforms the rate control algorithms defined in the

Datagram Congestion Control Protocol (DCCP) by about 10–15 dB higher video quality; 2)

reduces average delivery delay by over 90% compared to DCCP; 3) results in higher average

video quality of 4.47 and 1.92 dB than the two developed heuristics; 4) runs efficiently, up to six

times faster than the best-performing heuristic; and 5) does indeed provide service differentiation

among users.

12. Efficient Algorithms for Neighbor Discovery in Wireless Networks

Abstract :

Neighbor discovery is an important first step in the initialization of a wireless ad hoc

network. In this paper, we design and analyze several algorithms for neighbor discovery in

wireless networks. Starting with a single-hop wireless network of nodes, we propose a ALOHA-

like neighbor discovery algorithm when nodes cannot detect collisions, and an order-optimal

receiver feedback-based algorithm when nodes can detect collisions. Our algorithms neither

require nodes to have a priori estimates of the number of neighbors nor synchronization between

nodes. Our algorithms allow nodes to begin execution at different time instants and to terminate

neighbor discovery upon discovering all their neighbors. We finally show that receiver feedback

can be used to achieve a running time, even when nodes cannot detect collisions. We then

analyze neighbor discovery in a general multihop setting. We establish an upper bound of on the

running time of the ALOHA-like algorithm, where denotes the maximum node degree in the

network and the total number of nodes. We also establish a lower bound of on the running time

of any randomized neighbor discovery algorithm. Our result thus implies that the ALOHA-like

algorithm is at most a factor worse than optimal.


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13. Exploring the Design Space of Multichannel Peer-to-Peer Live Video

Streaming Systems

Abstract :

Most of the commercial peer-to-peer (P2P) video streaming deployments support hundreds

of channels and are referred to as multichannel systems. Recent research studies have proposed

specific protocols to improve the streaming quality for all channels by enabling cross-channel

cooperation among multiple channels. In this paper, we focus on the following fundamental

problems in designing cooperating multichannel systems: 1) what are the general characteristics

of existing and potential designs? and 2) under what circumstances should a particular design be

used to achieve the desired streaming quality with the lowest implementation complexity? To

answer the first question, we propose simple models based on linear programming and network-

flow graphs for three general designs, namely Naive Bandwidth allocation Approach (NBA),

Passive Channel-aware bandwidth allocation Approach (PCA), and Active Channel-aware

bandwidth allocation Approach (ACA), which provide insight into understanding the key

characteristics of cross-channel resource sharing. For the second question, we first develop

closed-form results for two-channel systems. Then, we use extensive numerical simulations to

compare the three designs for various peer population distributions, upload bandwidth

distributions, and channel structures. Our analytical and simulation results show that: 1) the NBA

design can rarely achieve the desired streaming quality in general cases; 2) the PCA design can

achieve the same performance as the ACA design in general cases; and 3) the ACA design

should be used for special applications.


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14. Fast Transmission to Remote Cooperative Groups: A New Key

Management Paradigm

Abstract :

The problem of efficiently and securely broadcasting to a remote cooperative group occurs in

many newly emerging networks. A major challenge in devising such systems is to overcome the

obstacles of the potentially limited communication from the group to the sender, the

unavailability of a fully trusted key generation center, and the dynamics of the sender. The

existing key management paradigms cannot deal with these challenges effectively. In this paper,

we circumvent these obstacles and close this gap by proposing a novel key management

paradigm. The new paradigm is a hybrid of traditional broadcast encryption and group key

agreement. In such a system, each member maintains a single public/secret key pair. Upon

seeing the public keys of the members, a remote sender can securely broadcast to any intended

subgroup chosen in an ad hoc way. Following this model, we instantiate a scheme that is proven

secure in the standard model. Even if all the nonintended members collude, they cannot extract

any useful information from the transmitted messages. After the public group encryption key is

extracted, both the computation overhead and the communication cost are independent of the

group size. Furthermore, our scheme facilitates simple yet efficient member deletion/ addition

and flexible rekeying strategies. Its strong security against collusion, its constant overhead, and

its implementation friendliness without relying on a fully trusted authority render our protocol a

very promising solution to many applications.


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15. Geographic Routing in -Dimensional Spaces With Guaranteed Delivery

and Low Stretch

Abstract :

Almost all geographic routing protocols have been designed for 2-D. We present a novel

geographic routing protocol, named Multihop Delaunay Triangulation (MDT), for 2-D, 3-D, and

higher dimensions with these properties: 1) guaranteed delivery for any connected graph of

nodes and physical links, and 2) low routing stretch from efficient forwarding of packets out of

local minima. The guaranteed delivery property holds for node locations specified by accurate,

inaccurate, or arbitrary coordinates. The MDT protocol suite includes a packet forwarding

protocol together with protocols for nodes to construct and maintain a distributed MDT for

routing. We present the performance of MDT protocols in 3-D and 4-D as well as performance

comparisons of MDT routing versus representative geographic routing protocols for nodes in 2-

D and 3-D. Experimental results show that MDT provides the lowest routing stretch in the

comparisons. Furthermore, MDT protocols are specially designed to handle churn, i.e., dynamic

topology changes due to addition and deletion of nodes and links. Experimental results show that

MDT’s routing success rate is close to 100% during churn, and node states converge quickly to a

correct MDT after churn.

16. ICTCP: Incast Congestion Control for TCP in Data-Center Networks

Abstract :

Transport Control Protocol (TCP) incast congestion happens in high-bandwidth and low-

latency networks when multiple synchronized servers send data to the same receiver in parallel.

For many important data-center applications such as MapReduce and Search, this many-to-one

traffic pattern is common. Hence TCP incast congestion may severely degrade their


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performances, e.g., by increasing response time. In this paper, we study TCP incast in detail by

focusing on the relationships between TCP throughput, round-trip time (RTT), and receive

window. Unlike previous approaches, which mitigate the impact of TCP incast congestion by

using a fine-grained timeout value, our idea is to design an Incast congestion Control for TCP

(ICTCP) scheme on the receiver side. In particular, our method adjusts the TCP receive window

proactively before packet loss occurs. The implementation and experiments in our testbed

demonstrate that we achieve almost zero timeouts and high goodput for TCP incast.

17. Optimal Content Placement for Peer-to-Peer Video-on-Demand Systems

Abstract :

In this paper, we address the problem of content placement in peer-to-peer (P2P) systems,

with the objective of maximizing the utilization of peers’ uplink bandwidth resources. We

consider system performance under a many-user asymptotic. We distinguish two scenarios,

namely ―Distributed Server Networks‖ (DSNs) for which requests are exogenous to the system,

and ―Pure P2P Networks‖ (PP2PNs) for which requests emanate from the peers themselves. For

both scenarios, we consider a loss network model of performance and determine asymptotically

optimal content placement strategies in the case of a limited content catalog. We then turn to an

alternative ―large catalog‖ scaling where the catalog size scales with the peer population. Under

this scaling, we establish that storage space per peer must necessarily grow unboundedly if

bandwidth utilization is to be maximized. Relating the system performance to properties of a

specific random graph model, we then identify a content placement strategy and a request

acceptance policy that jointly maximize bandwidth utilization, provided storage space per peer

grows unboundedly, although arbitrarily slowly, with system size.


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18. Peer-Assisted Social Media Streaming with Social Reciprocity

Abstract :

Online video sharing and social networking are cross-pollinating rapidly in today’s Internet:

Online social network users are sharing more and more media contents among each other, while

online video sharing sites are leveraging social connections among users to promote their videos.

An intriguing development as it is, the operational challenge in previous video sharing systems

persists, i.e., the large server cost demanded for scaling of the systems. Peer-to-peer video

sharing could be a rescue, only if the video viewers’ mutual resource contribution has been fully

incentivized and efficiently scheduled. Exploring the unique advantages of a social network

based video sharing system, we advocate to utilize social reciprocities among peers with social

relationships for efficient contribution incentivization and scheduling, so as to enable high-

quality video streaming with low server cost. We exploit social reciprocity with two give-and-

take ratios at each peer: (1) peer contribution ratio (PCR), which evaluates the reciprocity level

between a pair of social friends, and (2) system contribution ratio (SCR), which records the give-

and-take level of the user to and from the entire system. We design efficient peer-to-peer

mechanisms for video streaming using the two ratios, where each user optimally decides which

other users to seek relay help from and help in relaying video streams, respectively, based on

combined evaluations of their social relationship and historical reciprocity levels. Our design

achieves effective incentives for resource contribution, load balancing among relay peers, as well

as efficient social-aware resource scheduling. We also discuss practical implementation and

implement our design in a prototype social media sharing system. Our extensive evaluations

based on PlanetLab experiments verify that high-quality large-scale social media sharing can be

achieved with conservative server costs.


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19. Pricing-Based Decentralized Spectrum Access Control in Cognitive Radio

Networks

Abstract :

This paper investigates pricing-based spectrum access control in cognitive radio networks,

where primary users (PUs) sell the temporarily unused spectrum and secondary users (SUs)

compete via random access for such spectrum opportunities. Compared to existing market-based

approaches with centralized scheduling, pricing-based spectrum management with random

access provides a platform for SUs contending for spectrum access and is amenable to

decentralized implementation due to its low complexity. We focus on two market models, one

with a monopoly PU market and the other with a multiple-PU market. For the monopoly PU

market model, we devise decentralized pricing-based spectrum access mechanisms that enable

SUs to contend for channel usage. Specifically, we first consider SUs contending via slotted

Aloha. Since the revenue maximization problem therein is nonconvex, we characterize the

corresponding Pareto-optimal region and obtain a Pareto-optimal solution that maximizes the

SUs’ throughput subject to their budget constraints. To mitigate the spectrum underutilization

due to the ―price of contention,‖ we revisit the problem where SUs contend via CSMA, which

results in more efficient spectrum utilization and higher revenue. We then study the tradeoff

between the PU’s utility and its revenue when the PU’s salable spectrum is controllable. Next,

for the multiple-PU market model, we cast the competition among PUs as a three-stage

Stackelberg game, where each SU selects a PU’s channel to maximize its throughput. We

explore the existence and the uniqueness of Nash equilibrium, in terms of access prices and the

spectrum offered to SUs, and develop an iterative algorithm for strategy adaptation to achieve

the Nash equilibrium. Our findings reveal that there exists a unique Nash equilibrium when the

number of PUs is less than a threshold determined by the budgets and elasticity of SUs.


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20. QoS Guarantees and Service Differentiation for Dynamic Cloud

Applications

Abstract :

Cloud elasticity allows dynamic resource provisioning in concert with actual application

demands. Feedback control approaches have been applied with success to resource allocation in

physical servers. However, cloud dynamics make the design of an accurate and stable resource

controller challenging, especially when application-level performance is considered as the

measured output. Application-level performance is highly dependent on the characteristics of

workload and sensitive to cloud dynamics. To address these challenges, we extend a selftuning

fuzzy control (STFC) approach, originally developed for response time assurance in web servers

to resource allocation in virtualized environments. We introduce mechanisms for adaptive output

amplification and flexible rule selection in the STFC approach for better adaptability and

stability. Based on the STFC, we further design a two-layer QoS provisioning framework,

DynaQoS, that supports adaptive multi-objective resource allocation and service differentiation.

We implement a prototype of DynaQoS on a Xen-based cloud testbed. Experimental results on

representative server workloads show that STFC outperforms popular controllers such as

Kalman filter, ARMA and, Adaptive PI in the control of CPU, memory, and disk bandwidth

resources under both static and dynamic workloads. Further results with multiple control

objectives and service classes demonstrate the effectiveness of DynaQoS in performance-power

control and service differentiation.


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21. Quantifying and Verifying Reachability for Access Controlled Networks

Abstract :

Quantifying and querying network reachability is important for security monitoring and

auditing as well as many aspects of network management such as troubleshooting, maintenance,

and design. Although attempts to model network reachability have been made, feasible solutions

to computing network reachability have remained unknown. In this paper, we propose a suite of

algorithms for quantifying reachability based on network configurations [mainly Access Control

Lists (ACLs)] as well as solutions for querying network reachability. We present a network

reachability model that considers connectionless and connection- oriented transport protocols,

stateless and stateful routers/ firewalls, static and dynamic NAT, PAT, IP tunneling, etc. We

implemented the algorithms in our network reachability tool called Quarnet and conducted

experiments on a university network. Experimental results show that the offline computation of

reachability matrices takes a few hours, and the online processing of a reachability query takes

0.075 s on average.

22. Rake: Semantics Assisted Network-Based Tracing Framework

Abstract :

The ability to trace request execution paths is critical for diagnosing performance faults in

large-scale distributed systems. Previous black-box and white-box approaches are either

inaccurate or invasive. We present a novel semantics-assisted gray-box tracing approach, called

Rake, which can accurately trace individual request by observing network traffic. Rake infers the

causality between messages by identifying polymorphic IDs in messages according to

application semantics. To make Rake universally applicable, we design a Rake language so that

users can easily describe necessary semantics of their applications while reusing the core Rake


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component. We evaluate Rake using a few popular distributed applications, including web

search, distributed computing cluster, content provider network, and online chatting. Our results

demonstrate Rake is much more accurate than the black-box approaches while requiring no

modification to OS/applications. In the CoralCDN (a content distributed network) experiments,

Rake links messages with much higher accuracy than WAP5, a state-of-the-art blackbox

approach. In the Hadoop (a distributed computing cluster platform) experiments, Rake helps

reveal several previously unknown issues that may lead to performance degradation, including a

RPC (Remote Procedure Call) abusing problem.

23. Semi-Random Backoff: Towards Resource Reservation for Channel Access

in Wireless LANs

Abstract :

This paper proposes a semi-random backoff (SRB) method that enables resource reservation

in contention-based wireless LANs. The proposed SRB is fundamentally different from

traditional random backoff methods because it provides an easy migration path from random

backoffs to deterministic slot assignments. The central idea of the SRB is for the wireless station

to set its backoff counter to a deterministic value upon a successful packet transmission. This

deterministic value will allow the station to reuse the time-slot in consecutive backoff cycles.

When multiple stations with successful packet transmissions reuse their respective time-slots, the

collision probability is reduced, and the channel achieves the equivalence of resource

reservation. In case of a failed packet transmission, a station will revert to the standard random

backoff method and probe for a new available time-slot. The proposed SRB method can be

readily applied to both 802.11 DCF and 802.11e EDCA networks with minimum modification to

the existing DCF/EDCA implementations. Theoretical analysis and simulation results validate

the superior performance of the SRB for small-scale and heavily loaded wireless LANs. When

combined with an adaptive mechanism and a persistent backoff process, SRB can also be

effective for large-scale and lightly loaded wireless networks.


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24. Throughput-Optimal Scheduling in Multihop Wireless Networks Without

Per-Flow Information

Abstract :

In this paper, we consider the problem of link scheduling in multihop wireless networks

under general interference constraints. Our goal is to design scheduling schemes that do not use

per-flow or per-destination information, maintain a single data queue for each link, and exploit

only local information, while guaranteeing throughput optimality. Although the celebrated back-

pressure algorithm maximizes throughput, it requires per-flow or per-destination information. It

is usually difficult to obtain and maintain this type of information, especially in large networks,

where there are numerous flows. Also, the back-pressure algorithm maintains a complex data

structure at each node, keeps exchanging queue-length information among neighboring nodes,

and commonly results in poor delay performance. In this paper, we propose scheduling schemes

that can circumvent these drawbacks and guarantee throughput optimality. These schemes use

either the readily available hop-count information or only the local information for each link. We

rigorously analyze the performance of the proposed schemes using fluid limit techniques via an

inductive argument and show that they are throughput-optimal. We also conduct simulations to

validate our theoretical results in various settings and show that the proposed schemes can

substantially improve the delay performance in most scenarios.

25. Delay-Based Network Utility Maximization

Abstract :

It is well known that max-weight policies based on a queue backlog index can be used to

stabilize stochastic networks, and that similar stability results hold if a delay index is used. Using

Lyapunov optimization, we extend this analysis to design a utility maximizing algorithm that


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uses explicit delay information from the head-of-line packet at each user. The resulting policy is

shown to ensure deterministic worst-case delay guarantees and to yield a throughput utility that

differs from the optimally fair value by an amount that is inversely proportional to the delay

guarantee. Our results hold for a general class of 1-hop networks, including packet switches and

multiuser wireless systems with time-varying reliability.

26. Topology Control for Effective Interference Cancellation in Multiuser

MIMO Networks

Abstract :

In multiuser multiple-input–multiple-output (MIMO) networks, receivers decode multiple

concurrent signals using successive interference cancellation (SIC). With SIC, a weak target

signal can be deciphered in the presence of stronger interfering signals. However, this is only

feasible if each strong interfering signal satisfies a signal-to-noise-plus-interference ratio (SINR)

requirement. This necessitates the appropriate selection of a subset of links that can be

concurrently active in each receiver’s neighborhood; in other words, a subtopology consisting of

links that can be simultaneously active in the network is to be formed. If the selected

subtopologies are of small size, the delay between the transmission opportunities on a link

increases. Thus, care should be taken to form a limited number of subtopologies. We find that

the problem of constructing the minimum number of subtopologies such that SIC decoding is

successful with a desired probability threshold is NP-hard. Given this, we propose MUSIC, a

framework that greedily forms and activates subtopologies in a way that favors successful SIC

decoding with a high probability. MUSIC also ensures that the number of selected subtopologies

is kept small. We provide both a centralized and a distributed version of our framework. We

prove that our centralized version approximates the optimal solution for the considered

problem.We also perform extensive simulations to demonstrate that: 1)MUSIC forms a small

number of subtopologies that enable efficient SIC operations; the number of subtopologies


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formed is at most 17% larger than the optimum number of topologies, discovered through

exhaustive search (in small networks); 2) MUSIC outperforms approaches that simply consider

the number of antennas as a measure for determining the links that can be simultaneously active.

Specifically, MUSIC provides throughput improvements of up to four times, as compared to

such an approach, in various topological settings. The improvements can be directly attributable

to a significantly higher probability of correct SIC based decoding with MUSIC.

27. Localization of Wireless Sensor Networks in the Wild: Pursuit of Ranging

Quality

Abstract :

Localization is a fundamental issue of wireless sensor networks that has been extensively

studied in the literature. Our real-world experience from GreenOrbs, a sensor network system

deployed in a forest, shows that localization in the wild remains very challenging due to various

interfering factors. In this paper, we propose CDL, a Combined and Differentiated Localization

approach for localization that exploits the strength of range-free approaches and range-based

approaches using received signal strength indicator (RSSI). A critical observation is that ranging

quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our

method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware

calibration. We have implemented and evaluated CDL by extensive real-world experiments in

GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that

CDL outperforms current state-of-art localization approaches with a more accurate and

consistent performance. For example, the average location error using CDL in GreenOrbs system

is 2.9 m, while the previous best method SISR has an average error of 4.6 m.


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28. Torrents on Twitter: Explore Long-Term Social Relationships in Peer-to-

Peer Systems

Abstract :

Peer-to-peer file sharing systems, most notably Bit- Torrent (BT), have achieved tremendous

success among Internet users. Recent studies suggest that the long-term relationships among BT

peers can be explored to enhance the downloading performance; for example, for re-sharing

previously downloaded contents or for effectively collaborating among the peers. However,

whether such relationships do exist in real world remains unclear. In this paper, we take a first

step towards the real-world applicability of peers’ long-term relationship through a measurement

based study. We find that 95% peers cannot even meet each other again in the BT networks;

therefore, most peers can hardly be organized for further cooperation. This result contradicts to

the conventional understanding based on the observed daily arrival pattern in peer-to-peer

networks. To better understand this, we revisit the arrival of BT peers as well as their longrange

dependence. We find that the peers’ arrival patterns are highly diverse; only a limited number of

stable peers have clear self-similar and periodic daily arrivals patterns. The arrivals of most peers

are, however, quite random with little evidence of long-range dependence. To better utilize these

stable peers, we start to explore peers’ long-term relationships in specific swarms instead of

conventional BT networks. Fortunately, we find that the peers in Twitter-initialized torrents have

stronger temporal locality, thus offering great opportunity for improving their degree of sharing.

Our PlanetLab experiments further indicate that the incorporation of social relations remarkably

accelerates the download completion time. The improvement remains noticeable even in a hybrid

system with a small set of social friends only.


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AFFECTIVE COMPUTING

1. Predicting Emotional Responses to Long Informal Text

Abstract :

Most sentiment analysis approaches deal with binary or ordinal prediction of affective states

(e.g., positive versus negative) on review-related content from the perspective of the author. The

present work focuses on predicting the emotional responses of online communication in

nonreview social media on a real-valued scale on the two affective dimensions of valence and

arousal. For this, a new dataset is introduced, together with a detailed description of the process

that was followed to create it. Important phenomena such as correlations between different

affective dimensions and intercoder agreement are thoroughly discussed and analyzed. Various

methodologies for automatically predicting those states are also presented and evaluated. The

results show that the prediction of intricate emotional states is possible, obtaining at best a

correlation of 0.89 for valence and 0.42 for arousal with the human assigned assessments.

2. Analyses of a Multimodal Spontaneous Facial Expression Database

Abstract :

Creating a large and natural facial expression database is a prerequisite for facial expression

analysis and classification. It is, however, not only time consuming but also difficult to capture

an adequately large number of spontaneous facial expression images and their meanings because

no standard, uniform, and exact measurements are available for database collection and

annotation. Thus, comprehensive first-hand data analyses of a spontaneous expression database

may provide insight for future research on database construction, expression recognition, and

emotion inference. This paper presents our analyses of a multimodal spontaneous facial


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expression database of natural visible and infrared facial expressions (NVIE). First, the

effectiveness of emotion-eliciting videos in the database collection is analyzed with the mean

and variance of the subjects’ self-reported data. Second, an interrater reliability analysis of

raters’ subjective evaluations for apex expression images and sequences is conducted using

Kappa and Kendall’s coefficients. Third, we propose a matching rate matrix to explore the

agreements between displayed spontaneous expressions and felt affective states. Lastly, the

thermal differences between the posed and spontaneous facial expressions are analyzed using a

pairedsamples t-test. The results of these analyses demonstrate the effectiveness of our emotion-

inducing experimental design, the gender difference in emotional responses, and the coexistence

of multiple emotions/expressions. Facial image sequences are more informative than apex

images for both expression and emotion recognition. Labeling an expression image or sequence

with multiple categories together with their intensities could be a better approach than labeling

the expression image or sequence with one dominant category. The results also demonstrate both

the importance of facial expressions as a means of communication to convey affective states and

the diversity of the displayed manifestations of felt emotions. There are indeed some significant

differences between the temperature difference data of most posed and spontaneous facial

expressions, many of which are found in the forehead and cheek regions.

3. Facial Expression Recognition in the Encrypted Domain Based on Local

Fisher Discriminant Analysis

Abstract :

Facial expression recognition forms a critical capability desired by human-interacting

systems that aim to be responsive to variations in the human’s emotional state. Recent trends

toward cloud computing and outsourcing has led to the requirement for facial expression

recognition to be performed remotely by potentially untrusted servers. This paper presents a

system that addresses the challenge of performing facial expression recognition when the test

image is in the encrypted domain. More specifically, to the best of our knowledge, this is the first

known result that performs facial expression recognition in the encrypted domain. Such a system


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solves the problem of needing to trust servers since the test image for facial expression

recognition can remain in encrypted form at all times without needing any decryption, even

during the expression recognition process. Our experimental results on popular JAFFE and MUG

facial expression databases demonstrate that recognition rate of up to 95.24 percent can be

achieved even in the encrypted domain.

4. Modeling Arousal Phases in Daily Living Using Wearable Sensors

Abstract :

In this work, we introduce methods for studying psychological arousal in naturalistic daily

living. We present an activityaware arousal phase modeling approach that incorporates the

additional heart rate (AHR) algorithm to estimate arousal onsets (activations) in the presence of

physical activity (PA). In particular, our method filters spurious PA-induced activations from

AHR activations, e.g., caused by changes in body posture, using activity primitive patterns and

their distributions. Furthermore, our approach includes algorithms for estimating arousal duration

and intensity, which are key to arousal assessment. We analyzed the modeling procedure in a

participant study with 180 h of unconstrained daily life recordings using a multimodal wearable

system comprising two acceleration sensors, a heart rate monitor, and a belt computer. We show

how participants’ sensor-based arousal phase estimations can be evaluated in relation to daily

activity and self-report information. For example, participant-specific arousal was frequently

estimated during conversations and yielded highest intensities during office work. We believe

that our activity-aware arousal modeling can be used to investigate personal arousal

characteristics and introduce novel options for studying human behavior in daily living.


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SECURE COMPUTING

1. A System for Timely and Controlled Information Sharing in Emergency

Situations

Abstract :

During natural disasters or emergency situations, an essential requirement for an effective

emergency management is the information sharing. In this paper, we present an access control

model to enforce controlled information sharing in emergency situations. An in-depth analysis of

the model is discussed throughout the paper, and administration policies are introduced to

enhance the model flexibility during emergencies. Moreover, a prototype implementation and

experiments results are provided showing the efficiency and scalability of the system.

2. WARNINGBIRD: A Near Real-Time Detection System For Suspicious

Urls In Twitter Stream

Abstract :

Twitter is prone to malicious tweets containing URLs for spam, phishing, and malware

distribution. Conventional Twitter spam detection schemes utilize account features such as the

ratio of tweets containing URLs and the account creation date, or relation features in the Twitter

graph. These detection schemes are ineffective against feature fabrications or consume much

time and resources. Conventional suspicious URL detection schemes utilize several features

including lexical features of URLs, URL redirection, HTML content, and dynamic behavior.

However, evading techniques such as time-based evasion and crawler evasion exist. In this

paper, we propose WARNINGBIRD, a suspicious URL detection system for Twitter. Our


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system investigates correlations of URL redirect chains extracted from several tweets. Because

attackers have limited resources and usually reuse them, their URL redirect chains frequently

share the same URLs. We develop methods to discover correlated URL redirect chains using the

frequently shared URLs and to determine their suspiciousness. We collect numerous tweets from

the Twitter public timeline and build a statistical classifier using them. Evaluation results show

that our classifier accurately and efficiently detects suspicious URLs. We also present

WARNINGBIRD as a near real-time system for classifying suspicious URLs in the Twitter

stream.

3. Location-Aware and Safer Cards: Enhancing RFID Security and Privacy

via Location Sensing

Abstract :

In this paper, we report on a new approach for enhancing security and privacy in certain

RFID applications whereby location or location-related information (such as speed) can serve as

a legitimate access context. Examples of these applications include access cards, toll cards, credit

cards, and other payment tokens. We show that location awareness can be used by both tags and

back-end servers for defending against unauthorized reading and relay attacks on RFID systems.

On the tag side, we design a location-aware selective unlocking mechanism using which tags can

selectively respond to reader interrogations rather than doing so promiscuously. On the server

side, we design a location-aware secure transaction verification scheme that allows a bank server

to decide whether to approve or deny a payment transaction and detect a specific type of relay

attack involving malicious readers. The premise of our work is a current technological

advancement that can enable RFID tags with low-cost location (GPS) sensing capabilities.

Unlike prior research on this subject, our defenses do not rely on auxiliary devices or require any

explicit user involvement.


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4. Malware Clearance for Secure Commitment of OS-Level Virtual Machines

Abstract :

A virtual machine(VM) can be simply created upon use and disposed upon the completion of

the tasks or the detection of error. The disadvantage of this approach is that if there is no

malicious activity, the user has to redo all of the work in her actual workspace since there is no

easy way to commit (i.e., merge) only the benign updates within the VM back to the host

environment. In this work, we develop a VM commitment system called Secom to automatically

eliminate malicious state changes when merging the contents of an OS-level VM to the host.

Secom consists of three steps: grouping state changes into clusters, distinguishing between

benign and malicious clusters, and committing benign clusters. Secom has three novel features.

First, instead of relying on a huge volume of log data, it leverages OS-level information flow and

malware behavior information to recognize malicious changes. As a result, the approach imposes

a smaller performance overhead. Second, different from existing intrusion detection and

recovery systems that detect compromised OS objects one by one, Secom classifies objects into

clusters and then identifies malicious objects on a cluster by cluster basis. Third, to reduce the

false-positive rate when identifying malicious clusters, it simultaneously considers two malware

behaviors that are of different types and the origin of the processes that exhibit these behaviors,

rather than considers a single behavior alone as done by existing malware detection methods. We

have successfully implemented Secom on the feather-weight virtual machine system, a

Windows-based OS-level virtualization system. Experiments show that the prototype can

effectively eliminate malicious state changes while committing a VM with small performance

degradation. Moreover, compared with the commercial antimalware tools, the Secom prototype

has a smaller number of false negatives and thus can more thoroughly clean up malware side

effects. In addition, the number of false positives of the Secom prototype is also lower than that

achieved by the online behavior-based approach of the commercial tools.


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5. Predicting Architectural Vulnerability on Multithreaded Processors under

Resource Contention and Sharing

Abstract :

Architectural vulnerability factor (AVF) characterizes a processor’s vulnerability to soft

errors. Interthread resource contention and sharing on a multithreaded processor (e.g., SMT,

CMP) shows nonuniform impact on a program’s AVF when it is co-scheduled with different

programs. However, measuring the AVF is extremely expensive in terms of hardware and

computation. This paper proposes a scalable two-level predictive mechanism capable of

predicting a program’s AVF on a SMT/CMP architecture from easily measured metrics.

Essentially, the first-level model correlates the AVF in a contention-free environment with

important performance metrics and the processor configuration, while the second-level model

captures the interthread resource contention and sharing via processor structures’ occupancies.

By utilizing the proposed scheme, we can accurately estimate any unseen program’s soft error

vulnerability under resource contention and sharing with any other program(s), on an arbitrarily

configured multithreaded processor. In practice, the proposed model can be used to find soft

error resilient thread-to-core scheduling for multithreaded processors.


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6. SORT: A Self-Organizing Trust Model for Peer-to-Peer Systems

Abstract :

Open nature of peer-to-peer systems exposes them to malicious activity. Building trust

relationships among peers can mitigate attacks of malicious peers. This paper presents

distributed algorithms that enable a peer to reason about trustworthiness of other peers based on

past interactions and recommendations. Peers create their own trust network in their proximity

by using local information available and do not try to learn global trust information. Two

contexts of trust, service, and recommendation contexts, are defined to measure trustworthiness

in providing services and giving recommendations. Interactions and recommendations are

evaluated based on importance, recentness, and peer satisfaction parameters. Additionally,

recommender’s trustworthiness and confidence about a recommendation are considered while

evaluating recommendations. Simulation experiments on a file sharing application show that the

proposed model can mitigate attacks on 16 different malicious behavior models. In the

experiments, good peers were able to form trust relationships in their proximity and isolate

malicious peers.


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IMAGE PROCESSING

1. General Framework to Histogram-Shifting-Based Reversible Data Hiding

Abstract :

Histogram shifting (HS) is a useful technique of reversible data hiding (RDH).With HS-

based RDH, high capacity and low distortion can be achieved efficiently. In this paper, we revisit

the HS technique and present a general framework to construct HS-based RDH. By the proposed

framework, one can get a RDH algorithm by simply designing the so-called shifting and

embedding functions. Moreover, by taking specific shifting and embedding functions, we show

that several RDH algorithms reported in the literature are special cases of this general

construction. In addition, two novel and efficient RDH algorithms are also introduced to further

demonstrate the universality and applicability of our framework. It is expected that more

efficient RDH algorithms can be devised according to the proposed framework by carefully

designing the shifting and embedding functions.

2. Robust Ellipse Fitting Based on Sparse Combination of Data Points

Abstract :

Ellipse fitting is widely applied in the fields of computer vision and automatic industry

control, in which the procedure of ellipse fitting often follows the preprocessing step of edge

detection in the original image. Therefore, the ellipse fitting method also depends on the

accuracy of edge detection besides their own performance, especially due to the introduced

outliers and edge point errors from edge detection which will cause severe performance

degradation. In this paper, we develop a robust ellipse fitting method to alleviate the influence of

outliers. The proposed algorithm solves ellipse parameters by linearly combining a subset of


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(―more accurate‖) data points (formed from edge points) rather than all data points (which

contain possible outliers). In addition, considering that squaring the fitting residuals can magnify

the contributions of these extreme data points, our algorithm replaces it with the absolute

residuals to reduce this influence. Moreover, the norm of data point errors is bounded, and the

worst case performance optimization is formed to be robust against data point errors. The

resulting mixed l1–l2 optimization problem is further derived as a secondorder cone

programming one and solved by the computationally efficient interior-point methods. Note that

the fitting approach developed in this paper specifically deals with the overdetermined system,

whereas the current sparse representation theory is only applied to underdetermined systems.

Therefore, the proposed algorithm can be looked upon as an extended application and

development of the sparse representation theory. Some simulated and experimental examples are

presented to illustrate the effectiveness of the proposed ellipse fitting approach.

3. Computationally Tractable Stochastic Image Modeling Based on

Symmetric Markov Mesh Random Fields

Abstract :

In this paper, the properties of a new class of causal Markov random fields, named

symmetric Markov mesh random field, are initially discussed. It is shown that the symmetric

Markov mesh random fields from the upper corners are equivalent to the symmetric Markov

mesh random fields from the lower corners. Based on this new random field, a symmetric,

corner-independent, and isotropic image model is then derived which incorporates the

dependency of a pixel on all its neighbors. The introduced image model comprises the product of

several local 1D density and 2D joint density functions of pixels in an image thus making it

computationally tractable and practically feasible by allowing the use of histogram and joint

histogram approximations to estimate the model parameters. An image restoration application is

also presented to confirm the effectiveness of the model developed. The experimental results


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demonstrate that this new model provides an improved tool for image modeling purposes

compared to the conventional Markov random field models.

4. A Robust Method for Rotation Estimation Using Spherical Harmonics

Representation

Abstract :

This paper presents a robust method for 3D object rotation estimation using spherical

harmonics representation and the unit quaternion vector. The proposed method provides a

closed-form solution for rotation estimation without recurrence relations or searching for point

correspondences between two objects. The rotation estimation problem is casted as a

minimization problem, which finds the optimum rotation angles between two objects of interest

in the frequency domain. The optimum rotation angles are obtained by calculating the unit

quaternion vector from a symmetric matrix, which is constructed from the two sets of spherical

harmonics coefficients using eigendecomposition technique. Our experimental results on

hundreds of 3D objects show that our proposed method is very accurate in rotation estimation,

robust to noisy data, missing surface points, and can handle intra-class variability between 3D

objects.

5. Detecting, Grouping, and Structure Inference for Invariant Repetitive

Patterns in Images

Abstract :

The efficient and robust extraction of invariant patterns from an image is a long-standing

problem in computer vision. Invariant structures are often related to repetitive or near-repetitive

patterns. The perception of repetitive patterns in an image is strongly linked to the visual


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interpretation and composition of textures. Repetitive patterns are products of both repetitive

structures as well as repetitive reflections or color patterns. In other words, patterns that exhibit

nearstationary behavior provide rich information about objects, their shapes, and their texture in

an image. In this paper, we propose a new algorithm for repetitive pattern detection and

grouping. The algorithm follows the classical region growing image segmentation scheme. It

utilizes a mean-shift-like dynamic to group local image patches into clusters. It exploits a

continuous joint alignment to: 1) match similar patches, and 2) refine the subspace grouping. We

also propose an algorithm for inferring the composition structure of the repetitive patterns. The

inference algorithm constructs a data-driven structural completion field, which merges the

detected repetitive patterns into specific global geometric structures. The result of higher level

grouping for image patterns can be used to infer the geometry of objects and estimate the general

layout of a crowded scene.

6. Action Recognition from Video Using Feature Covariance Matrices

Abstract :

We propose a general framework for fast and accurate recognition of actions in video using

empirical covariance matrices of features. A dense set of spatio-temporal feature vectors are

computed from video to provide a localized description of the action, and subsequently

aggregated in an empirical covariance matrix to compactly represent the action. Two supervised

learning methods for action recognition are developed using feature covariance matrices.

Common to both methods is the transformation of the classification problem in the closed

convex cone of covariance matrices into an equivalent problem in the vector space of symmetric

matrices via the matrix logarithm. The first method applies nearest-neighbor classification using

a suitable Riemannian metric for covariance matrices. The second method approximates the

logarithm of a query covariance matrix by a sparse linear combination of the logarithms of

training covariance matrices. The action label is then determined from the sparse coefficients.

Both methods achieve state-of-the-art classification performance on several datasets, and are

robust to action variability, viewpoint changes, and low object resolution. The proposed


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framework is conceptually simple and has low storage and computational requirements making it

attractive for real-time implementation.

7. Local Directional Number Pattern for Face Analysis: Face and Expression

Recognition

Abstract :

This paper proposes a novel local feature descriptor, local directional number pattern (LDN),

for face analysis, i.e., face and expression recognition. LDN encodes the directional information

of the face’s textures (i.e., the texture’s structure) in a compact way, producing a more

discriminative code than current methods. We compute the structure of each micro-pattern with

the aid of a compass mask that extracts directional information, and we encode such information

using the prominent direction indices (directional numbers) and sign—which allows us to

distinguish among similar structural patterns that have different intensity transitions. We divide

the face into several regions, and extract the distribution of the LDN features from them. Then,

we concatenate these features into a feature vector, and we use it as a face descriptor. We

perform several experiments in which our descriptor performs consistently under illumination,

noise, expression, and time lapse variations. Moreover, we test our descriptor with different

masks to analyze its performance in different face analysis tasks.

8. Optimized 3D Watermarking for Minimal Surface Distortion

Abstract :

This paper proposes a new approach to 3D watermarking by ensuring the optimal

preservation of mesh surfaces. A new 3D surface preservation function metric is defined

consisting of the distance of a vertex displaced by watermarking to the original surface, to the

watermarked object surface as well as the actual vertex displacement. The proposed method is


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statistical, blind, and robust. Minimal surface distortion according to the proposed function

metric is enforced during the statistical watermark embedding stage using Levenberg–

Marquardt optimization method. A study of the watermark code crypto-security is provided for

the proposed methodology. According to the experimental results, the proposed methodology has

high robustness against the common mesh attacks while preserving the original object surface

during watermarking.

9. Robust Radial Face Detection for Omnidirectional Vision

Abstract :

Bio-inspired and non-conventional vision systems are highly researched topics. Among them,

omnidirectional vision systems have demonstrated their ability to significantly improve the

geometrical interpretation of scenes. However, few researchers have investigated how to perform

object detection with such systems. The existing approaches require a geometrical transformation

prior to the interpretation of the picture. In this paper, we investigate what must be taken into

account and how to process omnidirectional images provided by the sensor. We focus our

research on face detection and highlight the fact that particular attention should be paid to the

descriptors in order to successfully perform face detection on omnidirectional images. We

demonstrate that this choice is critical to obtaining high detection rates. Our results imply that the

adaptation of existing object-detection frameworks, designed for perspective images, should be

focused on the choice of appropriate image descriptors in the design of the object-detection

pipeline.


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10. Noise Reduction Based on Partial-Reference, Dual-Tree Complex Wavelet

Transform Shrinkage

Abstract :

This paper presents a novel way to reduce noise introduced or exacerbated by image

enhancement methods, in particular algorithms based on the random spray sampling technique,

but not only. According to the nature of sprays, output images of spray-based methods tend to

exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the

statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is

considered to be either free of noise or affected by non-perceivable levels of noise. Taking

advantage of the higher sensitivity of the human visual system to changes in brightness, the

analysis can be limited to the luma channel of both the non-enhanced and enhanced image. Also,

given the importance of directional content in human vision, the analysis is performed through

the dual-tree complex wavelet transform (DTWCT). Unlike the discrete wavelet transform, the

DTWCT allows for distinction of data directionality in the transform space. For each level of the

transform, the standard deviation of the non-enhanced image coefficients is computed across the

six orientations of the DTWCT, then it is normalized. The result is a map of the directional

structures present in the non-enhanced image. Said map is then used to shrink the coefficients of

the enhanced image. The shrunk coefficients and the coefficients from the non-enhanced image

are then mixed according to data directionality. Finally, a noise-reduced version of the enhanced

image is computed via the inverse transforms. A thorough numerical analysis of the results has

been performed in order to confirm the validity of the proposed approach.


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11. Design of Low-Complexity High-Performance Wavelet Filters for Image

Analysis

Abstract :

This paper addresses the construction of a family of wavelets based on halfband polynomials.

An algorithm is proposed that ensures maximum zeros at ω = π for a desired length of analysis

and synthesis filters. We start with the coefficients of the polynomial (x + 1)n and then use a

generalized matrix formulation method to construct the filter halfband polynomial. The designed

wavelets are efficient and give acceptable levels of peak signal-to-noise ratio when used for

image compression. Furthermore, these wavelets give satisfactory recognition rates when used

for feature extraction. Simulation results show that the designed wavelets are effective and more

efficient than the existing standard wavelets.

12. Wavelet Bayesian Network Image Denoising

Abstract :

From the perspective of the Bayesian approach, the denoising problem is essentially a prior

probability modeling and estimation task. In this paper, we propose an approach that exploits a

hidden Bayesian network, constructed from wavelet coefficients, to model the prior probability

of the original image. Then, we use the belief propagation (BP) algorithm, which estimates a

coefficient based on all the coefficients of an image, as the maximum-a-posterior (MAP)

estimator to derive the denoised wavelet coefficients. We show that if the network is a spanning

tree, the standard BP algorithm can perform MAP estimation efficiently. Our experiment results

demonstrate that, in terms of the peak-signal-to-noise-ratio and perceptual quality, the proposed

approach outperforms state-of-the-art algorithms on several images, particularly in the textured

regions, with various amounts of white Gaussian noise.


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13. Blur and Illumination Robust Face Recognition via Set-Theoretic

Characterization

Abstract :

We address the problem of unconstrained face recognition from remotely acquired images.

The main factors that make this problem challenging are image degradation due to blur, and

appearance variations due to illumination and pose. In this paper, we address the problems of

blur and illumination. We show that the set of all images obtained by blurring a given image

forms a convex set. Based on this settheoretic characterization, we propose a blur-robust

algorithm whose main step involves solving simple convex optimization problems. We do not

assume any parametric form for the blur kernels, however, if this information is available it can

be easily incorporated into our algorithm. Furthermore, using the lowdimensional model for

illumination variations, we show that the set of all images obtained from a face image by

blurring it and by changing the illumination conditions forms a bi-convex set. Based on this

characterization, we propose a blur and illuminationrobust algorithm. Our experiments on a

challenging real dataset obtained in uncontrolled settings illustrate the importance of jointly

modeling blur and illumination.

14. View-Based Discriminative Probabilistic Modeling for 3D Object Retrieval

and Recognition

Abstract :

In view-based 3D object retrieval and recognition, each object is described by multiple

views. A central problem is how to estimate the distance between two objects. Most conventional

methods integrate the distances of view pairs across two objects as an estimation of their

distance. In this paper, we propose a discriminative probabilistic object modeling approach. It


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builds probabilistic models for each object based on the distribution of its views, and the distance

between two objects is defined as the upper bound of the Kullback–Leibler divergence of the

corresponding probabilistic models. 3D object retrieval and recognition is accomplished based

on the distance measures. We first learn models for each object by the adaptation from a set of

global models with a maximum likelihood principle. A further adaption step is then performed to

enhance the discriminative ability of the models. We conduct experiments on the ETH 3D object

dataset, the National Taiwan University 3D model dataset, and the Princeton Shape Benchmark.

We compare our approach with different methods, and experimental results demonstrate the

superiority of our approach.

15. Context-Aware Sparse Decomposition for Image Denoising and Super-

Resolution

Abstract :

Image prior models based on sparse and redundant representations are attracting more and

more attention in the field of image restoration. The conventional sparsity-based methods

enforce sparsity prior on small image patches independently. Unfortunately, these works

neglected the contextual information between sparse representations of neighboring image

patches. It limits the modeling capability of sparsity-based image prior, especially when the

major structural information of the source image is lost in the following serious degradation

process. In this paper, we utilize the contextual information of local patches (denoted as context-

aware sparsity prior) to enhance the performance of sparsity-based restoration method. In

addition, a unified framework based on the markov random fields model is proposed to tune the

local prior into a global one to deal with arbitrary size images. An iterative numerical solution is

presented to solve the joint problem of model parameters estimation and sparse recovery.

Finally, the experimental results on image denoising and super-resolution demonstrate the

effectiveness and robustness of the proposed context-aware method.


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16. Learning the Spherical Harmonic Features for 3-D Face Recognition

Abstract :

In this paper, a competitive method for 3-D face recognition (FR) using spherical harmonic

features (SHF) is proposed. With this solution, 3-D face models are characterized by the energies

contained in spherical harmonics with different frequencies, thereby enabling the capture of both

gross shape and fine surface details of a 3-D facial surface. This is in clear contrast to most 3-D

FR techniques which are either holistic or feature based, using local features extracted from

distinctive points. First, 3-D face models are represented in a canonical representation, namely,

spherical depth map, by which SHF can be calculated. Then, considering the predictive

contribution of each SHF feature, especially in the presence of facial expression and occlusion,

feature selection methods are used to improve the predictive performance and provide faster and

more cost-effective predictors. Experiments have been carried out on three public 3-D face

datasets, SHREC2007, FRGC v2.0, and Bosphorus, with increasing difficulties in terms of facial

expression, pose, and occlusion, and which demonstrate the effectiveness of the proposed

method.

17. Rate-Distortion Optimized Rate Control for Depth Map-Based 3-D Video

Coding

Abstract :

In this paper, a novel rate control scheme with optimized bits allocation for the 3-D video

coding is proposed. First, we investigate the R-D characteristics of the texture and depth map of

the coded view, as well as the quality dependency between the virtual view and the coded view.

Second, an optimal bit allocation scheme is developed to allocate target bits for both the texture

and depth maps of different views. Meanwhile, a simplified model parameter estimation scheme


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is adopted to speed up the coding process. Finally, the experimental results on various 3-D video

sequences demonstrate that the proposed algorithm achieves excellent R-D efficiency and bit rate

accuracy compared to benchmark algorithms.

18. Adaptive Fingerprint Image Enhancement With Emphasis on

Preprocessing of Data

Abstract :

This article proposes several improvements to an adaptive fingerprint enhancement method

that is based on contextual filtering. The term adaptive implies that parameters of the method are

automatically adjusted based on the input fingerprint image. Five processing blocks comprise the

adaptive fingerprint enhancement method, where four of these blocks are updated in our

proposed system. Hence, the proposed overall system is novel. The four updated processing

blocks are: 1) preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the

preprocessing and local analysis blocks, a nonlinear dynamic range adjustment method is used.

In the global analysis and matched filtering blocks, different forms of order statistical filters are

applied. These processing blocks yield an improved and new adaptive fingerprint image

processing method. The performance of the updated processing blocks is presented in the

evaluation part of this paper. The algorithm is evaluated toward the NIST developed NBIS

software for fingerprint recognition on FVC databases.

19. Image Noise Level Estimation by Principal Component Analysis

Abstract :

The problem of blind noise level estimation arises in many image processing applications,

such as denoising, compression, and segmentation. In this paper, we propose a new noise level

estimation method on the basis of principal component analysis of image blocks. We show that


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the noise variance can be estimated as the smallest eigenvalue of the image block covariance

matrix. Compared with 13 existing methods, the proposed approach shows a good compromise

between speed and accuracy. It is at least 15 times faster than methods with similar accuracy, and

it is at least two times more accurate than other methods. Our method does not assume the

existence of homogeneous areas in the input image and, hence, can successfully process images

containing only textures.

20. LLSURE: Local Linear SURE-Based Edge-Preserving Image Filtering

Abstract :

In this paper, we propose a novel approach for performing high-quality edge-preserving

image filtering. Based on a local linear model and using the principle of Stein’s unbiased risk

estimate as an estimator for the mean squared error from the noisy image only, we derive a

simple explicit image filter which can filter out noise while preserving edges and fine-scale

details. Moreover, this filter has a fast and exact linear-time algorithm whose computational

complexity is independent of the filtering kernel size; thus, it can be applied to real time image

processing tasks. The experimental results demonstrate the effectiveness of the new filter for

various computer vision applications, including noise reduction, detail smoothing and

enhancement, high dynamic range compression, and flash/no- flash denoising.


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21. Visually Lossless Encoding for JPEG2000

Abstract :

Due to exponential growth in image sizes, visually lossless coding is increasingly being

considered as an alternative to numerically lossless coding, which has limited compression

ratios. This paper presents a method of encoding color images in a visually lossless manner using

JPEG2000. In order to hide coding artifacts caused by quantization, visibility thresholds (VTs)

are measured and used for quantization of subband signals in JPEG2000. The VTs are

experimentally determined from statistically modeled quantization distortion, which is based on

the distribution of wavelet coefficients and the dead-zone quantizer of JPEG2000. The resulting

VTs are adjusted for locally changing backgrounds through a visual masking model, and then

used to determine the minimum number of coding passes to be included in the final codestream

for visually lossless quality under the desired viewing conditions. Codestreams produced by this

scheme are fully JPEG2000 Part-I compliant.

22. Adaptive Markov Random Fields for Joint Unmixing and Segmentation of

Hyperspectral Images

Abstract :

Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of

decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with

their corresponding proportions (or abundances). Endmember extraction algorithms can be

employed for recovering the spectral signatures while abundances are estimated using an


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inversion step. Recent works have shown that exploiting spatial dependencies between image

pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to

model these spatial correlations and partition the image into multiple classes with homogeneous

abundances. This paper proposes to define the MRF sites using similarity regions. These regions

are built using a self-complementary area filter that stems from the morphological theory. This

kind of filter divides the original image into flat zones where the underlying pixels have the same

spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is

proposed to estimate the abundances, the class labels, the noise variance, and the corresponding

hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the

corresponding posterior distribution of the unknown parameters and hyperparameters.

Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of

the algorithm.

23. Efficient Image Classification via Multiple Rank Regression

Abstract :

The problem of image classification has aroused considerable research interest in the field of

image processing. Traditional methods often convert an image to a vector and then use a vector-

based classifier. In this paper, a novel multiple rank regression model (MRR) for matrix data

classification is proposed. Unlike traditional vector-based methods, we employ multiple-rank left

projecting vectors and right projecting vectors to regress each matrix data set to its label for each

category. The convergence behavior, initialization, computational complexity, and parameter

determination are also analyzed. Compared with vector-based regression methods, MRR

achieves higher accuracy and has lower computational complexity. Compared with traditional

supervised tensor-based methods, MRR performs better for matrix data classification. Promising

experimental results on face, object, and hand-written digit image classification tasks are

provided to show the effectiveness of our method.


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24. Separable Markov Random Field Model and Its Applications in Low Level

Vision

Abstract :

This brief proposes a continuously-valued Markov random field (MRF) model with separable

filter bank, denoted as MRFSepa, which significantly reduces the computational complexity in

the MRF modeling. In this framework, we design a novel gradient-based discriminative learning

method to learn the potential functions and separable filter banks. We learn MRFSepa models

with 2-D and 3-D separable filter banks for the applications of gray-scale/color image denoising

and color image demosaicing. By implementing MRFSepa model on graphics processing unit,

we achieve real-time image denoising and fast image demosaicing with high-quality results.

WEB SERVICES

1. Effective Message-Sequence Generation for Testing BPEL Programs

Abstract :

With the popularity of Web Services and Service-Oriented Architecture (SOA), quality

assurance of SOA applications, such as testing, has become a research focus. Programs

implemented by the Business Process Execution Language for Web Services (WS-BPEL), which

can be used to compose partner Web Services into composite Web Services, are one popular

kind of SOA applications. The unique features of WS-BPEL programs bring new challenges into

testing. A test case for testing a WS-BPEL program is a sequence of messages that can be


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received by the WS-BPEL program under test. Previous research has not studied the challenges

of message-sequence generation induced by unique features of WS-BPEL as a new language. In

this paper, we present a novel methodology to generate effective message sequences for testing

WS-BPEL programs. To capture the order relationship in a message sequence and the constraints

on correlated messages imposed by WS-BPEL’s routing mechanism, we model the WS-BPEL

program under test as a message-sequence graph (MSG), and generate message sequences based

on MSG. We performed experiments for our method and two other techniques with six WS-

BPEL programs. The results show that the message sequences generated by using our method

can effectively expose faults in the WS-BPEL programs.

2. A Bayesian Network-Based Knowledge Engineering Framework for IT

Service Management

Abstract :

Service management is becoming more and more important within the area of IT

management. How to efficiently manage and organize service in complicated IT service

environments with frequent changes is a challenging issue. IT service and the related information

from different sources are characterized as diverse, incomplete, heterogeneous, and

geographically distributed. It is hard to consume these complicated services without knowledge

assistant. To address this problem, a systematic way (with proposed toolsets and process) is

proposed to tackle the challenges of acquisition, structuring, and refinement of structured

knowledge. An integrated knowledge process is developed to guarantee the whole engineering

procedure which utilizes Bayesian networks (BNs) as the knowledge model. This framework can

be successfully applied on key tasks in service management, such as problem determination and

change impact analysis, and a real example of Cisco VoIP system is introduced to show the

usefulness of this method.


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3. Personalized QoS-Aware Web Service Recommendation and Visualization

Abstract :

With the proliferation of web services, effective QoS-based approach to service

recommendation is becoming more and more important. Although service recommendation has

been studied in the recent literature, the performance of existing ones is not satisfactory, since 1)

previous approaches fail to consider the QoS variance according to users’ locations; and 2)

previous recommender systems are all black boxes providing limited information on the

performance of the service candidates. In this paper, we propose a novel collaborative filtering

algorithm designed for large-scale web service recommendation. Different from previous work,

our approach employs the characteristic of QoS and achieves considerable improvement on the

recommendation accuracy. To help service users better understand the rationale of the

recommendation and remove some of the mystery, we use a recommendation visualization

technique to show how a recommendation is grouped with other choices. Comprehensive

experiments are conducted using more than 1.5 million QoS records of real-world web service

invocations. The experimental results show the efficiency and effectiveness of our approach.

4. A Decentralized Service Discovery Approach on Peer-to-Peer Networks

Abstract :

Service-Oriented Computing (SOC) is emerging as a paradigm for developing distributed

applications. A critical issue of utilizing SOC is to have a scalable, reliable, and robust service

discovery mechanism. However, traditional service discovery methods using centralized

registries can easily suffer from problems such as performance bottleneck and vulnerability to

failures in large scalable service networks, thus functioning abnormally. To address these

problems, this paper proposes a peer-to-peer-based decentralized service discovery approach


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named Chord4S. Chord4S utilizes the data distribution and lookup capabilities of the popular

Chord to distribute and discover services in a decentralized manner. Data availability is further

improved by distributing published descriptions of functionally equivalent services to different

successor nodes that are organized into virtual segments in the Chord4S circle. Based on the

service publication approach, Chord4S supports QoS-aware service discovery. Chord4S also

supports service discovery with wildcard(s). In addition, the Chord routing protocol is extended

to support efficient discovery of multiple services with a single query. This enables late

negotiation of Service Level Agreements (SLAs) between service consumers and multiple

candidate service providers. The experimental evaluation shows that Chord4S achieves higher

data availability and provides efficient query with reasonable overhead.

5. A Two-Tiered On-Demand Resource Allocation Mechanism for VM-Based

Data Centers

Abstract :

In a shared virtual computing environment, dynamic load changes as well as different quality

requirements of applications in their lifetime give rise to dynamic and various capacity demands,

which results in lower resource utilization and application quality using the existing static

resource allocation. Furthermore, the total required capacities of all the hosted applications in

current enterprise data centers, for example, Google, may surpass the capacities of the platform.

In this paper, we argue that the existing techniques by turning on or off servers with the help of

virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource

allocation method to solve the problem of on-demand resource provision for VMs is the key to

improve the efficiency of data centers. However, the existing dynamic resource allocation

methods only focus on either the local optimization within a server or central global

optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand

resource allocation mechanism consisting of the local and global resource allocation with

feedback to provide on-demand capacities to the concurrent applications. We model the on-


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demand resource allocation using optimization theory. Based on the proposed dynamic resource

allocation mechanism and model, we propose a set of on-demand resource allocation algorithms.

Our algorithms preferentially ensure performance of critical applications named by the data

center manager when resource competition arises according to the time-varying capacity

demands and the quality of applications. Using Rainbow, a Xen-based prototype we

implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand

resource allocation mechanism and algorithms. The experimental results show that Rainbow

without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements

in the application performance, as well as 26 percent higher average CPU utilization than

traditional service computing framework, in which applications use exclusive servers. The two-

tiered on-demand resource allocation further improves performance by 9 to 16 percent for those

critical applications, 75 percent of the maximum performance improvement, introducing up to 5

percent performance degradations to others, with 1 to 5 percent improvements in the resource

utilization in comparison with Rainbow-NDA.

Education

2013 2014 ieee projects titles with abstract