CICT 2017, International Conference @ BMIET, SONEPAT, NCR

Table of Contents

Organizing Committee

Advisory Committee

Technical Program Committee

Message from the desk of Chairman, BMIET

Message from the desk of Founder-CEO, BMIET

Message from the desk of Deputy-Director, BMIET

Message from the desk of Principal, BMIET

ABSTRACTS

PaperID Authors Title Page No.

135 Himanshi Saini, Amit Kumar Garg

Performance Analysis of Routing and Protection

Schemes for High Speed Networks

111 Priyanka Aggarwal, Neeraj Kr. Shukla, Simran Choudhary

Efficient CRC Implementation in 10G Ethernet

and DigRF V4 Protocol

133 Riya Sinha, Dr. Amit Kumar Garg, Swati Tyagi

A Comprehensive Review on Comparative Study

of Different Techniques of Dispersion

Compensation

102 Nidhi Sharma, V.K Srivastava, Alok Sharma

An Improved Authentication Security Scheme

109 Vinek Agarwal, J.S Singh, Ashish Negi

Travel-time Prediction: A Short survey

124 Tarun Gupta A survey of Traffic Management and Behavior

in a QoS Environment

130 Namita Kathpal, Amit Kumar Garg

Simulative Investigation of 2.5Gbps RZ modulation format using various optical sources

in SOA based RoF system

131 Mukesh Singhla Energy Efficient Routing Protocols for Wireless

Proceedings of First International Conference on Computational Intelligence and Communication Technologies

1

10

16

20

26

29

35

40

40

sensor Network

128 Alok Sharma, Nidhi Sharma, V.K Srivastava,

Hundred Percent Secure and Pure Steganography

without Password Protection

136 Abhinav Juneja, Shubham Jain, Ekta Gandhi

Stock Market Data Analysis using Apache Hadoop

115 Abhinav Juneja, Prayans Jain, Siddharth

Generation of Business Intelligence by Sentimental Analysis through Big Data and

Hadoop

123 B.MaheshDynamic Update and Public Auditing with

Dispute Arbitration for Cloud Data

127 Ashima Arya, Jagpreet Sandhu

A Survey on Big Data Storage Issues in Cloud Computing Environment

129 Archna Kumar, Abhinav Juneja, Sapna Juneja

Constraints and Limitations in Software Reliability Prediction

137 Saloni, Vishal Jain, Devender Saini

A Review on the Intelligent Schemes for Automatic Generation control in Modern Power

System

122 Rajeev Kapoor, Jagpreet Singh, Subhash Chander

Internet of Things: A Survey of Architectures and Recent Research Trends

134 Vishal Jain, Saloni, Devender Saini

Strategies in Hybrid Evolutionary Algorithms for

Optimization

138 Gurminder Kaur, Priyansh Gupta

Home and Automobile Automation Model

139 Savita Khatri, Neeraj Dahiya

New Variant of bat algorithm and Clustering

Approach for optimization problems


2

43

47

50

53

58

62

66

70

73

88

91

94

Organizing Committee

Patrons

Mr Rajeev Jain, Chairman

Mr Rakesh Kuchhal, FOUNDER-CEO

General Chair

Lt Col. Yogesh Jain, DD, BMIET, Sonipat

Technical Program Chair

Prof (Dr) Harish Mittal, Principal, BMIET, Sonipat

Technical Program Co-Chair

Mr. Abhinav Juneja, Vice-Principal

Mr Vishal Jain , HOD, ECE & EEE

Organizing Secretary

Dr Manoj Kumar, BMIET, Sonepat

Organising Committee Members

Dr Seema Dalal

Dr Divya

Mr Sameer Mehta

Ms Monisha

Dr Shilpi Saxena

Ms Sapna Juneja

Mr Sudhir Vasesi

Mr Arun Kumar

Ms Bhawna

Ms Anita Malik

Ms Kanika

Mr Ravinder Kumar

Mr Vishal Verma

Mr Ajay Kumar

Mr Pawan Kumar

Mr Vikas Kuchhal

Mr Sunil Kumar

Mr Sandeep Rathi

Ms Gurminder

Ms Saloni

Ms Sonika

Ms Preeti


3

Advisory Committee

• Prof (Dr.) Vinod Kumar, Vice Chancellor, Jaypee University of Information Technology,

Solan, H.P., India

• Brig (Dr) Somnath Mishra, Vice Chancellor, Sikkim Manipal University (SMU), India

• Dr Krzysztof Galkowski, Institute of Control and Computation Engineering, University

of Zielona Gora, Poland

• Dr Venkata Raghavendra Miriampally, Adama Science & Technology University,

Adama, Ethiopia

• Dr A Clementking, College of Computer Science, King Khalid University , Abha, Saudi

Arabia

• Dr Maimunah Mohd Shah, University Technology MARA (UiTM), Puncak Alam

Campus, Selangor, Malaysia

• Dr Alok Tiwari, King AbdulAziz University,Jeddah, Saudi Arabia

• Dr Pranob Misra, LUMILEDs, SANJOSE, United States

• Dr Rajender Singh Chhillar, M. D. University, Rohtak, India

• Dr Amit Kumar Garg, ECE Deptt., Deenbandhu Chhotu Ram University of Sc. & Tech,

Murthal, HR, India

• Dr S P Khatkar, UTD, M D University, Rohtak, India

• Dr V P S Naidu, Multi-Sensor Data Fusion Lab, CSIR - National Aerospace

Laboratories, Bangalore, India

• Dr Vinay Kumar Goel, GNIOT, Gr. Noida, India

• Dr Jyotindra Mulshankerbhai Jani, Lt. M.J. Kundaliya Mahila College, Rajkot, India

• Dr Santosh Kumar Nanda, Eastern Academy of Science and Technology Bhubaneswar,

Odisha, India

• Dr Neeraj K Chavda, A. D. Patel Institute of Technology, New Vallabh Vidyanagar ,

Anand , Gujarat, India

• Dr Geeta R. Bharamagoudar, KLE Institute of Technology, Hubballi, Karnatak, India

• Dr N. Rajkumar, Ramakrishna Engineering College, Vattamalaipalayam,

Coimbatore,Tamilnadu, India

• Dr Santosh K Pandey, Ministry of Electronics & IT, Electronics Niketan, Lodhi Road,

New Delhi, India

• Dr Sunandan Bhunia, Central Institute of Technology, BTAD, Assam, India

• Dr Renu Tuli, Amity School of Engineering and Technology Bijwasan, New Delhi, India

• Dr Sudipto Chaki, MCKV Institute of Engineering, Howrah, West Bengal, India


4

Technical Program Committee

Dr Zdzislaw Polkowski Jan Wyzykowski University, Poland

Dr Krzysztof Sozanski University of Zielona Góra, Poland

Dr Xiao-Zhi Gao Lappeenranta University of Technology, Finland

Dr Izzat M Alsmadi Yarmouk University, Jordan

Dr Sampson Asare University of Botswana

Dr Subhasini David CBE, Halhale, Eritrea

Dr Dan Randall American Sentinel University, USA

Dr Veena T. Nandi Majan College, Ruwi, Muscat, Oman

Dr VVR Raman ACHS, Asmara, Eritrea

Dr Samadhiya Durgesh Chung Hua University Taiwan

Dr Kranti V. Toraskar, IS/IT & Info-Security Consultant at KITE-Consult, Hong Kong

Dr Pradeep Bhatia GJUS&T, Hisar

Dr Rahul Rishi UIET, MDU, Rohtak

Dr Dharminder Kumar GJUS&T, Hisar

Dr J S Saini DCRUST, Murthal, India

Dr Sunil Kumar Khatri AIIT, Amity University, Noida

Dr A. K Garg DCRUST, Murthal

Dr Yudhvir Singh GJUS&T, Hisar

Dr. Tanupriya Choudhury ASET, Amity University, Noida

Dr Malay Ranjan Tripathy ASET, Amity Univerty UP, Noida, India

Dr Yumnam Jayanta Singh Assam Don Bosco University, Guwahati, India

Dr Sangeeta Gupta GNIM, Delhi, India

Dr V K Panchal SBIT, Sonepat

Dr Pankaj Gupta VCE, Rohtak

Dr Shyam Akashe ITM, Gwalior

Dr O P Sangwan GJUS&T, Hisar

Dr Poonam Bansal MSIT, New Delhi

Dr Saurabh Mukherjee Banasthali University

Dr A.V. Senthil Kumar Hindusthan College of Arts and Science, Coimbatore

Dr Nirbhay Chaubey ISTAR, VVGT University, Gujarat

Dr Jasmine K S R. V. College of Engineering, Bangalore

Dr Pichai Shanmugavadivu Gandhigram Rural Institute - Deemed University Gandhigram,

Tamil Nadu

Dr Deepak Goyal VCE, Rohtak

Dr N S N Murthy Sharma Sreenidhi Institute of Sc. & Tech. Yamnampet, Hyderabad

Dr Dharmpal Singh JIS College of Engineering, Kalyani, India

Dr N. Rajkumar Sri Ramakrishna Engineering College, Vattamalaipalayam,

Coimbatore

Dr Puneet Goswami SRM University, Sonepat


5

B.M. INSTITUTE OF ENGINEERING & TECHNOLOGY, SONEPAT

Rajiv Jain Chairman

MESSAGE

I am happy to note that B.M. Institute of Engineering & Technology, Sonepat is

organizing its First International Conference on Computational Intelligence &

Communication Technologies (CICT-17) on 4-5th

Nov. 2017.

The conference provides a stage for the academicians and researchers to discuss

the latest developments in the field of Computational Intelligence &

Communication.

My hearty greetings to the faculty members of the Institute, for organizing an

International Conference on an important topic of academic interest. My best

wishes for the successful conduct of the Conference.

Rajiv Jain

Bahalgarh Road, Behind Fazilpur Power Sub Station, Sonepat-131001 (HARYANA)

Tel: 0130-2236911-14, Website: www.bmiet.net E-mail [email protected]


6

B.M INSTITUTE OF ENGINEERING & TECHNOLOGY, SONEPAT

Rakesh Kuchhal FOUNDER-CEO

MESSAGE

I am immensely happy to learn that the Institute is organizing its First International

Conference on Computational Intelligence & Communication Technologies

(CICT-17) on 4-5th

Nov. 2017and a souvenir is being brought to commemorate this

occasion.

I sincerely hope that CICT-2017 is going to deliberate upon several important

topics during the conference which will be of importance to the nation and will

enhance the quality of academic and professional research. I am sure that the

Institute will keep on contributing more effectively in order to promote academic

research.

I convey my best wishes for the success of the Conference

.

Rakesh Kuchhal




7


Lt. Col Yogesh Jain DEPUTY. DIRECTOR

MESSAGE

I am glad to note that B.M Institute of Engineering and Technology, Sonepat is

organizing its First International Conference on Computational Intelligence and

Communication Technologies (CICT-2017) on 4-5th

Nov 2017 on the Institute

campus. Computational Intelligence is the thrust area of all sciences and has

become an indispensable tool in solving the problems of Engineering and

Technology. The Conference will bring like-minded individuals on one platform to

discuss new challenges and trends in field of research. I am sure that the

deliberations will enrich academic wisdom of the participants to enable exploration

of new domains of applications in CICT-17. I hope that the delegates will have an

enjoyable and fruitful stay in the BMIET campus. I wish the Conference a grand

success.

Yogesh Jain




8


Dr. Harish Mittal PRINCIPAL

MESSAGE

I am indeed most delighted to be given the opportunity to chair our First

International Conference on Computational Intelligence and

Communication Technologies (CICT-2017).

As Program chairman of this event, I hope to bring together a good

programme that stimulates knowledge and scientific intellect. A holistic and

interactive approach has been employed in planning the Conference in

which we shall discuss the latest developments in the field of Computational

Intelligence and Communication Technologies.

The review process was a daunting challenge for the Program Committee.

Based on the received review reports, acceptance rate was around 30%.

Specifically, the program covers important aspects of Wired and Wireless

Communications, Simulation & Modeling of Communication Systems,

Computer Vision & Image Processing, Cloud Computing, Artificial,

Biological and Bio-Inspired Intelligence, Antennas and Propagation and

Control Systems.

On behalf of the organizing committee, I would like to extend a warm

invitation to all the participants who have contributed in this Conference.

Dr. Harish Mittal




9

Performance Analysis of Routing and Protection

Schemes for High Speed Networks Himanshi Saini

Assistant Professor, Electronics and Communication

Engineering Department,

Deenbandhu Chhotu Ram University of Science and

Technology, Murthal, Sonepat, Haryana, India

Amit Kumar Garg Professor, Electronics and Communication Engineering

Department,

Deenbandhu Chhotu Ram University of Science and

Technology, Murthal, Sonepat, Haryana, India

Abstract--Management of high-speed networks becomes critical

as traffic volume increases. Impact of failures is aggravated in

networks carrying huge volume of data. Impact is more

pronounced as level of multiplexing in high speed networks

increase. It is important to deal with survivability of high speed

networks in efficient manner in order to maintain high Quality of

Service (QoS). In this paper, a performance analysis of Routing

and Protection algorithms in optical networks has been

performed. Routing algorithm for each routing technique has

been analysed and compared on basis of performance metrics

such as total recovery speed, capacity utilization, blocking

probability, complexity, recovery path length, resource

utilization ratio, cost etc. The present study indicates that

recovery speed of Streams technique is comparable to Dedicated

Path Protection (DPP), Capacity Utilization of streams is better

than DPP and it has least blocking probability among Shared

Path Protection (SPP) and DPP. Among Hamiltonian Cycle

Protection Techniques, Hamiltonian cycle Neighbour capacity

Closure (HNC) offers least spare capacity. The analysis can be

applied to differentiated service selection with optimal Quality of

Service (QoS).

Keywords--optical network; routing; protection; resource

utilization ratio; blocking probability

I. INTRODUCTION

It is observed that a planned survivability algorithm is critical

to minimize damage in high speed networks as data traffic

increases in such networks. Dense Wavelength Division

Multiplexing (DWDM) optical networks has transmission rate

of several gigabits per second so failure for even fraction of

second can result into huge data loss and recovery overhead.

Therefore it is important for a high speed network to be

survivable throughout the operation. Optical opaque networks

are replaced with Transparent Optical Networks (TON) for

high speed. There are various issues in TONs such as

wavelength conversion, exacerbated impact of failure. High

impact of network failure is as a result of huge traffic carried

by TONs. This requires a deep understanding of the trade-offs

between different survivability algorithms [1]. All

performance metrics such as Total Recovery Speed, Capacity

Utilization, Blocking Probability, Complexity, Resource

Utilization Ratio do not respond optimally to a particular

network condition so a tradeoffs among performance

parameters has to be established. Routing algorithms can

optimize bandwidth utilization so that Dense Wavelength

Division Multiplexing (DWDM) users can attain maximum

throughput [2]. Routing and protection techniques such as

Streams, Hamiltonian cycle Protection (HCP), Multi Domain

Hamiltonian Cycle Protection (MHCP) are analyzed on basis

of various network performance parameters.

Section 2 covers the detailed description of algorithms under

consideration and related issues. Section 3 shows the

comparative analysis. Conclusion is presented in section 4

followed by references.

II. ISSUES AND RELATED WORK

Sun-il Kim et al. [3] presented a protection algorithm

STREAMS for single link and node failures. Pre-established

back up path that is shared across different connections is

called a Stream. Streams set up is shown in Fig. 1.Streams

allow sharing of backup wavelengths between backup paths

that are link disjoint and do not diverge whereas in SPP,

wavelength on link [A, B] as shown in Fig. 2, is shared by two

backup paths. Every Streams solution can always be used for

SPP, but not all SPP solutions are applicable to Streams [3].

Non Dynamic streams algorithm initiates with connection

establishment between source and destination pair followed by

finding shortest primary paths, calculation of primary and

backup path pair, checking for validity of found solution,

allocating resources for the new connection and finally

updating network status. Its performance lies between SPP and

DPP. Following are input requirements and output from

Streams. Input for Streams

Output from Stream

P(src,dst) : Set of shortest primary paths (*src=source, *dst=destination)

B(pi,h) :Ordered set of backup paths corresponding to pi ∈P sorted by

length in ascending order (*h=hop)

S :Set of streams [initially empty]

λ(s) : Wavelength used by stream s ∈ S [initially empty]

Free(λ) : Set of free λ-channels on wavelength λ

PSB(s,l) :This set contains all primary paths that are protected by a part

of s, including l

compatible (s, b) :checks for posskble splits/merges that may arise as a

result of adding b to s, w : Shortest possible wavelength

exhop :number of extra hops allowed for backup paths

*A path is treated as an ordered set of links.

Network capacity information, streams information

Update p,b,s


10

Fig. 1. Streams Setup [1] Fig. 2. Shared Path Protection Setup (left), Streams Setup (right) [3]

Streams Algorithm

Hong Huanget al. [4] introduced resilience solutions in form

of HCP based Algorithm. Hamiltonian cycle traverses each

node in the network exactly once. HCP configures failed link

as recovery target, not the individual connections [4]. It

reduces spare resources into a minimal set of links.

Hamiltonian cycle based resilience solutions manage and deal

with primary and backup network separately. Performance

metric for moderate Networks is Spare to Primary Ratio (SPR)

which is equal to ratio of spare capacity to primary capacity.

For large networks Hong Huang et al. [4] introduced Single

Hamiltonian resilience for Inhomogeneous networks (SHI)

algorithm, Hamiltonian Cycle Cover (HCC) algorithm and

Hamiltonian cycle Neighbour capacity Closure (HNC)

algorithm.

SHI Algorithm

HCC Algorithm

HNC Algorithm

1. mincost←|links|+1

2. for all pi ∈P(src, dst)

a. for all bj∈B(pi,exhop)

i. for all sk∈S

if (compatible (sk, b))

cost←0

valid←true

for all links lm∈b

if ((p∩PSB(sk,lm)) = not empty),

valid←false

if (lm∉sk) ,cost←cost+1

if (valid AND cost<mincost)

mincost ←cost

stream ←sk

ii. k=k+1, goto i

iii. if (mincost= |links|+1)

stream←b, mincost←length(b)

iv. j=j+1, goto a

v. if (cost<mincost)

mincost ←cost+length(pi), p ←pi, b←bj, s ←stream

b. i=i+1, goto 2

3. all links←b∪s

4. new links←b\s

5. if (new links⊄Free(λ(s)))

a. Free(λ(s)) ←Free(λ(s))∪s

b. a ⊂Free(w)

c. λ(s) ← w

6. for all links lm∈b

a. PSB(s,lm) ←PSB(s,lm)∪p

b. m=m+1, goto 6

7. Free(λ(s)) ←Free(λ(s))\new links

8. s ←all links

HC (bps): The capacity of Hamiltonian Cycle

bwj :capacity of primary link j that is on the HC

bwi :capacity for primary link i that is not on the HC

HC (bps) =max [bwj, 0.5 bwi], over all i, j

From Figure 3: HC (bps) =[5,2,2,5,8/2,6/2]=5

HCC manages network using a multi-domain solution.

1. Find a HC for each domain.

2. For each HC calculate capacity bps =max [bwj, 0.5 bwi]

3. For links that intersect HC’s ,capacity,bpj = max

[bps],where the max operation is taken among

neighboringHCs intersecting on the link j.

4. The result is a protection capacity provision,

whichconsists of a set of HCs, each responsible for

protecting its local domain.

From figure 4: bps =Max[5,4] = 5

1. Partition the primary network into a set of protection

domains.

2. Find a HC for each domain.

3. For each HC calculate capacity bps = max [bwj, 0.5 bwi]

4. For links that intersect HC’s ,capacity, bpj = abs(bps1 -

bps2), where s1 and s2 are the two neighboring

domains and abs( ) is the absolute value function

5. The result is a protection capacity provision, which

consists of a set of HCs, each responsible for protecting

its local domain.

From figure 5: bpj =abs(bps1 - bps2) =abs(5-4) =1


11

Fig. 3. Single Hamiltonian resilience for Inhomogeneous networks example

Fig. 4. Hamiltonian Cycle Cover example

Fig. 5. Hamiltonian cycle Neighbour capacity Closure example

Lei Guo et al. [5] proposed MHCP algorithm which considers

Local Hamiltonian Cycle (LHC) in each single-domain to deal

with intra-link failures and Globe Hamiltonian Cycle (GHC)

in multi-domains to protect the inter-link failures. For MHCP

implementation, network model considered in [5] is multi-

domain optical network, (N, λ, InterL, T), where N is the set

of network nodes, λ is the set of wavelengths in each fiber

link, InterL is the set of inter-fiber links between different

domains, and T is the set of topologies of multi-domain

networks defined as T=Nm, IntraLm, m=1,2,... in which Nm

is the set of network nodes in domain m and IntraLm is the set

of intra-fiber links in domain m. In domain m, a Hamiltonian

cycle LHCm which is composed of intra-fiber links is

generated based on the physical topology of domain m to

provide the protection for intra-failures. In multi-domains, a

GHC which is composed of inter-fiber links and virtual-links

is generated based on the virtual topology of multi-domains to

provide the protection for inter-failures, where the virtual-link

VLm is the map of LHCm. All virtual-links compose a set

VLVLm, m=1,2,.... The shortest path algorithm, i.e.,

Dijkstra’s algorithm, is applied to compute the route.

MHCP Algorithm as proposed in [5]

Input: Network topology; T connection requests; rq←0, Output: The total resources consumption R Rq: Connection request r, Wrq: Working path of Rq, Wk: Number of working wavelengths on link k.

Fk: Number of free wavelengths on link k, OLm: Set of on-cycle links which are intra-fiber links traversed by LHCmin

domain m.

SLm: Set of straddling links which are intra-fiber links not traversed by LHCmin domain m, BLm: Number of backup

wavelengths on each on-cycle link on LHCmin domain m, OG : Set of on-cycle links which are inter-fiber links traversed by

GHC in multi-domains, SG : Set of straddling links which are inter-fiber links not traversed by GHC in multi-domains, BG :

Number of backup wavelengths on each on-cycle link on GHC in multi-domains, Costk: Cost of link k, |S |: Number of

elements in set S

1. Backup Wavelengths Assignment

a. Backup wavelengths required on each on-cycle link on LHCm are determined by maximum of four parts

i. Max value of working wavelengths on on-cycle links onLHCm: max (Wk | ∀ K ϵ OLm )

ii. Max value of half of working wavelengths on straddling links on LHCm : max (Wk÷ 2 | ∀ K ϵ

SLm )

iii. Max value of half of working wavelengths on straddling links on GHC: max (Wk ÷ 2 | ∀ K ϵ OG )

iv. Max value of quarter of working wavelengths on straddling links on GHC: max (Wk ÷ 4 | ∀ K ϵ

SG)

b. Backup wavelengths required on each on-cycle link on GHC are determined by maximum of two parts

i. Max value of working wavelengths on on-cycle links on GHC: max (Wk| ∀ K ϵ OG )

ii. Max value of half of working wavelengths on straddling links on GHC: max(Wk ÷ 2 | ∀ K ϵ SG)

2. Working Path Selection

Intra-domain Routing: If the source node X and destination node Y belong to the same domain m. Adjust the cost for

each link and compute least cost working path based on physical topology of domain.

i. If ((k ϵ IntraLm and Fk<1) or ( k∉IntraLm))

ii. Costk = ∞iii. If (k ϵ IntraLm and Fk<1)

iv. Costk = (λ+1- Fk)/λ ×Costk*

v. If the working path traverses link kand the sum of free and backup wavelengths on some on-cycle

link x are not enough : Costk* = ∞

vi. else Costk* = 1

3. Find LHC for each single-domain based on its physical topology

4. Find GHC for multi-domains based on the virtual topology

5. BLm←0 (for every domain m), BG←0

6. Ifrq≥T, go to 10 else goto 5

7. Find Wrq using step 2, If found goto 6 else goto 9

8. Save Wrq, Wk←Wk +1 ∀ K ϵWrq

9. Update BLm(for every domain m) and BG using step 1

10. rq← rq+1, goto 4

11. Block request, rq← rq+1, goto 4

12. Find total resources consumption

R = BG .| OG| + Σ∀ K ϵInterLWk + Σ∀ mBLm. | OLm| (for all domains) + Σ ∀ m Σ ∀ k Wk (for all domains and intra

domain links in each domain)


12

In step 5, in the worst case, MHCP will run three times of

Dijkstra’s algorithm to compute the inter-domain working

path for each connection request. Time complexity of MHCP

is O(3N2 ). In Hamiltonian cycle protection, all working

wavelengths share the common backup wavelengths on

Hamiltonian cycle, while in shared-path protection some

working wavelengths share some backup wavelengths and

other working wavelengths share other backup wavelengths;

that is, the backup wavelengths sharability in Hamiltonian

cycle protection is better than that in shared-path protection

[6].

Sunil Gowda et al. [7] proposed a routing algorithm,

conversion free primary routing (CFPR), converter

multiplexing technique and backup path relocation scheme.

Network Architecture used in [7] is Dynamic routing, Path

level protection; with dedicated and shared protection schemes

for backup paths. Wavelength router architecture is based on

share-per-node wavelength converter configuration. Primary

paths require dedicated resources, including wavelength

converters; it is preferable to route primary connections on

conversion-free paths. Objective of CFPR is to avoid

wavelength conversion while routing primary connections.

Complexity of CFPR is O(N2W), for a network with N nodes

and W wavelengths. Converter Multiplexing Technique

(CMT) Algorithm and Backup Path Relocation Scheme

(BPRS) Algorithm are implemented using a dynamic network

traffic model in which connection requests arrive at a node.

Each connection request is assignment a wavelength. Traffic

load is defined in Erlangs. Share-per-node architecture is used;

all nodes allocate an equal number of converters. Both the

dedicated and shared protection schemes are studied.

In technique presented in [7], first conversion free paths

availability is checked followed by computing primary paths

using Dijkstra’s shortest path algorithm, if conversion free

paths are not available then Hop count based shortest path

routing technique is used, if there are overlapping segments

then Back up path relocation technique is used which is

further implemented in two categories, wavelength relocation

in which new wavelength is used for the overlapping segment

and segment relocation in which completely different path is

relocated to the overlapping segment.Relocation schemes are

illustrated in Fig. 6. For a connection request between nodes 1

and 6, path 1−3−6 on wavelength 0 is computed to be a

candidate primary path. Assigning this path requires hop 1−3

of backup path b1 to be relocated. Wavelength 2 offers a path

between nodes 1 − 3. Thus, the overlapping segment is

relocated to wavelength 2, after reserving a converter at node

1which is illustration of wavelength relocation.Backup path b2

is relocated from path 2 − 3 − 6 onto path 2 − 3 − 5 –6 which

is illustration of segment relocation.

CFPR, CMT, BPRS schemes as presented in [7] are outlined as following:

1. If conversion free paths available AND no path overlapping

a. CFPR

i. Fwx,y= 0, if wavelength w on link (x,y) is assignedto a primary path

ii. Fwx,y=1, if the wavelength is either free or is reserved for backup path(s)

iii. For each wavelength W

iv. Primary routes, Pwx,y are computed using Dijkstra’s shortest path algorithm.

v. If no path is available on w wavelength, Pwx,y= φ

2. Else if (conversion free paths not available)

a. Hop count based shortest path routing algorithm / Convertor multiplexing technique

3. Else if (segment overlapping)

a. Back up path relocation

i. Wavelength Relocation //new wavelength is used for the overlapping segment

Pw: primary path wavelength

Bw: backup path wavelength

Bs: backup path source

Bd: backup path destination

Ps: primary path source

Pd: primary path destination

PwPsPd: set of segments constituting primary path on wavelength w

PwBsBd: set of segments constituting backup path on wavelength w

For w = 1 to W

If any segment s1 ϵ PwPsPd = any segment s2 ϵPw

BsBd

s2 is routed at some available wavelength other than w

ii. Segment Relocation //completely different path is relocated to the overlapping segment

Pw: primary path wavelength

Bw: backup path wavelength

Bs: backup path source

Bd: backup path destination

Ps: primary path source

Pd: primary path destination

PwPsPd: set of segments constituting primary path on wavelength w

PwBsBd: set of segments constituting backup path on wavelength w

For w = 1 to W

If any segment s1 ϵ PwPsPd = any segment s2 ϵPw

BsBd

Relocate s2 on PwBsBd


13

Fig. 6. Backup path relocation (left and right sets denote network states before

and after relocation, respectively) [3]

III. RESULTS AND DISCUSSIONS

Shared Path Protection (SPP) allows primary path to share

some of the backup light paths whereas in Dedicated Path

Protection (DPP), primary path has dedicated backup light

path. Streams utilize SPP and DPP merits. Recovery speed of

Streams is comparable to Dedicated Path Protection (DPP),

Capacity Utilization of streams is better than DPP and it has

least blocking probability among SPP and DPP. Comparative

analysis among SPP, Streams and DPP is framed in Table 1.

TABLE I: COMPARISON OF SPP, STREAMS, DPP [1]

Routing and

Protection

Technique

Total Recovery

Speed

Capacity

Utilization

Blocking

Probability

SPP Low High

(Almost Equal)

high

Streams High

(Almost Equal)

least

DPP Low low

Among Hamiltonian Cycle Protection Techniques, HNC

offers least spare capacity, HCP is least complex but

applicable only in moderate size networks with scope of single

failure detection. Comparative analysis among Hamiltonian

Cycle Protection Techniques is framed in Table 2.

TABLE II. COMPARISON OF HCP, SHI, HCC, HNC [4]

Routing and

Protection

Technique

Complexity Scope Recovery path

length

HCP(Moderate

Networks

least Single Failure Long

SHI low

HCC high Multiple

Failure

Short

HNC approachable

to HCC

approachable to

SHI

In Multi domain network, Hamiltonian Cycle offers less

Resource Utilization Ratio (RUR) and less Blocking

Probability (BP) as compared to Shared Protection.The RUR

is defined as the ratio of the total backup wave-lengths over

the total working wavelengths, and smaller RUR means better

resource utilization ratio. The BP is defined as the ratio of the

blocked connection requests over the total connection

requests, and smaller BP means higher network throughput.

Comparative analysis among Shared Protection and

Hamiltonian Cycle Protection in Multi Domain network is

framed in table 3. Comparison of Basic hop-count (HC) based

shortest path routing algorithm and CFPR routing algorithm is

shown in table 4

TABLE III. COMPARISON OF MULTI DOMAIN SHARED

PROTECTION (MSP) AND MULTI-DOMAIN HAMILTONIAN CYCLE

PROTECTION (MHCP) [5]

Routing and

Protection

Technique

RUR (Resource Utilization Ratio)

BP (Blocking Probability)

MSP high high

MHCP low (40% improvement

ratio over MSP)

low (40%

improvement ratio

over MSP)

TABLE IV. COMPARISON OF HC AND CFPR [8]

Routing and

Protection

Technique

Blocking

probability

Connections

blocked due to

wavelength

unavailability

Connections

blocked due

to converter

unavailability

HC High Low High

CFPR Low High Low

IV. Conclusion and Future Scope

Contribution of high speed networks can be efficiently

exploited if appropriate resilience scheme is incorporated in

network design. Network failure under high traffic scenario

result into huge loss of data and revenue. In this paper, some

of the routing techniques like Streams, Hamiltonian Cycle

Protection Techniques, Conversion free Primary Routing,

Multidomain Hamiltonian techniques are analyzed on basis of

performance metrics such as total recovery speed, capacity

utilization, blocking probability, complexity, scope, recovery

path length, applicability, resource utilization ratio, cost. It is

seen that recovery speed of Streams technique is comparable

to Dedicated Path Protection (DPP), Capacity Utilization of

streams is better than DPP and it has least blocking probability

among Shared Path Protection (SPP) and DPP. Among

Hamiltonian Cycle Protection Techniques, Hamiltonian cycle

Neighbour capacity Closure (HNC) offers least spare capacity.

The analysis can select differentiated service along with

maintaining an optimal QoS.

REFERENCES [1] Sun-il Kim, Steven S. Lumetta, “Capacity-Efficient Protection with Fast

Recovery in Optically Transparent Mesh Networks”, First International

Conference on Broadband Networks, pp. 290 – 299, Oct. 2004 [2] Jin Seek Choi, Nada Golmie, Francois Lapeyrere, Frederic Mouveaux,

David Su, “A functional classification of routing and wavelength

assignment schemes in DWDM networks”, CiteSeerXβ

[3] Sun-il Kim, Xiaolan J. Zhang, Steven S. Lumetta, “Rapid and Efficient

Protection for All-Optical WDM Mesh Networks”, IEEE Journal on

Selected Areas in Communications, vol.25 , Issue.9, pp. 68-82, Dec. 2009, doi: 10.1109/JSAC-OCN.2007.026306


14

[4] Hong Huang and John A. Copeland, “A Series of Hamiltonian Cycle

Based Solutions to Provide Simple and Scalable Mesh Optical NetworkResilience”, IEEE Communications Magazine, vol.40, Issue.11, pp. 46-

51, Nov. 2002, doi: 10.1109/MCOM.2002.1046992

[5] Lei Guo , Xingwei Wang, Jiannong Cao, WeigangHou, Hongming Li,Hongpeng Wang, “A New Survivable Heuristic Algorithm Based on

Hamiltonian Cycle Protection in Multi-Domain Optical Networks”,

2009 International Conference on Computer Engineering and

Applications, Singapore, vol.2, 2011

[6] L. Guo, X. Wang, X. Zheng, et al., “New results for path-based shared

protection and link-based Hamiltonian cycle protection in survivable WDM networks”, Photon.Netw.Commun., pp. 245-252, 2008

[7] Sunil Gowda and Krishna M. Sivalingam, “Protection Mechanisms for

Optical WDM Networks based on Wavelength Converter Multiplexing and Backup Path Relocation Techniques”, IEEE Societies INFOCOM

2003. Twenty-Second Annual Joint Conference of the IEEE Computer

and Communications, vol.1, pp. 12-21, 2003.


15

Efficient CRC Implementation in 10G Ethernet and

DigRF V4 Protocol

Priyanka Aggarwal, Neeraj Kr. Shukla, Simran Choudhary

The NorthCap University, Gurgaon (Haryana) India

Abstract— CRC is a most commonly used error detection

technique in most of the digital logic design, communication

link, so as to confirm whether the received digital message has

got any error or not, and whether it has been corrupted in the

transmission in between the different modules of the design or

not. The protocols or the devices which are required to be

operated at higher speed like 10G Ethernet operates at 156.25

MHz, DigRF V4 (a digital interface standard between

baseband IC and RF IC) operates at 100MHz of clock speed,

there comes the requirement for faster CRC implementation.

Many types of techniques for the same purpose have been

developed starting from serial CRC to parallel CRC with more

and more improvement in parallel CRC by developing

different techniques in the parallel CRC implementation.

There is also a possibility that the packet or the frame in these

protocols does not have length equal to the interface width, in

that case parallel CRC implementation becomes less efficient.

So, there should be a solution for this as well. The proposed

work gives a solution for these two problems by first

implementing the parallel CRC architecture because of its

higher speed as compared to serial CRC, and it also takes care

of the case of packet length not being equal to or a multiple of

the interface width. It uses only three configuration for CRC-

32 to be used as CRC-32 (4), CRC-32 (2) and CRC-32 (1), to

cover all the corner cases of byte presence in the complete

packet, where 4, 2 and 1 are the number of bytes to be

transmitted and are actually present in the packet or frame.

Keywords—10G, CRC, LFSR

I. INTRODUCTION

CRC is one of the most widely used error detection technique in communication protocol, computer networks and many storage devices because of its effectiveness for the same. In this a certain sequence of bits, called checksum are appended to the message which is being transmitted [3] [5]. At the receiver end, it is checked that whether these checksum bits are agreeing with the data or not and thereby ascertain the chances of any error that occurred in the data during the data transfer [10].

The generator basically takes in the input data stream and considers it as an algebraic polynomial and does the modulo-2 division by another polynomial which is actually the binary message string specific for a particular type of protocol or storage device where it is being employed [5].

CRC-N [9] implies that CRC can be defined as CRC-8, CRC-16, CRC-3, etc., where N is the degree of generator polynomial which is used for CRC execution and it is specific for the specific protocol.

CRC-N (m) implies that CRC [11] generator of any defined length (N) can be used in serial or in the parallel manner which depends basically on the number of bits (m) which are being fed in one clock cycle. For any value of N, m=1, then it is called “SERIAL CRC GENERATORS”. In these generators, only one bit is transmitted in one clock cycle. When more than one bit is sent (m>1) with any value of N, then such type of CRC generators are called “PARALLEL CRC GENERATORS” [6] [13].

Mainly two problems are worked for, in this work:

a. Meeting the speed of CRC checking with the system’sspeed.

b. Taking care of the packet length of the frames beingtransmitted.

The RTL Architecture proposed in this work takes care of both the problems in mind.

II. BACKGROUND WORK

The background work in this field deals with first increasing the speed of CRC computation by switching from serial approach to the parallel one. It then revolves around developing newer and more efficient techniques in parallel CRC [8] generators by introducing some DSP algorithm like – Retiming, Unfolding and Pipelining [1], so as to meet thefrequency requirement in high-speed applications. Moreover,a secured LBIST technique is presented for efficient CRCgeneration [2].

A. 10G Ethernet and DigRF V4 Protocol

This section gives brief information about the frequency requirement in 10G Ethernet and DigRF V4 protocols and the CRC width (N) used for them. Both of them are high-speed Protocols, requiring CRC [12] Process to be synchronized with the system’s speed of 156.25 MHz for 10G Ethernet [4] and 100 MHz for DigRF V4 Protocol [2].

The CRC width requirement is different for both protocols. 10G Ethernet requires CRC-32 with 64- bits of data packets and DigRF V4 requires CRC-16 with 32-bits of input data packets. The proposed work finds an optimum solution among serial and parallel CRC Generators to meet the speed of computation while taking care of the actual packet length which may or may not be equal or a multiple of interface width.


16

III. PROPOSED HYBRID CRC ARCHITECTURE

The proposed Architecture uses three cases of m values, which are 4, 2 or 1. These three cases cover all the cases of number of bytes that are actually present. It does so by making a valid bit associated with each byte in a packet. The value of this valid-bit ensures whether the particular byte is present or not, and finally the CRC is calculated for the remaining bytes by making use of input width only among 4, 2, or 1.

A. State Machine Diagram and RTL Architecture

Presenting below the State Machine Diagram for theproposed technique of CRC Implementation:

Fig.1. State Machine Diagram for proposed Approach

The Figure shows different cases of how the parallel CRC generators of different widths are being used to generate a CRC of 32-bits for Ethernet and similarly for DigRF V4 Protocol which is of 16-bits.

Fig.2. RTL Architecture of proposed Technique

Figure 1 and 2 are the different ways of showing the

execution of proposed technique which takes the

advantage of parallel CRC generator and takes care of

number of valid bytes. First of all, it checks whether the

last four bytes 0 to 4 are valid (means present) or not. If

yes, then check the status of remaining bytes 4 to 7 to

find how many of these are valid and then the result of

these 0 to 3 bytes is fed to the remaining one. In case

lower four bytes are not valid, it checks whether only two

bytes are valid. If yes, then it checks for the next two

bytes, 2nd and 3rd that whether they are valid or not. If

yes, then it feeds the results of 0th and 1st byte CRC to

these two, otherwise that becomes the result. In this way,

the proposed Architecture for CRC calculation solves the

two problems stated.

IV. SIMULATIONS AND RESULTS

The simulation part covers the CRC-32 generation for 10G Ethernet taking in 64-bits as input packet data, by using three ways: CRC-32 (1), CRC-32 (64) and the proposed one. These are synthesized on Xilinx 14.7 ISE. The synthesis reports are generated for all the three approaches on the same tool.

Following are the Frequency requirement for the CRC-32 = 23ABDBF7, for the input data = FF00FF00FF00FF00, with the three methodologies:


17

Fig.3. CRC-32 Value

TABLE 1. Synthesis Report

Technique Used Frequency Requirement

CRC-32 (1) 146.07 MHz

CRC-32 (64) 456.12 MHz

Proposed 173.6 MHz

Fig.4. Differences of Frequencies with the required

As seen from the values, the value in case of Serial CRC-32 is near to as desired (156.25 MHz) for 10G Ethernet, but Parallel one is more efficient in terms of speed compared to any of the three approaches, but still the emphasis is laid on the proposed technique because although it is having less efficiency as compared to parallel approach but it is closer to the required value. Moreover the proposed approach takes care of the number of bytes that are actually present in a packet being exchanged.

Depending upon the value of “cntrl_in [7:0]” variable, the byte presence is being detected. When all the bytes are present, the value of this = 1111111, as shown in the Fig.5 When any of the byte is not there, the corresponding bit sets to “0”. For instance, when MSB is not present and the CRC-32 is generated, then input data = “00FFFF00FF00FF00”, which gives the CRC-32 value = B983C7C4.

0

100

200

300

400

500

CRC-32(1) CRC-32(64) Proposed

Technique

Synthesis Report for 10G Ethernet


18

Fig.5. CRC-32 when MSB not Valid

Similar technique is applied on the DigRF V4 Protocol to get the similar results of frequency matching with 100 MHz and in this also the CRC computation is done in accordance with the number of valid bytes present in a packet being exchanged.

REFERENCES

[1] Chaitali Tohgaonkar, Prof. Sanjay B. Tembhurne, Prof. Vipin S. Bhure, “Design of Parallel CRC Generation for High Speed Application”, International Journal of Advanced Research In Computer and Communication Engineering, Vol. 4, Issue 6, pp.265-267, June 2015.

[2] N.Prasad, P.V.V.Rajesh, “A Novel Parallel CRC Generation with Secured LBIST”, International Journal & Magazine of Engineering, Technology, Management and Research, Vol. 3, Issue 4, pp.96-99, April, 2016

[3] Computing.dcu.ie, ‘Polynomial codes for error detection’. [Online]. Available:http://www.computing.dcu.ie/~humphrys/Notes/Networks/data.polynomial.html

[4] Microchip.com, ‘SM843251-156’, Year published 2010. [Online]. Available:http://ww1.microchip.com/downloads/en/DeviceDoc/sm843251-156.pdf

[5] Steven R. King, Frank L. Berry, Michael E. Kounavis, ‘Performing a cyclic redundancy checksum operation responsive to a user-level instruction’, 20170242746, May 9, 2017.

[6] Prof. M. S. Kasar, Gauri Mandhare, Snehal Patil, Preeti Kumari, Sarika Yadav, “FPGA IMPLEMENTATION OF 8-BIT PARALLEL CYCLIC REDUNDANCY CODE”, International Education & Research Journal [IERJ], E-ISSN No: 2454-9916, Vol. 3, Issue 4, April 2017

[7] Qianqi Zhuang, Shawn Patrick Stapleton, ‘Power amplifier protection using a cyclic redundancy check on the digital transport of data’, 15380686, Jan 31, 2017

[8] Mahya Sam Daliri, Reza Faghih Mirzaee, Keivan Navi, Nader Bagherzadeh, “Ternary cyclic redundancy check by a new hardware-friendly ternary operator”, Microelectronics Journal, Volume 54, August 2016.

[9] Weirong Jiang, Gordon J. Brebner, Mark B. Carson, ‘Modular and scalable cyclic redundancy check computation circuit’, WO/2014/144941, May 24, 2016.

[10] Hye Ji KIM, Ji Hoon Kim, ‘Apparatus and method for cyclicredundancy check’, US20160371142 A1, Dec 22 2016.

[11] Philip Koopman, Kevin Driscoll, Brendan Hall, “Selection of Cyclic Redundancy Code and Checksum Algorithms to Ensure Critical Data Integrity”, National Technical Information Service (NTIS),Springfield, Virginia, March 2015

[12] Joong-Ho Lee, Control and Automation (CA), “CRC (Cyclic Redundancy Check) Implementation in High-Speed Semiconductor Memory”, 2015 8th International Conference on on Control and Automation, 25-28 Nov. 2015

[13] Yuanhong Huo, Xiaoyang Li, Wei Wang, Dake Liu, “High performance table-based architecture for parallel CRC calculation”, 2015 IEEE International Workshop, 22-24 April 2015


19

A Comprehensive Review on Comparative Study of

Different Techniques of Dispersion Compensation Riya Sinha1, Dr. Amit Kumar Garg2, Swati Tyagi3

1,3M.Tech. Scholar, Department of Electronics and Communication Engineering, DCRUST, Murthal

[email protected],

[email protected]

2Professor, Department of Electronics and Communication Engineering, DCRUST, Murthal

[email protected]

Abstract – Optical fiber communication (OFC) provides

high bit rate data communication. There are various

types of impairments and signal degradation

mechanisms which are involved with this high speed communication system. In case of long distance

communication, the most prominent impairment is

dispersion. Dispersion affects the information signal

very badly when it travels along a long distance. There

are different techniques available to compensate

dispersion. In this paper, the various methods of

dispersion compensation in single mode fiber created

because of dependence of group index to wavelength

known as chromatic dispersion are being discussed.

Various methods of dispersion compensation are

Dispersion compensation fiber (DCF) which

compensates dispersion at 1310nm and 1550 nm and

Fiber Bragg gratings (FBG) which compensate dispersion at wavelength around 1550nm. These

techniques and their performance measures are being

compared with respect to the BER, Q-factor, Eye height,

threshold etc. DCF technique increases the total losses

because of non-linear effects and cost of the optical

transmission system while FBG decreases the cost of the

system and has low insertion loss as well. Based on the simulation results, it has been concluded that which

technique is better for high speed long haul OFC

networks.

Keywords – Dispersion, DCF, FBG, dispersion

compensation, long haul communication.

I. INTRODUCTION

Optical fiber communication is one of the most

dominant topic in the communication system in today’s

era. It not only helps in increasing the transmission

speed but it also decreases the overall cost of the

communication system. But, for the application of

optical fiber in long haul communication, when the

signal is being transmitted at transmitter, some losses are

observed at the receiver end which results in some

information loss from the original data signal. In Single

mode fiber (SMF), chromatic dispersion and polarization mode dispersion takes place. Chromatic

dispersion occurs due to the dependence of speed of the

information carrying signal on the refractive index of the

fiber which further depends on the wavelength of the

signal carrying information. It can be compensated using

different dispersion compensation techniques. Fiber

Bragg Grating (FBG) and Dispersion Compensating

Fiber (DCF) are two most commonly used techniques

for dispersion compensation in long haul

communication. DCF compensation needs very high

negative dispersion coefficient for compensating

dispersion in a narrow band of frequency. This increases

the overall losses from non-linear effects and the cost of the optical communication system. In FBG technique,

the propagated light which satisfies the Bragg condition

is resonated by the grating structure and is reflected and

thus we get only a small part of the signal and the rest all

goes out of the fiber. It gives low losses and also

decreases the cost of the transmission system.

In this paper, the performance analysis between these two techniques is being compared in order to find

the better compensation technique for long distance

optical fiber communication. For a SMF of dispersion

parameter (16 ps/nm/km), a DCF of (-80 ps/nm/km) can

be used to compensate the dispersion. But using DCF

technique would be more useful for 100 km or 288 km

distances. On comparing the Q-factor of the two

compensation techniques, the Q-factor for DCF at 288

km is almost same as the Q-factor of FBG compensation

at 100 km. This means by using DCF, the optical signal

can travel three times more distance than FBG with the

same Q-factor.


20

The rest of the paper is organized as follows. The

theory of the dispersion compensation schemes

explained in section II. The results are given in section

III. Concluding remarks are given in section IV.

II. THEORY

Dispersion needs to be compensated by

dispersion compensation techniques. The first type is

DCF or Dispersion Compensating Fiber and the second

type is FBG or Fiber Bragg Grating. DCF and FBG

explained in section A and B.

A. Dispersion Compensating Fiber

In dispersion compensating fiber technique, a fiber

having a large negative dispersion is being used alongwith a standard fiber. The amount of light dispersed by a

normal fiber is reduced or even nullified by using a

dispersion compensating fiber having a very large value

of dispersion of opposite sign as compared to tha

standard fiber. DCF’s are used to upgrade the installed

1310 nm optimized optical fiber links for operations at1550 nm. The higher the dispersion coeff

DCF, the smaller will be required length.

compensation Fibers have a high negative dispersion

from -70 to -90ps/nm.km and can be used to compensate

the positive dispersion of transmitter fiber in C and L

bands.

Methodology:

Fiber based compensation is done by three methods

1. Pre-compensation: In this scheme, the DCF of

negative dispersion is placed before the SMF as shown

in Fig. 1(a).

2. Post-compensation: In this scheme, the DCF of

negative dispersion is placed after the SMF as shown in

Fig. 1(b).

3. Symmetrical or mixed compensation: In this scheme,the DCF of negative dispersion is once placed before

SMF and then placed after SMF as shown in Fig. 1

(a) Pre-compensation scheme

(b) Post-compensation scheme

The rest of the paper is organized as follows. The

dispersion compensation schemes are

given in section

III. Concluding remarks are given in section IV.

Dispersion needs to be compensated by various

The first type is

DCF or Dispersion Compensating Fiber and the second

rating. DCF and FBG are

In dispersion compensating fiber technique, a fiber

negative dispersion is being used along with a standard fiber. The amount of light dispersed by a

normal fiber is reduced or even nullified by using a

dispersion compensating fiber having a very large value

of dispersion of opposite sign as compared to that of the

DCF’s are used to upgrade the installed

1310 nm optimized optical fiber links for operations at 1550 nm. The higher the dispersion coefficient of the

length. Dispersion

igh negative dispersion

90ps/nm.km and can be used to compensate

the positive dispersion of transmitter fiber in C and L

Fiber based compensation is done by three methods –

, the DCF of

ced before the SMF as shown

, the DCF of

aced after the SMF as shown in

ed compensation: In this scheme, rsion is once placed before

n placed after SMF as shown in Fig. 1(c).

(c) Symmetric compensation scheme

Fig. 1: Different dispersion compensation schemes

In this, symmetric compensation method largely reduces

the non-linear effects as compared to pre

and post-compensation method. As the bit error rate

(BER) increases, output of the optical fiber also

increases. Symmetric compensation has minimum bit

error rate indicating the best performance in comparison

to pre and post compensation. Advantages of DCF are

that they can be easily constructed and highly reliable.

DCF provides continuous compensation over a wide

range of optical wavelengths. However DCF has high

insertion loss. A 60 km compensator can exhibit 6 dB of

loss or more. Because of this, DCF's are usually colocated with EDFA's which also increase the overall cost

of the fiber. Since DCF has a small core s

make it prone to certain types of nonlinearities. So DCF

also has high optical nonlinearities.

B. Fiber Bragg Gratings

Fiber Bragg gratings were introduced in 1980 and have

been a subject of research with several applications. It is

a reflective device composed of an optical fiber that

contains a modulation of its core refractive index over a

certain length. The Grating reflects light propagation

through the fiber when its wavelength corresponds to the

modulation periodicity. The reflected wavelen

called the Bragg wavelength, and is defined by the

relationship:

Using fiber Bragg gratings for dispersion

is a promising approach because they are passive optical

component fiber compatible, having low insertion losses

and costs. They are used as sensors, as wavelength

stabilizers for pump lasers, in narrow band WDM

add/drop filters and also as fi

compensation. Advantages of FBG are that it helps in

minimizing the overall cost of the fiber and also it also

has low insertion loss.

λ 2n

where n is the effective refractive index of thegrating in the fiber core and Λ

period.

Symmetric compensation scheme

dispersion compensation schemes

In this, symmetric compensation method largely reduces

linear effects as compared to pre-compensation

compensation method. As the bit error rate

(BER) increases, output of the optical fiber also

Symmetric compensation has minimum bit

error rate indicating the best performance in comparison

to pre and post compensation. Advantages of DCF are

that they can be easily constructed and highly reliable.

DCF provides continuous compensation over a wide

nge of optical wavelengths. However DCF has high

insertion loss. A 60 km compensator can exhibit 6 dB of

loss or more. Because of this, DCF's are usually co-located with EDFA's which also increase the overall cost

of the fiber. Since DCF has a small core size which

make it prone to certain types of nonlinearities. So DCF

also has high optical nonlinearities.

Fiber Bragg gratings were introduced in 1980 and have

been a subject of research with several applications. It is

device composed of an optical fiber that

contains a modulation of its core refractive index over a

certain length. The Grating reflects light propagation

through the fiber when its wavelength corresponds to the

modulation periodicity. The reflected wavelength (λ) is

called the Bragg wavelength, and is defined by the

Using fiber Bragg gratings for dispersion compensation

is a promising approach because they are passive optical

component fiber compatible, having low insertion losses

and costs. They are used as sensors, as wavelength

stabilizers for pump lasers, in narrow band WDM

add/drop filters and also as filters for dispersion

compensation. Advantages of FBG are that it helps in

minimizing the overall cost of the fiber and also it also

nΛ

is the effective refractive index of the grating in the fiber core and Λ is the grating


21

Methodology:

The structure of FBG varies via the refractive index, or

the grating period. The grating period can be uniform or

graded, and either localized or distributed in a

superstructure. The refractive index has two primary

characteristics, the refractive index profile which can be

uniform or apodized, and the offset which can be positive or zero.

On the basis of grating period, Fiber Bragg Grating can

be of four types –

1. Uniform gratings: In this gratings are done in fixed

interval shown in Fig 2(a).

2. Chirped gratings: In this gratings are done non-

uniformly as shown in Fig 2(b).3. Tilted gratings: In this gratings are done in uniform

manner but tilted as shown in Fig 2(c).

4. Superstructure: In this gratings are uniformly grouped

as shown in Fig 2(d).

Fig. 2: Types of Fiber Bragg Gratings

The most common advantage of FBG is low insertion

loss (IL). Typically, a 120-km FBG module has an

insertion loss in the range of 3 to 4 dB, depending on the type. The ability to tolerate high optical powers without

any loss caused by nonlinear effects is also one

prominent characteristic separating the FBG-DCM from

the DCF-DCM. A DCF displays non-linear effects at

low optical powers, but the FBG-DCM won’t introduce

such effects even at the highest power levels.

III. RESULTS AND DISCUSSIONS

In case of the FBG compensation, the minimum BER for

100 km SMF is -52.9 when it decreases to -27.45 for a

transmission path length of 200 km. If we go further

distance with the similar parameters, we get minimum

BER of -6 for 300 km SMF. This is shown in Fig. 3(b).

On the other hand, for DCF based system, we get minimum BER of -1000 for duration from 0.36 to 0.72

for 100 km SMF. If the length increases to 200 km, it

still remains the same for duration of 0.38 to 0.54. At

SMF length 300 km, we get minimum BER of -23

which is almost the same as the BER of 200 km SMF for

FBG compensation. This is shown in Fig. 3(a). All the

BER values are measured in log of BER. From this BER analysis, we can decide that the DCF technique can

provide better performance than the FBG technique in

long haul communication.

Another parameter for performance analysis is

the Q-factor. In the simulation three different values of

maximum Q-factor for three SMF lengths are being

measured. For FBG system, we got maximum Q-factor

15.12, 10.75 and 4.73 for 100, 200 and 300 km,

respectively. This is shown in Fig. 4(b). For DCF

system, maximum measured Q-factor is 177.21, 47.75

and 9.9 for 100, 200 and 300 km, respectively. So, the

Q-factor of 300 km using DCF is almost the same as the

Q-factor of 200 km for FBG technique. This is shown in

Fig. 4(a). Thus, we can say that using DCF technique

optical signal can travel more distance then that of FBG

compensation technique.

(a) DCF minimum BER 100 – 300 km


22

(b) FBG minimum BER 100 – 300 km

Fig. 3: Minimum BER of DCF and FBG compensation schemes for

different distances

(a) DCF maximum Q-factor 100 – 300 km

(b) FBG maximum Q-factor for 100 – 300 km

Fig. 4: Maximum Q-factor of DCF and FBG compensation

schemes for different distances

300 km

Fig. 3: Minimum BER of DCF and FBG compensation schemes for

300 km

300 km

factor of DCF and FBG compensation

schemes for different distances

(a) DCF for 100 km

(b) DCF for 200 km

(c) DCF for 300 km

Fig. 5: Eye diagrams for DCF compensation for 100, 200 and

300 km distances

DCF for 100 km

DCF for 200 km

DCF for 300 km

Fig. 5: Eye diagrams for DCF compensation for 100, 200 and

300 km distances


23

Fig. 6: Eye diagram for DCF scheme for 400 km distance

(a) FBG for 100 km

(b) FBG for 200 km

Fig. 6: Eye diagram for DCF scheme for 400 km distance

(c) FBG for 300 km

Fig. 7: Eye diagram for FBG compensation scheme for 100, 200 and

300 km distances

Finally, the Eye diagram for the two mentioned

techniques have been analyzed. Eye diagram is the

oscilloscope display of a digital signal. For the FBG

technique, we get acceptable eye shapes for up to

200km. The eye shape is not very good for a distance of

300km. This is shown in Fig. 7. On the other hand, for

DCF compensation a quite good eye shape for 300 km

SMF length is being observed. Though it

degraded to a poor shape after travelling 400 km. This is

shown in Fig. 5 and Fig. 6. Up to this poi

decided that, DCF technique is a better option than FBG

compensation for long distances (e.g.

After comparing all the performance parameters,

it can be asserted that DCF can perf

compensation even for a longer distance.

technique, minimum BER and Q-factor for 300 km

be achieved as those using FBG at 200 km. Even eye

height of DCF compensation for 300 km is better than

that of FBG at 200 km. So, it can beis a better technique than FBG for long

speed optical fiber communication.

IV. CONCLUSION AND FUTURE DIRECTIONS

Different dispersion compensation techniques for long

haul communication have been successfully studied.

After analyzing all the parameters of

different compensation techniques, it has been

concluded that the FBG technique is acceptable for

certain distance, but after travelling a longer distance, itsperformance degrades. We cannot make up that

degradation by altering any existing component of our

system. DCF can be a solution to overcome this

FBG for 300 km

Fig. 7: Eye diagram for FBG compensation scheme for 100, 200 and

300 km distances

Finally, the Eye diagram for the two mentioned

techniques have been analyzed. Eye diagram is the

oscilloscope display of a digital signal. For the FBG

technique, we get acceptable eye shapes for up to

200km. The eye shape is not very good for a distance of

300km. This is shown in Fig. 7. On the other hand, for

good eye shape for 300 km

. Though it has been

degraded to a poor shape after travelling 400 km. This is

6. Up to this point it can be

that, DCF technique is a better option than FBG

sation for long distances (e.g. 200km or above).

After comparing all the performance parameters,

that DCF can perform as well as FBG

r a longer distance. Using DCF

factor for 300 km can

FBG at 200 km. Even eye

300 km is better than

at of FBG at 200 km. So, it can be concluded that DCF chnique than FBG for long distance high

speed optical fiber communication.

AND FUTURE DIRECTIONS

Different dispersion compensation techniques for long

haul communication have been successfully studied.

After analyzing all the parameters of interest for

different compensation techniques, it has been

G technique is acceptable for a

certain distance, but after travelling a longer distance, its performance degrades. We cannot make up that

degradation by altering any existing component of our

system. DCF can be a solution to overcome this


24

difficulty. Even DCF is not sufficiently good after a

certain distance.

As it has been observed that DCF does not work well for

a distance like 400 km or more. Thus, a more suitable

method which can be used for longer distances like 500

km or more would be found in future. It has also been

planned to work on different DCF techniques and

compare them to find a better solution for long distance

optical fiber communication. Any two compensation

schemes can also be combined together for better results.

REFERENCES

[1] Shivinder Devra, Gurmeet Kaur, “Different

Compensation Techniques to Compensate Chromatic

Dispersion In Fiber Optics”.International Journal ofEngineering and Information Technology;volume-3,

issue-2: pp. 1-4;2011

[2] N. Ravi Teja, M. Aneesh Babu, “Different Types of

Dispersions in an Optical Fiber”.International Journal of

Scientific and Research Publications; volume-2, issue-

12: pp. 1-5;2012

[3] Md. J. Islam, Md. S. Islam, “Dispersion

Compensation in Fiber Communication Using Fiber

Bragg Grating”.Global Journal of researches in

engineering; volume-12, issue-2:pp. 21-25;2012[4] G. H. Patel, R. B. Patel, “Dispersion compensation in

WDM network using Dispersion compensating

Fiber”.Journal of Information, Knowledge and Research

in Electronics and Communication Engineering;

volume-2, issue-2: pp. 662-665;2013

[5] Pawan Kumar Dubey, Vibha Shukla, “Dispersion inOptical Fiber Communication”.International Journal of

Science and Research; volume-3, issue-10: pp. 236-

239;2014

[6] Gagandeep Singh, Jyoti Saxena, “Dispersion

Compensation Using FBG and DCF in 120 Gbps WDM

System”. International Journal of Engineering Science

and Innovative Technology; volume-3, issue-6: pp. 514-

519;2014

[7] Ashima Bhardwaj, Gaurav Soni, “Performance

analysis of 20Gbps Optical Transmission system using

Fiber Bragg Grating”.International Journal of Scientific

and Research Publications; volume-5, issue-1: pp. 1-

4;2015

[8] M Singh, “Different Dispersion CompensationTechniques in Fiber Optic Communication

System”.International Journal of Advanced Research in

Electronics and Communication Engineering; volume-4,

issue-8: pp. 2236-2240;2015

[9] R Singh., Prof. L Kumar, “Dispersion compensation

in Optical Fiber communication for 40 Gbps usingdispersion compensating Fiber”.International Journal for

Science and Emerging Technologies with Latest Trends;

volume-19, issue-1: pp. 19-22;2015

[10] A. J. Aggarwal, M. Kumar, “Comparison of

different techniques of Dispersion

compensation”.International Journal of Electronics and

Computer Science Engineering; volume-1, issue-2: pp.

912-918

[11] M. Singh, Rajbeer Rao, “Analysis of Dispersion

Compensation using Fiber Bragg Grating in Optical

Fiber Communication System”.International Journal of

Computer Applications; volume-126, issue-5: pp. 1-

5;2015

[12] Manpreet Kaur, H. Sarangal, “Dispersion

Compensation with Dispersion Compensating Fibers”.

International Journal of Advanced Research in

Computer and Communication Engineering; volume-4,

issue-2: pp. 354-356;2015

[13] Naveen Dalal, Dr. Amit Kumar Garg, “A

Comprehensive Study of Various Compensation

Techniques in High Speed Single Mode Optical Fiber

Communication”. International Journal of Recent Trends

in Engineering and Research; volume-2, issue-5: pp.

275-278;2016


25

An Improved Authentication Security Scheme

Nidhi Sharma

Ph.D Scholar

Dept of CSE, Baba Mast Nath

University,Rohtak

[email protected]

Dr. V.K Srivastava Professor


University,Rohtak

Alok Sharma Ph.D Scholar


University, Rohtak

Abstract—With the emergence of Big Data and Cloud

Computing, user authentication becomes more and more

difficult as Password security is a major issue for any

authenticating process. Password plays an important role in

various applications like internet services, net banking, ATM

machines etc. But passwords are not much safe to provide the

security to the users because of large no of attacks. Different

researches in past have proposed different techniques to make

the passwords most secured. In, this paper, a new technique is

proposed which involves three steps-salting, hashing, and then

generating a masked list. This masked list is stored in the

user’s password file. If any intruder attempts to log in using

any of these masked list words then an alarm is raised for the

application and the application can block that user or address.

Keywords— Big Data, Cloud computing, salting, hashing,

masked list, passwords security, honeywords.

1. INTRODUCTIONAs large amount of data is to be handled by cloud

computing so the foremost requirement in today’s digital world is information security which is normally secured by some authentication process.. There exist various methods for authentication (e.g. passwords, PINs etc) but the password based systems are the most generally used methods for authentication. But to store user passwords within databases as plaintext or only with their unsalted hash values is a blunder mistake. Many successful hacking attempts which enabled attackers to get unauthorized access to sensitive database entries including user passwords have been practiced in the past.[4]

Revealing of password files is a serious security problem that has affected many users and companies like Yahoo, LinkedIn, eHarmony and Adobe [2], since revealed passwords cause many possible cyber-attacks. These recent crisis has indicated that the weak password storage methods are presently in place on many web sites. For example, the passwords in the eHarmony system were stored using MD5 hashes without salt and also the LinkedIn passwords were also stored with unsalted hash values by using SHA-1 algorithm [3]. Even an attacker gets success to steal password file with the help of password cracking techniques it is easy to get most of the plaintext passwords.

According to this, there are two issues that should be acknowledged to control these security problems: First is passwords must be protected by taking relevant providence and storing with their hash values enumerated through salting mechanisms. And second is that a secure system should detect whether a password file is revealed or not to take relevant actions.

Lot of work is already defined by different researchers for password security. Earlier, Juels and Rivest used decoy passwords against hashed password databases to detect attacks. In there technique, the real password is stored with several honeywords for each user account in order to sense imitation. By using honeywords attacker cannot be sure if it is the real password or a honeyword even he has file of hashed passwords. And, if an attacker attempt to log in with a honeyword will trigger an alarm which notifying about password file breach to the administrator. Recently, Imran Erguler analyze the honeyword system and had suggested a different approach that selects the fake words from existing user passwords in the system to provide sensible honeywords and also to reduce storage cost of honeyword scheme. He give some remarks about the security of the system and pointed out that the key item for this method is the generation algorithm of the fake words such that they shall be unidentifiable from the correct passwords.

II.SALTING

A salt is random bits of characters used to modify a password before their hash value is calculated and it makes difficult to reverse the hashed password. Salt can be added to the hash to prevent a collision if another user in the system has selected the same password. Salt can be a combination of letters, numbers, characters or special characters etc. Salt will be added to make it more difficult for an adversary to crack password by using cracking methods because adding salt to a password hash prevents an attacker to check known dictionary words across the entire system [1]. The attacker has to produce every possible salt value. If the salt is longer and more complex, the greater time is required to crack the password. For every hash value different salt bits must be generated and for intruder a new dictionary must be generated for each stored password.


26

III HASHING

Hashing is applied on the input password which is selected by the user. Hashing is a ‘one-way function’. This hash function takes input of any length, and after hashing produces a unique output of constant length. It is hard to decipher the hash and any attempt to crack it is practically infeasible. When storing passwords, the password is hashed by using hashing algorithm and then the resulting hash is stored in password file instead of plain text password.

IV.MASKING

It is basically the insertion of fake passwords associated with each user’s account. When an attacker gets the password list, he retrieves many password candidates for each account but cannot be sure about which password is real.[4] For each user account, the real password is stored with different mask words in order to sense impersonation. If different mask words are selected properly, an attacker who steals a file of hashed passwords cannot be sure whether the password is real or fake for any account. Here, Masking is a result of hashing which generates different permutations of real password. The main purpose of masking is to hide the real password with different fake passwords.

V.PHASES OF THE PROPOSED ALGORITHM

In the first step, add random strings of bits as a salt by applying salting techniques to password before their hash value is calculated and in second step after hashing, the crash list of real password is generated by using differential masking process. These two steps associated with this work are shown in figure1 :

Fig1: Proposed model

Phase I

Salting and Hashing Process

Password salting is adding a random string of characters to passwords before their hash is calculated to make

password hashing more secure and it makes them difficult to reverse as in Fig 2. The random string of characters can be a combination of letters, numbers and other special characters. This represents a salting process which generates salt bits for a specific binary string as shown in Table1.

The steps are as follows:

1. Get password

2. Generate salt using trusted random method

3. Append salt to original password

4. Generate salt hash password using hash function.

Fig2: Details of hashed passwords using the hashing and salting technique

Table1: Hashed Password after salting

From the result it is clear that password security using salting and hashing pattern provides a higher security because the user’s original ‘clear text’ password is not stored in database. Even if the password file is stolen by hacker/attacker or password file is public. Looking at the password file length of original password cannot be predicted which makes it difficult for hacker to break the password.

User

Password

Hashed Password after

salting

students123

india2020

mitchell

world2015

secrethash

worldwide45

3e7aca6e3d98efc1e1869e6c

2839216d410b4d43

f676a666362637b

8bd198c9398859d9

3b78e8e0a61afbc840a5

bef696a6d3c75404a871c1a

Step: I

Performing Salting and Hashing

(Add random salt bits to passwords before their

hash value is calculated)

Step: II

Differential Masking Process

(Crash List is generated)


27

Phase II Masking Process

(To generate different fake passwords of real password)

For each user account, the real password is stored with different fake words in order to sense impersonation. Here, different permutations of real password are generated.

Suppose user selects a password ‘nidhi12’then this techniques creates a mask list of real password by masking process with different permutations of Q, I, R, X, Q-1 as shown in Fig 3

Fig3: To Generate different fake passwords of real

password

VI.CONCLUSION

This paper deals with user authenticity which is the most important aspect and introduce a new technique which converts the password provided into a series of hexadecimal strings (mask list) which are formed by applying hashing process using masking. One of these words is over hash string while the others form mask list. If any malicious user gets an access to the password file and attempts to login using any one of the words form the mask list then the system generates an alarm for the application concerned.

REFERENCES

[1] Search Security http://searchsecurity.techtarget.com/

definition /salt, Retrieved 15th Oct, 2011

[2] D. Mirante and C. Justin, “Understanding Password Database Compromises”, Department of Computer Science, Polytechnic Institute of NYU, Tech. Rep. TR-CSE-2013-02, 2013.

[3] K. Brown, “The Dangers of Weak Hashes, “SANS Institute Information Security Reading Room, Tech. Rep. 2013.

[4] Emin Islam Tatli, “Cracking More Password Hashes With Patterns”, Department of Electrical and Electronics Engineering, Istanbul Medipol University,2014.


28

Travel-Time Prediction: A Short Survey Vivek Agrawal Dr. J.N Singh Dr. Ashish Negi

Galgotia College of Engg and Tech Galgotia University UTU

Greater Noida Greater Noida Dehradun

[email protected] [email protected] [email protected]

Abstract - Travel-time information could be applied

in various fields and its purpose is to improve the

reliability and service quality. From the traveler’s point

of view, the travel-time information helps to save the

time and improve reliability through the selection of

travel routes pre-trip and en-route. One way to

accomplish this is to provide driver or passenger with

current traffic information throughout their trip. In the

application of logistics, travel-time information could

reduce the delivery costs, increase the reliability of

delivery, and improve the service quality. For traffic

managers, this information is an important aspect for

the smooth operation of traffic system.

Keywords- Intelligent Transport Systems, Travel

time prediction, Micro-simulation model.

1. INTRODUCTION

Travel time has been identified as an important performance measuring aspect and regular surveys are now being conducted in the capital cities by state road authorities. Meanwhile, a large number of research studies and literature reviews concerned with the field of travel-time prediction have demonstrated the importance of travel time information in practical applications of transportation and logistics. Some of the methods strive to measure travel time directly using vehicle re-identification technology ([2, 6, 1]). These methods require video cameras or other special purpose equipments. In [1], Coifman proposed a methodology which can match a portion of vehicles using vehicle length measurements, but the method still requires double-loop speed measurements.

Traffic data is divided in following three categories: historical, current, and predictive [16]. Travel-time prediction is usually distinguished into two main approaches: statistical models and analytical models. Statistical models is characterized as data driven methods that generally use a time series of historical and current traffic variables such as travel times, speeds, and volumes as input. Analytical models predict travel times by using microscopic or macroscopic traffic simulators, such as METANET [18], [19], NETCELL [21], and MITSIM [20].

Given the historical travel-time data f(t-1), f(t-2), ……f(t-n) and at time t-1, t-2, ……t-n respectively, we can predict the future values of f(t+1), f(t+2),…..by analyzing the historical data set. On the basis of the relation between the time-variant historical data set and its results, the prediction of future values can be done. Numerous statistical methods on the accurate prediction of

travel time have been proposed, such as the ARIMA model [17], linear model [15], and neural networks [7].

The purpose of this paper is to review the applications and the prediction methodologies of travel-time from previous research and highlight some of the results from survey data that can be used in the further research. The goal of the research is trying to develop and test a potential research methodology to promote the efficiency and accuracy of travel-time prediction for its further applications and development in arterial roads.

This paper is organized as follows. Section 2 covers related work. Section 3 defines the methodology developed. Section 4 describes the experiments. Finally, Section 5 contains the conclusions.

II.RELATED WORK

The concept of Trajectory Pattern introduced in [11] defines a sequence of geo-referenced objects Sof size m and a list of Temporal Annotations A ofsize (m - 1), whose values represent the temporaldistance between two consecutive geo-referencedobjects belonging to S. The information of thetrajectory patterns, i.e., the geo-referenced objectsand temporal annotations, are extracted from a setof trajectories of moving objects (in this case, asequence of triple <latitude, longitude, timestamp>)by identifying regions of interest (geo-referencedobjects) often visited by moving objects. Thecomputation of temporal annotations is formalizedas a problem of density estimate, whose values areused to calculate the time difference betweentimestamps of two consecutive triples in atrajectory.

Liao et al. [12] shows that a person after certain time follows a routine and the same routine can be "learned" by the existing system. For the description of the routine, the author take care of three characteristics: location, change of transportation mode and main locations. On the basis of above characteristics, the authors can develop a mechanism to predict the location, the changes in the transportation mode, and the prime locations that he or she will pass.

Monreale et al. [3] authors created a mechanism called WhereNext to estimate the next possible locations of a moving object with more and more accuracy. The mechanism uses the previously extracted movement patterns, which represent possible behaviors of moving objects, like pattern of regions generally visited by object in


29

motion. A decision tree, namely a T-Tree pattern, is constructed according to trajectory patterns (as defined in [1]) that were already extracted and evaluated

IdŽ et al. [4] works on the method of predicting travel time for an arbitrary pair of source-destination on a map. The proposed method works in the direction of probabilistic prediction of the travel time along an unknown path (a sequence of links), and the path similarity is defined as the kernel function. Their work introduces two novel ideas. The first one is the use of a kernel string to represent the similarity between paths. The second one is the application of the Gaussian process regression for predicting the travel time.

Wu et al. [5] have similar goals, but their approach is different. The authors use Support Vector Regression (SVR) for the estimation of the time that a particular part of highway will be covered, based on the collection of data by speed gauges posted along specific roadways in Taiwan. Also according to the authors, SVR had better results than Artificial Neural Networks because SVR is more amenable to generalization than Artificial Neural Networks.

The works [9] and [10] of Alvarez et al. use the definition of the conceptual model of trajectories with segmentation by stops and moves proposed in the work cited above [8]. The first makes the discovery of movement patterns for trajectories based on data mining techniques. It proposed a framework to model and performs the mining for discovery of movement semantic patterns. The main focus is on discovering the most frequent moves between two stops, where each stop is seen as an application's interest.

Still based on the conceptual model of Spaccapietra et al. [8], the works of Palma et al. [11] and Rocha et al. [12] followed by clusteringapproaches to knowledge discovery. The firstproposes a solution to the discovery of importantplaces in the trajectory based on speed, i. e., and thestops discovery. The work is based on the simpleidea that segments with lower speed may representlocal interest in two parts: the first part of theprocess operates in the discovery of potential stopsand the second based on the outcome of the first,analyzes them related the geographical information.The second seeks to discover places of interestbased on changes of direction, considering areaswhere this aspect is important as the discovery ofplaces where the ships perform sea fishing,preventing them engaging activity in forbiddenplaces.

On another line of work, Guc et al. [13] supports the idea that the trajectory data can be used to facilitate the manual process of trajectories semantic annotation. For this, they propose a trajectory annotation model based on notion of episodes that allows the separation between the physical and semantic part and also architecture to program to perform semantic annotations. Yan et al. [14] work stays on the same line and proposes a

framework (SeMiTri) of general propose to various domain applications (i.e. to heterogeneous trajectories) that lets you manage and enrich trajectories with semantic annotations, allowing the application can benefit from a semantic representation of movement through the inferences made from space-time properties of the position raw data (e.g. extraction of stops and moves, tracking its direction or movement pattern), geographical regions covered by the trajectories (e.g. streets and notables local) and application objects related to the trajectories (e.g. customers, parking).

The framework takes advantage of the proposed model for semantic trajectories, where a trajectory is represented as a sequence of semantic episodes that correspond to a interpretation of the application and also presents three algorithms for performing trajectory annotations, one based on regions of interest, another based on the path and the latter based on points of interest, these algorithms are responsible for covering the peculiarities of the heterogeneity of trajectories, as trajectories of vehicles, people walking, animals, etc.

III.METHODOLOGY

The methodology for estimating the time that will be spent by a vehicle between any two points is focused on: (1) pre-processes raw trajectory data to create aggregated data and (2) To learn the behavior of the vehicles using this aggregated data.

A. Moves and stops

Any moving object moves and stops. The stops have a goal (semantic) and a period of time: to take a bus, a person waits for 10 minutes at a bus stop or a vehicle stops for 5 minutes to refuel at a gas station. Figure 1 shows a situation in which a moving object stops at P1, P2 and P3 along the path between A and B in the space S.

Taking into account the moves and stops of moving objects, the travel time prediction mechanism uses data mobility (GPS coordinates with timestamp and speed) to "learn" the behavior of vehicles (and their drivers) in a particular region and calculate the time a vehicle (driven by the same driver) will take to move between two distinct points.


30

B.Mobility data extraction

The travel time estimation mechanism is characterized by the historical movements of vehicles over time, as the goal is to estimate the travel time of a vehicle, associated with a driver, between two specific points. Thus, it is necessary to know when and where the vehicle has moved and when and where it has stopped. This is done by pre-processing the data to create more qualitative information indicating moves and stops (figure 2) when compared to simply positioning the vehicles in time (figure 3). A stop is detected when the vehicle does not travels s meters in t minutes, where s and t are parameters of the stop detection function and the stop location is a known place based on the application context

Figure 2 - Extraction of data movement

through the positioning data.

The model in figure 4 illustrates the relationship between move, stop, vehicle, driver and region. Whereas the data are divided by regions, every movement and stop of a vehicle are associated with a region and a driver.

C.Trafficked segment processing

On the path between two stops, the speed of vehicle varies, resulting in some parts to be slower than others. The segmentation helps in finding the time vehicle will take from the current position to the next known (again, based on the application context) location it will stop with segment slices of different average speed. The segmentation is done in sections, as shown in figure 3. Considering the moves of a vehicle between the stops P1 and P2, four segments are defined: S1, S2, S3 and S4. Segment S1 starts at P1 and ends at P2. Segment S2 starts at the position indicated in the figure and ends at stop P2. Segment S3 starts at the position indicated in the figure and ends at stop P2. And finally, segment S4 starts at the position indicated in the figure and ends at stop P2. Another way to segment the movement would be as follows: the first segment would be from P1 to S2. The second segment would be from S2 to S3. The third segment would be from S3 to S4. And finally the last segment would be from S4 to P2. But the first targeting option was chosen

Figure 3 - Illustration of movement

segmentation.

The segmentation of movements creates a new entity in the model illustrated in figure 4: the Segment entity. Thus, a movement (Move) has a set of entities Segment. The entity Segment has as attribute the start point (or coordinate), the end point (or coordinate), the start timestamp, the end timestamp and the distance traveled.

Figure 4 - relationship between move, stop, vehicle,

driver and region

IV.MODELING APPROACHES:

Simulation method The simulation method uses a traffic model to

calculate the routing of vehicles from first principles based on established modeling algorithms. It is potentially a very powerful tool, provided that the underlying model is robust and there is sufficient data to allow accurate calibration.

Statistical method The statistical method is based on mathematical

relationships developed through statistical regression analysis. It is the simpler of the two methodologies which means it should be quicker and cheaper to implement, but this also limits its capabilities.


31

Table1: Analysis of Approaches

Features Simulation-based approach Statistics-based approach

Basis of system Model calculating vehicle

routes from first principle.

Mathematical relationships between road segment and

time

Network

size/complexity Can be highly complex, eg.

Urban road network.

Can include route choice.

Simple networks (eg single

corridor network). Limited

route choice.

Data requirements Complex: Additionally

requires data on travel

behavior

Basic: ‘Live feed’ of traffic

flows, speeds and external

events, plus historic log.

Ability to predict

during events

Good. Plus, can be used to

pre-empt planned events

Good if event has occurred

before, otherwise limited.

Cost to implement High. Modest.

Cost to monitor and maintain

High. Will generally need

constant monitoring and

require periodic recalibration.

Low. Will be largely self-

maintaining and will

require low levels of monitoring.

Software

available? Yes. No.

V.PREDICTION METHODOLOGY AND

ERROR MEASUREMENTS

Suppose that the current time is t and we want to

predict y(t+l) at the future time t+l with the

knowledge of the value y(t-n), y(t-n+1), ……..y(t)

for past time t-n, t-n+1, ……t. respectively. The

prediction function is expressed as

y(t+l) = f(t, l, y(t), y(t-l), …….y(t-n))

We examine the travel times of different

prediction methods. Relative mean errors (RME)

and root-mean-squared errors (RMSE) are applied

as performance indices

RME = ∑=

−n

i Yi

YiYi

n 1

*1

RMSE = ∑=

−n

i Yi

YiYi

n 1

2*1

Where Yi is the observed value and Yi* is the

predicted value.

1. Travel-Time Predicting Methods

To evaluate the applicability of travel-time prediction, some common travel-time prediction methods are exploited for performance comparison

A. Current Travel-Time Prediction Method

This method computes travel time from the data available at the instant when prediction is performed [14]. The travel time is defined by

T(t,∆) = ∑−

=

+

∆−

−1

0

1

),(

L

i i

ii

txv

xx

Where ∆ is the data delay, L is the number of

sections, (xi+1-x) denotes the distance of a section

of a highway, and v (xi, t-∆) is the speed at the start

of the highway section.

B. Historical Mean Prediction Method

This is the travel time obtained from the average

travel time of the historical traffic data at the same

time of day and day of week.

T(t) = ∑=

w

i

tiTw 1

),(1

Where w is the number of weeks trained and is the

past travel time at time of historical week.

Table: Prediction results in RME and RMSE of different predictors for traveling different distances (all

testing data points)

RME Current-time

predictor

Historical-mean

predictor

SRV-Predictor

45 km 9.29% 12.52% 3.91%

161 km 3.88% 5.01% 1.71%

350 km 2.85% 2.56% 0.96%

RMSE Current-time

predictor

Historical-mean

predictor

SRV-Predictor

45 km 28.75 16.20% 6.79%

161 km 9.98% 6.66% 2.57%

350 km 5.49% 3.42% 1.33%

The results in Table II show the RME and RMSE of different predictors for different travel distances over all the data points of the testing set. They show that the SVR predictor reduces both RME and RMSE to less than half of those achieved by the current time and historical-mean predictors for all different distances.

All three of the predictors predict well for long distance (350 km), but this makes it difficult to compare the performances of the three predictors.

VI.FURTHER RESEARCH

This paper provides a review of travel-time studies that includes variables of travel time, measurement of travel time, methodologies of travel-time prediction and estimation, research difficulties. The application of micro-simulation techniques could help to overcome the current difficulties of travel-time studies. In addition, the link up with the SCATS traffic control system might extend travel-time prediction from isolated


32

environment to arterial road sections by minimizing the uncertainty in factors from signal systems.

Adapting micro-simulation techniques to be a test-bed can solve the problem of data shortage and also perform the various strategies on the test-bed. The method could contribute to the efficiency of related research, as long as a well validated simulation model is available. The final stage of the research will still involve the use of real traffic data from field survey to validate and adjust the developed travel time prediction model.

There are a many areas where further research could be done, in an attempt to improve the model further:

• As discussed in earlier notes, there are anumber of issues with the underlying data, namely that there are large gaps in the data for some of the links. Sourcing of improved or alternative data sources, or improved interpolation may result in more accurate predictions.

There should also be consideration of additional explanatory variables, such as the weather, which could not be considered in this study due to data issues.

VI.CONCLUSION

Predicted travel-time information provides the capacity for road users to organize travel schedule pre-trip and en-trip. It helps to save transport operation cost and reduce environmental impacts. Besides, accurate travel time information also helps delivery industries to promote their service quality by delivering on time. However the development of travel time estimation and prediction are suffered from the shortage of traffic data sets and too much interference from transport environment.

A comprehensive literature review was undertaken which examined a wide range of previous studies and research. The outcome of this review indicated that of the two potential solutions the statistical approach was the most suitable, based on the availability of the data and the client’s requirements. The travel time estimation for moving vehicles involves a large number of variables, which makes the solution complex. Changes in traffic behavior greatly influence the travel estimated time of the vehicle. But the vehicle driver itself, the vehicle’s features, the vehicle load, if the day is before a holiday, are also variables to be considered, in addition to the vehicle identifier.

REFERENCES

[1] B. Coifman. “Vehicle reidentifcation and traveltime measurement in real-time on freeways using the existing loop detector infrastructure”. Transportation Research Record, (1643):181191, 1998.

[2] B. Coifman, D. Beymer, P. MeLaughlan, and J.Malik. “A real-time computer vision for vehicle tracking

and traffic surveillance. Trasnportation Research”: Part C, 6(4):271288, 1998.

[3] Monreale, A. et al. “WhereNext: a location predictor on trajectory pattern mining”. KDD 2009, p. 637–646, 2009.

[4] IdŽ, T. and Kato, S. “Travel-Time prediction using Gaussian process regression: a trajectory-based approach”. SIAM Intl. Conf. Data Mining (2009)

[5] Wu, C-H., Wei, C-C., Ming-Hua Chang, M-H.,Su, D-C. and Ho, J-M. “Travel Time Prediction with Support Vector Regression”. Proc. Of IEEE Intelligent Transportation Conference. October, 2003 pg. 1438-1442.

[6] R. Kuhne and S. Immes. “Freeway control systems for using section-related traffic variable detection”. In Pacific Rim TransTech Conference Proceedings, volume 1, pages 5662. ASCE, 1993.

[7] J.W. C. van Lint, S. P. Hoogendoorn, and H. J.van Zuylen, “Robust and adaptive travel time prediction with neural networks,” presented at the Proc. 6th Annual Transport, Infrastructure and LogisticsCongr., Delft, The Netherlands, Dec. 2000.

[8] Spaccapietra, S., Parent, C., Damiani, M. L., deMac•do, J. A., Porto, F., and Vangenot, C. “A conceptual view on trajectories”. Data & Knowledge Engineering 65, 1 (2008), 126-146.

[9] Alvares, L. O., Bogorny, V., de Mac•do, J. A. F.,Moelans, B., and Spaccapietra, S. “Dynamic modeling of trajectory patterns using data mining and reverse engineering” at the Tutorials, posters, panels and industrial contributions at the 26th International Conference on Conceptual Modeling - ER (Auckland, New Zealand, 2007), A. H. F. L. L. M. John Grundy, Sven Hartmann and J. F. Roddick, Eds., vol. 83 of CRPIT, ACS, pp. 149-154.

[10] Alvares, L. O., Bogorny, V., Kuijpers, B., deMac•do, J. A. F., Moelans, B., and vaisman, A. A. “A model for enriching trajectories with semantic geographical information”. In Proceedings of the 15th ACM International Symposium on Geographic Information Systems (New York, NY, USA, 2007), GIS '07, ACM, pp. 22:1-22:8.

[11] Giannoti, F., Nanni, M., Pinelli, F. andPedreschi, D. “Trajectory pattern mining”. KDD 2007, p. 330-339.

[12] Liao, L., Patterson, D., Fox, F. and Kautz, H.“Learning and inferring transportation routines”. Artificial Intelligence, v.171 n.5-6, p.311-331, April, 2007.

[13] S. Ruping. mySVM Software [Online]http://www-ai.cs.uni-dort- mund.de/SOFTWARE /MYSVM/

[14] D. Park and L. R. Ritett, “Forecasting multiple-period freeway link travel times using modular neural networks,” presented at the 77th Annu. Meeting Transportation Research Board, Washington, DC, Jan. 1998.

[15] X. Zhang, J. Rice, and P. Bickel, “Empirical comparison of travel time estimation methods, Tech. Rep. Dept”. Stat., Univ. California, Berkeley UCB-ITS-PRR-99-43, Dec. 1999.

[16] R. Chrobok, O. Kaumann, J.Wahle, and M.Schreckenberg, “Three categories of traffic data: Historical, current, and predictive,” in Proc. 9th Int. Fed. Automatic Control Symp. Control in Transportation Systems, 2000, pp. 250–255.

[17] E. Fraschini and K. Ashausen, “Day on Day Dependencies in Travel Time: First Result Using ARIMA Modeling: ETH”, IVT Institute for Verkehrsplanung, Transporttechnik, Strassen-und Eisenbahnbau, Feb. 2001.


33

[18] Q. Yang and H. N. Koutsopoulos, “Amicroscopic traffic simulator for evaluation of dynamic traffic management systems,” Transport. Res., pt. C, vol. 4, no. 3, pp. 113–129, 1996.

[19] A. Messmer and M. Papageorgiou, “METANET: A macroscopic simu-lation program for motorway networks,” Traffic Eng. Contr., vol. 31, no. 549, pp. 466–470, 1990

20] A. Kotsialos, M. Papageorgiou, C. Diakaki, Y.Pavlis, and F. Mid-delham, “Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET,” IEEE Trans. Intell. Transport. Syst., vol. 3, pp. 282–292, Dec. 2002.

[21] R. Cayford, W. H. Lin, and C. F. Daganzo,“The NETCELL simulation package: Technical description,” Univ. California, Berkeley, CA PATH Res. Rep. UCB-ITS-PRR-97-23, 1997.

s


34

A Survey of Traffic Management and Behaviour

in A QOS Environment.

Tarun Gupta Department of Electronics and Communication Engineering, Research Scholar, DCRUST University, Murthal,

Haryana, India [email protected]

Amit Kumar Garg Department of Electronics and Communication Engineering, Professor in ECE Dept., DCRUST University,

Murthal, Haryana, India [email protected]

Abstract— In this paper an efficient Quality of Service (QoS) oriented traffic

management schemes has been proposed which is based on several types of

packet handling techniques. Queuing is one of the vital mechanism in a traffic

management system. Each router in the network must implement some queuing

discipline that governs how packets are buffered while waiting to be transmitted

Simulation results obtained that proposed Weighted Fair Queuing (WFQ)

technique is better than conventional techniques such as First in First Out (FIFO),

Priority Queuing (PQ) in terms of Packet dropped, Packet end to end delay for

various network services like Video Conferencing for bursty traffic. This paper

emphasizes the average queuing delay for Poisson and Self-similar traffic models

with two algorithmic approaches: Greedy algorithm and Evolutionary algorithm.

The greedy algorithm performs a least cost search on the total delay along paths

for routing traffic in a multi-hop fashion, the evolutionary algorithm uses the

genetic methods to optimize the average delay in a network. The results showed

that virtual topology design for self-similar traffic is quite different from the virtual

topology design for poisson traffic model using the standard mentioned

algorithms.

Keywords—IP Quality of Service; Latency; Greedy algorithm;

Wavelength Division Multiplexing.

1. INTRODUCTION

In a growing trend traffic is increased rapidly so in order to

make our network efficient in such conditions, traffic is processed as quickly as possible but there is no guarantee of

timelines or actual delivery. With the rapid transformation of the Internet into a commercial infrastructure, demands for

service quality have rapidly developed. Many challenges came on to the picture how to provide Quality of Service (QoS) for

applications such as Internet telephony and video-conferencing which requires a higher QoS than electronic mail

and general web browsing. In an IP network, many methods has been proposed to implement QoS such as fair queuing,

weighted fair queuing, frame-based fair queuing etc. However, all of these methods are based on employing buffers at the

network nodes. To implement the existing QoS mechanisms to differentiate services, all intermediate nodes should have a certain amount of buffer space. However, the use of electronic

buffer necessitates optical to electrical (O/E) and electrical to optical (E/O) conversions which sacrifice the data

transparency. On the other hand, no optical buffer (RAM) is available and the use of fiber-delay lines (FDLs) which can provide a limited delay should also be avoided as much as possible in the optical layer.

The measure problem in a network are related to the allocation of network resources as buffers and link bandwidth to different users. A limited amount of resources has to be

shared among many different competing traffic flows in an efficient way in order to maximize the performance and the

use of the network resources. The behavior of routers in terms of packet handling can be controlled to achieve different kind

of services [5]. This proposed paper indicates the performance of a number of packet handling mechanisms and produces a

comparative picture of them using the simulation software OPNET Modeler (version 14.5) [14]. The Opnet modeler is

one of the most advanced tools from among Opnet products palette, together with additional modules such as Wireless for

defence, 3D network visualize (3DNV), Application Characterization Environment (ACE) and system in the loop

(SITL) modules which allows advanced simulation methods for wired and wireless communication networks.

In this paper, the problem of designing virtual topologies for multi-hop optical WDM networks when the traffic is self-similar in nature is considered. Studies over the last few years

suggest that the network traffic is bursty and can be much better modeled using self similar process instead of Poisson

process. Here examine the buffer size of a network and observe that even with reasonably low buffer overflow

probability, the maximum buffer size requirement for self-similar traffic can be very large. Therefore, a self-similar

traffic model has an impact on the queuing delay which is usually much higher than that obtained with the Poisson

model.

1. State of the art in QoS issues for packet handling

Techniques.

Various queuing disciplines can be used to control which

packets get transmitted and which packets get dropped. The proposed scheme comprises of (i) First-in-first-out (FIFO)

queuing. (ii) Priority queuing (PQ) (iii) Weighted-Fair queuing (WFQ).

In the first case, this technique describes the principle of a queue or first-come first serve behavior: what comes in first is handled first, what comes in next waits until the first is

finished etc. Thus it is analogous to the behavior of persons “standing in a line” or “Queue” where the persons leave the


35

queue in the order they arrive. First In First Out (FIFO) is the most basic queuing discipline. In FIFO queuing all packets are

treated equally by placing them into a single queue, then servicing them in the same order they were placed in the

queue. FIFO queuing is also referred to as First Come First Serve (FCFS) queuing [7]. Priority Queuing assigns multiple

queues to a network interface with each queue being given a priority level. A queue with higher priority is processed earlier

than a queue with lower priority. Priority Queuing has four preconfigured queues, high medium, normal and low priority

queue. By default each of these queues has 20, 40, 60 and 80 packets capacity [4-5]. If packets arrive in the high queue then

priority queuing drops everything its doing in order to transmit those packets, and the packets in other queue is again empty.

When a packet is sent out an interface, the priority queues on that interface are scanned for packets in descending order for priority. The high priority queue is scanned first, then the

medium priority queue and then so on. The packet at the head of the highest queue is chosen for transmission. This

procedure is repeated every time when a packet is to be sent. The maximum length of a queue is defined by the length limit. When a queue is longer the limit packets are dropped [5].

In QoS, a flow-based queuing algorithm that schedules

low-volume traffic first, while allowing high-volume traffic share the remaining bandwidth. This is handled by assigning a

weight to each flow, where lower weights are the first to be serviced [5]. WFQ is a generalization of fair queuing (FQ). Both in WFQ and FQ, each data flow has a separate FIFO queue. In FQ, with a link data rate of R, at any given time the

N active data flows (the ones with non-empty queues) are serviced simultaneously, each at an average data rate of R / N.

Since each data flow has its own queue, an ill-behaved flow (who has sent larger packets or more packets per second than the others since it became active) will only punish itself and not other sessions [1-4]. In the mentioned techniques, the

study has been carried out on some issues like traffic dropped,

packet end to end delay and simulation results indicates that WFQ technique has a better quality than conventional

techniques in all the mentioned issues.

2. Proposed efficient QoS scheme

The proposed scheme comprises of (i) Greedy algorithm

(GA) (ii) Evolutionary algorithm (EA). In the proposed scheme, two different algorithm approaches is used for

solving the problem by considering both the queuing delay and the propagation delay of a network while designing a virtual topology. Researchers of optical networks have

attempted such problems of designing virtual topologies and have obtained solutions using heuristics/methods in

polynomial time [1-4]. Mukherjee et al. in [10] formulated the virtual topology design problem as a nonlinear optimization

problem where the objective was minimization of average network delay. In [1], Banerjee and Mukherjee formulated the

virtual topology design problem as a linear program where the queuing delays were intentionally ignored in the formulation.

This is of the opinion that the queuing delays are negligible with respect to the propagation delays when the load per

channel is reasonably low and cited the results obtained in [10] as reason to neglect the queuing delay.

Greedy algorithm greedily finds the connection with least delay from an initial topology constructed using first-fit

wavelength assignment policy. In first fit wavelength assignment strategy all the wavelengths are numbered and the

wavelength with lowest number or subscript has been given highest priority for wavelength assignment. In this strategy, a free wavelength with lowest subscript is assigned on all the

links along a route to establish the connection request. If the lowest subscript wavelength is not free then the connection

request is tried on second available free wavelength if it is also not free then the third wavelength is tried and so on. Thus for every connection request a free wavelength is searched according to lowest index from the available set of

wavelengths. This strategy does not check the path length of a connection request.

Evolutionary algorithms are emerging as a good alternative for solving hard optimization problems, we next propose an Evolutionary algorithm that tries to optimize the average delay of a network. The algorithm uses a hybrid routing and

wavelength assignment policy for the initial topology and then acts upon it in the evolutionary way to construct the final

topology. In the proposed wavelength assignment strategy a wavelength is assigned to the connection request according to the path length. The connection requests with short path length are given more wavelengths as compared to the connection

requests with long path length. This will lead to improved

system performance in terms of blocking probability. In this strategy, two sets of wavelengths are defined viz. set 1 and set

2. In set 1 all the wavelengths are available for connectionrequests and in set 2 few higher indexed wavelengths are

available only. For example if total available wavelengths areassumed 5 in the network then

Set 1= (λn), where n= 1 : 5

Set 2= (λm), where m= 4 : 5


36

The connection requests with short path length will be served by set1 and the connection requests with long path

length will be served by set 2. Here the connection requests are categorized into two categories viz. short path length and

long path length category according to path length. The connection requests with path length less than or equal to X

(where X is the opted path length for a network topology) are treated as short path length category and the connection

requests with path length greater than X are treated as long path length category. To determine the value of X, offline

calculation of all possible s–d pairs and their path length has been done.

Average queuing delays in the network with the Greedy, Evolutionary, and the Heuristic [10] algorithms for Poisson

and Self-similar traffic models are shown in Figs. 3 and 4 respectively. It has been observed that while the average queuing delay of the network considering Poisson model is

less than 2 milliseconds in most cases (Fig.3), it can be almost five times higher when the traffic is self-similar in nature

(Fig.4 ). The heuristic algorithm of [10] shows much higher queuing delays using the self-similar traffic model compared to the Poisson model as it constructs the virtual topology independent of the network delay. From the figures it can be

seen that the results improve in the cases of Greedy and Evolutionary algorithms, because these algorithms optimize

the topology considering both propagation and queuing delays [5]. The virtual topology constructed with the Greedy algorithm shows high average queuing delays for both Poisson and self-similar models when the number of wavelengths is 4,

but it improves as the number of wavelengths increases [11]. This is because, the virtual topology initially constructed with

the first fit wavelength assignment policy which does not necessarily optimize delay in the network. As the number of wavelengths increases, more single hop connections are established thereby reducing the delay. It is also observed that

when estimated with the standard M/M/1 queuing model all

the three algorithms give low (almost negligible) and similar queuing delays (Fig.3), whereas, it is not the case with self-

similar traffic model (Fig.4 ). This shows that although algorithm is independent of queuing delays (or neglecting

queuing delays) can be used effectively for designing a good virtual topology with Poisson traffic model, for bursty or self-

similar traffic it is necessary that the algorithms consider queuing delays of the network for the best network performance. For an efficient network performance the average queuing delay is least in any optical network.

3. Simulation results

Fig. 1 show the results of traffic drop versus

transmission rate respectively. The performance measure is estimated by varying an transmission rate from interval to interval. As shown in Fig. 1, it is seen that the packet drop starts at around 95 sec. The simulation results obtained that

packet drop for FIFO is higher, for PQ it is semi lower and for WFQ it is lower.

Fig.1. Traffic drop vs. transmission rate

Fig. 2 shows packet end to end delay versus transmission rate for video conferencing services [12-13], where it can be

observed that as the traffic increases the packet end to end delay time is smaller for WFQ group then PQ and FIFO groups. For an efficient network performance, packet end to

end delay is least. This parameter was tested for Video Conferencing as it requires higher QoS in comparison of other

type of traffic. Like Video conferencing, in VOIP services also FIFO and PQ groups packet end to end delay time is always

higher than WFQ.

Fig.2. End to End delay vs. transmission rate

Fig.3 and Fig.4 show the results of comparison of the average queuing delay for Poisson model [6] and self-similar traffic model [8] with the greedy and evolutionary algorithm as well as the conventional heuristic algorithm. Fig. 3 shows

that average queuing delay is too small for Poisson model with different algorithm approaches. So for such type of models

if algorithm is made independent of queuing delays (or neglecting queuing delays) it can be used effectively for designing a good virtual topology [9].


37

Fig.3. Average queuing delay vs. number of wavelengths for

(Poisson model)

Where Gss and Gp: Results with the Greedy algorithm

using the self-similar model and the Poisson model respectively. Ess and Ep: Results with the Evolutionary algorithm using the self-similar model and the Poisson model respectively. Hss and Hp: Results with the Heuristic algorithm

of [10] using the self-similar model and the Poisson model respectively.

Fig.4 obtain the results that average queuing delay is comparatively more in self-similar model with different algorithm approaches. So for the proposed bursty or self-similar traffic it is necessary that the algorithms considering

the queuing delays while designing a good network virtual topology.

Fig.4. Average queuing delay vs. number of wavelengths

for (Self-similar model)

III.CONCLUSION

In this paper, an efficient Quality of Service (QoS) oriented traffic management schemes has been proposed to

reduce the average queuing delays by designing a different virtual topology for the networks. It is seen that the proposed

scheme guarantees that WFQ shows the better performance

among all the conventional queuing techniques in terms of Packet drop, File receiving, voice data receive and video

conferencing. However there are some problems with the integration occurs which can delay the voice data in reaching

the proper address at a continuous rate. So in order to solve the time delay problem the results of WPQ algorithm can be

applied. For voice communications over IP to become acceptable to the users, the delay needs to be less than a

threshold value and the IETF (Internet Engineering Task Force) are working on this aspect . Delay for applications like

video conferencing are very apparent, causing the video signal to jerk and sputter. Fair Queuing algorithm can solve the

problem. But according to the simulation it is already proved that a modernized format of fair queuing WFQ (Weighted Fair

Queue) can perform better. So, it can be said with confidence that user traffic stream like voice, video, data can be easily transferred with its efficient level performance by using

Weighted Fair Queue algorithm. The obtained results shown that virtual topologies designed considering self-similar traffic

is quite different from the virtual topologies designed using the standard Poisson model as former model design is dependent on the queuing delays which cannot neglect and therefore more effective in handling the present day bursty

Internet traffic.

REFERENCES

[1] B. Mukherjee, D. Banerjee, S. Ramamurthy, A.Mukherjee, Some principles for designing a wide-area WDM optical network, IEEE/ACM Transactions on Networking, vol. 4, no. 5, (Oct. 1996), pp. 684–695.

[2] D. Banerjee, B. Mukherjee, Wavelength routed optical networks: Linear formulation, resource budget tradeoffs and a reconfiguration study, IEEE/ACM Transactions on Networking, vol. 8, no. 9, (Oct. 2000), pp. 598–607

[3] J. A. Bannister, L.Fratta, M.Gerla, Topological design of the wavelength-division optical network, Proc. INFOCOM 90 (San Francisco, LA, USA, June 1990), pp. 1005–1013.

[4] Maheshwari, Harish, Mandhania, Sonali Sisodia “VoIP Technology: Overview and Enhancements” (MCA, I.I.P.S , D.A.V.V).

[5]OpnetModeler,OPNET14.5<.http://www.optnet.com/optnetmodeler[online[6] P. Fiorini, L. Lipsky, H.-P. Schwefel, Analytical models of performance in telecommunication systems based on On-Off traffic sources with self-similar behavior, Proc, of 7th Int. Conf. Telecommunication Systems and Modeling (Nashville, TN, USA, Mar, 1999).

[7] R.M. Krishna Swamy, K.N. Sivarajan, Design of logical topologies: A

linear formulation for wavelength routed optical networks with no wavelength changers, IEEE/ACM Transactions on Networking, vol. 9, no. 2, (Apr. 2001), pp. 186–198.

[8] R.Ramaswami, K. N. Sivarajan, Design of logical topologies for wavelength-routed optical networks, IEEE Journal of Selected Areas of Communications, vol. 14, no. 5, (June 1996), pp. 840–851

[9] S. Banerjee, B. Mukherjee, D. Sarkar, Heuristic algorithms for constructing optimized structures of linear multihop lightwave networks, IEEE Transactions on Networking, vol. 2, no. 2–4, (Feb.–Apr. 1994), pp. 1811–1826.

[10] Setrag Khoshafian, A. Brad Baker; “Contributor A. Brad Baker”, vol. 2, no. 4, (July 2006), pp. 122-132

[11] T.G.Robertazzi, Computer Networks and Systems: Queuing Theory and Performance Evaluation (Springer-Verlag), 2000.

[12] V.Paxson, S. Floyd, Wide-area traffic: The failure of Poisson modeling, IEEE/ACM Transactions on Networking, vol. 3, no. 3, (June 1995), pp. 226–244.

[13] H.-P. Schwefel, L. Lipsky, Impacst of self-similar On/Off traffic on


38

delay in stationary queuing models, Performance Evaluation, vol. 43, no. 4, (Mar. 2001), pp. 203–221.

[14]. Z.Zhang, A. Acampora, A heuristic wavelength assignment algorithm

for multihop WDM networks with wavelength routing and wavelength re-use, IEEE/ACM Transactions on Networking, vol. 3, no. 3, (June 1995), pp. 281–288


39

Simulative Investigation of 2.5Gbps RZ modulation

format using various optical sources in SOA based

RoF system.

Namita Kathpal

Department of Electronics & Communication Engineering

Deenbandhu Chhotu Ram University of Science &

Technology

Murthal (Sonepat), India

[email protected]

Amit Kumar Garg

Department of Electronics & Communication Engineering

Deenbandhu Chhotu Ram University of Science &

Technology

Murthal (Sonepat), India

[email protected]

Abstract— In this paper, the impact of various optical sources

on the performance of Semiconductor Optical Amplifier based

Radio over Fiber (RoF) system has been analyzed. This RoF

system has been modeled and analyzed using OptiSystem (14.0)

software. The transmission performance of RoF system in terms

of Q-factor, BER and Eye Height at different fiber length using

various optical sources such as VCSEL, Controlled Pump Laser,

Directly modulated Laser and Empirical Laser has been

measured and compared. An improvement in SNR has been

observed by employing VCSEL with Mach-Zehnder modulator

as compared to traditional optical sources in RoF system.

Keywords—VCSEL; Controlled Pump Laser; Directly

modulated Laser; Empirical Laser

I. INTRODUCTION

The constantly increasing bandwidth requirement for fast speed wireless access entails the merging of wireless technology and fiber access technology [1]. Radio over Fiber is the most feasible technology in providing broadband wireless access services in the emerging optical wireless networks [2]. The performance of RoF system rely on several factors such as modulation format, optical modulation, electrical modulation, optical fiber, optical source, bit rate and an optical detector. The principle purpose of optical source is to provide electrical to optical conversion. The responsibility of Laser diode is to modulate the RF signal with light signal. The ample demand for RF transmission through fiber include efficient bandwidth, efficient RF to optical conversion and minimum distortion [3]. There are several Laser Diode for RF to IF conversion i.e. Vertical-Cavity surface emitting Laser, Directly modulated Laser, Controlled Pump Laser and Empirical Laser. The use of Laser as an optical source in RoF system enables the transmission up to multi-gigahertzes. In this paper, the performance of various optical sources at different fiber length has been analyzed in terms of performance metrics such as BER, Q-factor and Eye Height.

II. SIMULATION SETUP

The simulation setup of RZ modulation format based RoF system is shown in Fig. 1. The simulation parameter used in the simulation setup is listed in Table 1. In the downlink transmission, the central station is designed of two signal gen-

TABLE I. SIMULATION PARAMETERS

Names of Parameter Values Units

RZ Bitrate 2.5 Gbps

Light Signal 193.1 THz

Pump Signal 193.13 THz

MZM Extinction Ratio 30 dB

SOA Length 500 µm

SOA Width 3 µm

SOA Height 0.08 µm

EDFA Length 5 m

Fiber Attenuation 0.2 dB/km

Fiber Dispersion 16.75 ps/nm-km

Fiber Length 10 to 50 km

Fig. 1 Simulation setup


40

-erators i.e. light signal and Pump signal. At transmitter, twolaser diodes emit light at frequency 193.1 THz and 193.13THz which are provided to Mach-Zehnder Modulator. Thesemodulated signals are multiplexed by WDM multiplexer.These multiplexed signals are amplified by SOA followed byEDFA and transmitted over single mode fiber to base station.The downlink data signal is passed through PIN photodetectorto extract the transmitted signal. The resulting electrical signalis filtered and applied to BER analyzer for analyzing thedownlink signal.

III. RESULTS AND DISCUSSION

The eye diagram of various optical sources at the output of

low pass Bessel filter is examined by BER analyzer as shown

in Fig. 2 (a)–(d). The performance of RZ modulation based

RoF system has been analyzed by Q-factor, BER and Eye

Height. Q-factor describes the quality of signal transmission.

Bit error rate measures the probability of bit errors to the total

number of transmitted bits.

(a) (b)

(c) (d)

Fig. 2 Eye Diagram of RZ modulation format based RoF

system at 20 km fiber length utilizing

(a) Vertical Cavity Surface Emitting Laser

(b) Controlled Pump Laser

(c) Directly Modulated Laser

(d) Empirical Laser

Comparative analysis between various optical sources at

different fiber length is listed in Table. II-V.

TABLE II. INVESTIGATION OF VCSEL PERFORMANCE AT DIFFERENT

FIBER LENGTH

Fiber

Length

(km)

Vertical Cavity Surface Emitting Laser

Q-factor BER Eye Height

10 43.21 0 0.51

20 30.75 3.56e-208 0.29

30 5.13 1.25e-8 0.05

40 11.23 6.43e-30 0.05

50 5.97 2.53e-10 0.01

TABLE III. INVESTIGATION OF CONTROLLED PUMP LASER

PERFORMANCE AT DIFFERENT FIBER LENGTH

Fiber

Length

(km)

Controlled Pump Laser


10 9.46 1.43e-22 0.41

20 10.49 4.57e-27 0.22

30 6.65 1.92e-12 0.08

40 4.84 2.92e-7 0.02

50 3.69 5.33e-5 0.008

TABLE IV. INVESTIGATION OF DIRECTLY MODULATED LASER

PERFORMANCE AT DIFFERENT FIBER LENGTH

Fiber

Length

(km)

Directly Modulated Laser


10 9.73 1.07e-23 0.40

20 8.17 1.40e-17 0.02

30 4.75 1.24e-7 0.05

40 3.89 1.45e-5 0.01

50 3.07 2.60e-4 0.0007

TABLE V. INVESTIGATION OF EMPIRICAL LASER PERFORMANCE AT

DIFFERENT FIBER LENGTH

Fiber

Length

(km)

Empirical Laser


10 56.56 0 0.44

20 10.51 3.28e-26 0.26

30 4.93 4.69e-8 0.05

40 7.78 6.81e-16 0.05

50 3.29 9.09e-5 0.002


41

Fig. 3 Q-factor versus Fiber Length

Fig. 4. Eye Height versus Fiber Length The Q-factor with the VCSEL at 20 km fiber length is calculated as 30.75 and reduced to 10.51 using Empirical Laser, which again reduces to 10.49 using Controlled Pump Laser, which is further, reduces to 8.17 in the case of directly modulated Laser. From Fig. 3 and Fig. 4, it is observed that the Eye Height, Eye Opening and Q-factor decreases with increase in propagation distance. It is revealed from Fig. 3 and 4 that VCSEL with RZ modulation format offers superior performance as compared to other optical sources.

IV. CONCLUSION

This paper presents a RZ modulation format based RoF

system and evaluate the performance for 2.5Gbps

communication link. The presented link has been simulated

under various fiber lengths to analyze the performance of

different Lasers to determine the optimum system design for

long haul communication.

References

[1] D. Wake, A. Nkansah, N. J. Gomes, S. Member, G. De Valicourt, R. Brenot, M. Violas, Z. Liu, F. Ferreira, and S. Pato, “A Comparison of Radio Over Fiber Link Types for the Support of Wideband Radio Channels,” Journal of Lightwave Technology, vol. 28, no. 16, pp. 2416–2422, 2010.

[2] V. Sharma, A. Singh and A. K. Sharma, “Simulative investigation of nonlinear distortion in single- and two-tone RoF systems using direct- and external-modulation techniques,” Optik - International Journal for Light and Electron Optics, vol. 121, no. 17, pp. 1545–1549, 2010.

[3] C. Lin, J. Chen, P. Peng, C. Peng, W. Peng, B. Chiou and S. Chi, “Hybrid optical access network integrating fiber-to-the-home and radio-over-fiber systems,” IEEE Photonics Technology Letters, vol. 19, no. 8, pp. 610-612, April 15, 2007.

[4] T. Katsuzama, “Development of Semiconductor Laser for Optical Communication,”Sei Technical Review, Number 69, October 2009.

[5] Q. Wang, F. Zeng, S. Blais, and J. Yao, "Optical ultrawideband monocycle pulse generation based on cross-gain modulation in a semiconductor optical amplifier," Optics Letters, vol. 31, no. 21, pp. 3083-3085, 2006.

[6] K. Y. Cho, Y. J. Lee, H. Y. Choi, A. Murakami, A. Agata, Y.Takushima, and Y. C. Chung, “Effects of reflection in RSOA-based

WDM-PON utilizing remodulation technique,” J. Lightw. Technol.,

vol. 27, no. 10, pp. 1286–1295, May 2009. [7] A.Loayssa, J.M. Salvide, D. Benito and M.J. Garde, “Novel Optical

Single - Sideband Suppressed Carrier Modulator using Bidirectionally Driven Electro-optic Modulator", IEEE Technical Digest: Microwave Photonics 2000 conference, Alwyn Seeds, pp. 117 - 120, Oxford, 2000.

[8] A.Carena,V.Curri and P.Poggiolini, “On the Optimization of Hybrid Raman/Erbium-Doped Fiber Amplifiers‟, IEEE Photonics Technology Letters, vol. 13, no. 11, pp. 1170-1172, Nov. 2001.

[9] R.S. Kaler, “Simulation of 16 ×10 Gb/s WDM system based on optical amplifiers at different transmission distance and dispersion” Optik, pp.1654– 1658, 2012.

[10] Chien-Hung Yeh, Kuo Hsiang Lai, Ying Jie Huang Chien-Chung Lee and Sien Chi. “Hybrid L-Band Optical Fiber Amplifier Module with Erbium-Doped Fiber Amplifiers and Semiconductor Optical Amplifier”, Japanese Journal of applied Physics, vol. 43, pp. 5357–5358, 2004.

[11] N. Kathpal and A. K. Garg, “Performance Analysis of Multitone RoF system using DPSK based Optical Modulators,” International Journal of Electronics, Electrical and Computational System, vol. 6, no. 7, pp. 532–535, 2017.

[12] N. Kathpal and A. K. Garg, “Performance analysis of Radio over Fiber system using Direct and External Modulation Schemes,” International Journal of Engineering Technology, Management and Applied Sciences, vol. 8, no. 4, pp. 172–175, 2017.

[13] N. Kathpal and A. K. Garg, “Mitigation of Dispersion Effects for better Quality of Transmission in RoF system – A Review,” International Journal of Engineering Technology, Management and Applied Sciences, vol. 5, no. 3, pp. 9–13, 2017.


42

Abstract—to route data efficiently from source to

destination is one of the stimulating jobs in wireless sensor

network. And also in Wireless Sensor Networks (WSNs) due to

increasing demand for real-time applications has made the

Quality of Service (QoS) based communication protocols

becomes interesting and most researched topic. Bandwidth and

delay constraints are Quality of Service (QoS) requirements

which are required. and For the different QoS based

applications of WSNs raise substantial challenges. Energy

consumption is also a prominent and critical issue faced by

wireless sensor networks. When the sensors communicate with

each other the maximum amount of energy is consumed. In

order to develop the lifetime of the network, energy should be

used in an efficient manner. Therefore we need energy efficient

routing mechanisms .The well-known low-energy adaptive

clustering hierarchy (LEACH), that is Cluster-based routing

techniques are used to achieve scalable solutions and extend the

network lifetime until the last node dies (LND).

Index Terms— WSN, Localization of WSN Nodes, Design

challenges of WSN, Schemes of Node Deployment

I. INTRODUCTION

In recent years Wireless Sensor Networks (WSNs) have achieved worldwide attention, particularly with the proliferation of Micro-Electro-Mechanical Systems (MEMS) technology, which has facilitated the development of smart sensors [1]. In current years, wireless communications technology is growing rapidly, and the miniaturization and low cost of sensing devices, have give boost to the development of wireless sensor networks (WSNs)[2].The limited and generally irreplaceable power sources of the sensor nodes is One of the major constraints of WSNs. Still, it is impractical to replace, in many applications, the sensor nodes as they work under harsh environment. Therefore, for long run operation of WSNs reducing energy consumption of the sensor nodes is considered as the most critical challenge. For designing energy saving protocols which should have features of low-power radio communication hardware, energy-aware MAC protocols, etc.[3] Extensive researches have been carried out. Wireless Sensor Networks (WSN) refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. Like pollution levels, temperature, sound, humidity, wind speed and direction, pressure, etc environment conditions, WSNs govern. In general quite a substantial amount of data, WSN may generate If data fused could be used, the throughput could be used [4].

1.1 Multi-hop routing algorithms for wireless sensor

networks

The basic function of a routing algorithm is to select the path

from a set of available paths that is most efficient based on a

specific criterion. Intuitively, to maximize the WSN’s

network lifetime, the path that achieves minimum power

consumption while ensuring fair power consumption among

individual nodes should be used. multi-hop routing

algorithms of WSN a lot of effort has focused, and many

algorithms have been proposed for this. flat multi-hop routing

algorithms and hierarchical multi-hop routing algorithms are

the broad classification.

1.1.1 Flat multi-hop routing algorithms

In Fig. 1, an picture representation of how flat multi-hop

routing algorithms are used to send data is shown. In the

illustration, to communicate over a bounded area within its

maximum transmission range to other sensor nodes each

sensor node has the ability, and an arrow’s thickness is

proportional to the quantity of facts being transmitted over

that corresponding link. In practice, utilization of link act

very differently between different routing algorithms.

Fig. 1: Flat multi-hop routing sending data.

1.1.2 Hierarchical multi-hop routing

To choose minimum power consuming paths they used

power aware metrics and Flat multi-hop routing algorithms

are excellent in choosing this. However data collected from

the WSN which is of correlated nature it cannot take the

complete advantage. The application scope of the WSN (e.g.,

temperature readings collected from geographically nearby

locations have a high likelihood of changing into similar),and

The comparatively high node density of the WSN create

information aggregation a really attractive procedure in

WSN. ranked multi-hop routing algorithms with success

utilize the info aggregation to decrease the degree of

information flowing within the network. In class-conscious

multi-hop routing algorithms, detector nodes assume

completely different roles, which may be modified with time.

As given in Fig. 2. LEACH may be a two-layered

class-conscious multi-hop routing algorithmic rule. Every

node will play the role of a Cluster Head (CH) or Cluster

Member (CM) [5] [8]

Fig. 2: Two layered Hierarchical multi-hop routing [5].

1.2 Multipath routing

Energy Efficient Routing Protocols for

Wireless sensor network

Dr. Mukesh Singla Prof. & Director M.S.I.E.T, Kalanaur

email id: [email protected]


43

Single path routing protocols in sensor network are designed

to discover a single path between a source/destination pair.

From the other hand, multipath routing consists of finding

multiple paths between the source and destination nodes.

These multiple paths can be used to solve some trade-off in these

networks and fulfilled with the vibrant nature of WSNs .

1.2.1 Benefits of multipath routing

As mentioned in the introduction, multipath routing protocols

can provide load balancing, fault tolerance, bandwidth

aggregation, and reduced delay. Below, we discuss how to

provide each of these benefits in multipath routing.

1.2.1.1 Load balancing

As explicit in, one amongst the explanations that classical

multipath routing has been explored is to supply load

equalisation. Load balancing can be achieved by splitting the

traffic across multiple route. This quality of multipath routing

is implicable to WSNs. in the sensor network energy

utilization across nodes, potentially resulting in longer

lifespans Load balancing can spread. Furthermore, load

balancing also helps in avoiding congestion and bottleneck

problems .

1.2.1.2 Reliability and fault tolerance

In WSNs Reliability is a big matter, because data

transmission is subject to lost due to several reasons: several

types of interference, media access conflicts, network

topology changes, etc. These reasons affect the wireless

radios to correctly decode the wireless signals. Developing

multipath routing one among the explanations behind is to

supply route failure protection, and increase resiliency to

route failures. Discovering and maintaining multiple paths

between the source and destination pair improves the routing

performance by providing alternative routes. When the

primary path fails, an alternative path will be used to transfer

the data during this case the multiple methods don't seem to

be used at the same time. for knowledge routing Multiple

methods is used at the same time. coinciding multipath

routing can be accustomed improve responsibility.

1.2.1.3 Highly aggregated bandwidth

For a connection Routing over a single path may not provide

enough bandwidth; Bandwidth may be limited in a WSN.

However, if data are routed over multiple paths

simultaneously the overall bandwidth of the paths may

satisfy the bandwidth requirement of an application.

1.2.1.4 Minimizing end to end delay

By assuming that the paths between the source and

destination pair are node disjoint paths where correlation

between the paths is very low, and there is no route coupling

between different routes (for example, through the using of

directional antennas this could be achieved), the end to end

delay can be minimized by dividing the data (to be sent) into

a number of segments and using multiple paths to route

segments simultaneously to the destination.

1.3 Problems with multipath routing

A shared wireless channel to communicate Nodes in the

wireless sensor network use. neighboring nodes must content

for the channel This means. When the channel is busy by a

transmission node, neighboring nodes hear the transmission

and square measure blocked from receiving information from

alternative nodes. to boot, reckoning on the below egg laying

mack protocol, neighboring nodes could ought to defer their

transmission until the channel is free. Even once varied

channels square measure used, attributable to the interference

the standard of neighboring transmission is also degraded.

Now, contemplate the utilization of a multipath routing,

wherever the multiple ways square measure used at the same

time. Even, the multiple routes square measure node-disjoint

paths; transmissions over the routes could interfere if some

nodes square measure within the transmission vary of every

alternative. This downside is termed route coupling. once 2

routes square measure situated physically shut enough to

interfere with one another throughout digital communication

Route coupling happens. for access to the wireless channel

they share and may find yourself activity worse than one path

protocol Nodes in those 2 routes square measure perpetually

competitory As a result. Thus, for improved performance

node-disjoint routes aren't a decent condition

II. LITERATURE REVIEW

Enan A. Khalil et al. in 2011[1] the main challenges in

designing and planning the operations of Wireless Sensor

Networks (WSNs) are to optimize energy consumption and

prolong network lifetime. Like as the well-known

low-energy adaptive clustering hierarchy (LEACH)

Cluster-based routing techniques, are used to attain scalable

solutions and extend the network lifespan until the last node

dies (LND). Also to address energy-aware routing challenges

as meta-heuristics by designing intelligent models that

collaborate together to optimize an appropriate energy aware

objective function in recent years evolutionary algorithms

(EAs) have been successfully used. On the other hand, some

protocols, are concerned with another objective: extending

the stability time until the first node dies (FND), such as

stable election protocol (SEP). Often, between extending the

time until FND and the time until LND there is a tradeoff. To

our data, no try has been created to get a more robust

compromise between the soundness time and network period

of time. the foremost vital characteristic of the Semitic deity

(i.e., the objective function) of The design,This paper

reformulates, so as to obtain a routing protocol that can

provide more robust in terms of network stability period

results than the existing heuristic and meta-heuristic

protocols, lifetime, and energy consumption. with efficient

energy utilization routing protocol Better tradeoff between

the lifespan and the stability period of the network, which can

guarantee An evolutionary-based is projected. WSN models

are evaluated and compared against the LEACH, SEP, and

one of the existing evolutionary-based routing protocols,

hierarchical clustering-algorithm-based genetic algorithm

(HCR) To support this claim, extensive simulations on 90

homogeneous and heterogeneous.

Ahmed E.A.A. Abdulla et al. in 2012[2] Power-aware routing

in wireless sensor networks (WSNs) focuses on the crucial

problem of extending the network lifetime of WSNs, which

are limited by low-capacity batteries. However, most of the

contemporary works fail to resolve the hotspot problem,

which is the isolation of the sink node due to the power

exhaustion of sink close-by nodes. To address this issue

through a hybrid approach that combines two routing

strategies Author propose a solution, flat multi-hop routing


44

and hierarchical multihop routing In this paper. The former

aims to reduce the whole power consumption within the

network, and also the latter tries to decrease the number of

traffic by utilizing knowledge compression. Author

mathematically evaluate the power consumption of our

proposed algorithm, then author demonstrate through

extensive simulations that the proposed scheme is able to

extend the network lifetime by alleviating the hotspot

problem.

Md Azharuddin et al. in 2014[3] Conservation of energy and

fault tolerance are two major issues in the deployment of a

wireless sensor network (WSN). For an oversized scale WSN

style of agglomeration and routing algorithms ought to

incorporate each these problems for the long-term operation

of the network. Author recommends in this paper distributed

clustering and routing algorithms jointly referred as DFCR.

to be energy efficient and fault tolerant The algorithm is

shown. Due to hasty failure of the cluster heads (CHs) The

DFCR uses a distributed run time recovery of the sensor

nodes. It takes care of the device nodes that don't have any

CH among their communication vary. on the proposed

algorithm Author performs extensive experiments using

various network scenarios. With the existing algorithms the

experimental outcome are compared to demonstrate the

strength of the algorithm in terms of various performance

metrics.

A.SARANYA et al. in 2015[4] In Wireless Sensor Networks,

Sensors are generally battery powered devices. This network

is used to gather various kinds of information to Base station

(BS). Of procedure management, storage capability, energy

provides are the vital problems in their energy constraint they

contain. to maximize the network time period, author needn't

solely to attenuate total energy consumption and conjointly

balance WSN load. A new Fuzzy based General Self

organized Tree based Energy Balance routing protocol

proposed in this paper, which builds a routing tree using a

process where, for each round BS assigns a root node and

broadcast to all sensor nodes. Equally by considering solely

itself and its neighbor’s info every node selects its parent,

therefore creating a dynamic protocol. projected protocol

performance is healthier than alternative protocols

Simulation outcome show that

Jalel Ben-Othman et al. in 2010[5] in Wireless Sensor

Networks (WSNs) The increasing demand for real-time

applications has made the Quality of Service (QoS) based

communication protocols an interesting and hot research

topic. for the dissimilar QoS primarily based applications of

WSNs raises important challenges Satisfying Quality of

Service (QoS) needs (e.g. information measure and delay

constraints).More exactly, the networking protocols need to

survive up with energy constraints, while providing precise

QoS guarantee. Hence, enabling QoS applications in sensing

element networks in numerous layers of the protocol stack

needs power and QoS awareness. In several of those

applications (such as transmission applications, or time

period and mission crucial applications is mixed of delay

sensitive and delay tolerant traffic ), the network traffic.

Hence, QoS routing becomes a very important issue. during

this paper, author propose AN Energy economical and QoS

aware multipath routing protocol (abbreviated shortly as

EQSR) that maximizes the network period through

reconciliation energy consumption across multiple nodes, to

allow delay sensitive traffic to reach the sink node within an

acceptable delay uses the concept of service differentiation,

reduces the end to end delay through spreading out the traffic

across numerous paths, and increases the throughput through

introducing data redundancy. To work out the most effective

next hop through the routes construction part EQSR uses the

residual energy, node on the market buffer size, and ratio

(SNR). EQSR protocol employs a queuing model to handle

both real-time and non-real-time traffic Relayed on the

notion of service differentiation. With the MCMP

(Multi-Constraint Multi-Path) routing protocol author

calculate and associate the performance of our routing

protocol By means of simulations. Achieves our protocol

lower average delay Simulation outcome have shown that, a

lot of energy savings, and better packet delivery quantitative

relation than the MCMP protocol.

Basma M. Mohammad El-Basioni et al. in 2011[6] Because

sensor nodes typically are battery-powered and in most cases

it may not be possible to change or recharge batteries, the key

challenge in Wireless Sensor Networks (WSNs) design is the

energy-efficiency and how to deal with the trade-off between

it and the QoS parameters required by some applications. in

terms of lifetime, delay, loss percentage, and throughput, and

proposes The QoS of an energy-efficient cluster-based

routing protocol called Energy-Aware routing Protocol

(EAP) some modifications on it to enhance its performance

studies in this paper. Better characteristics in terms of packets

loss, delay, and throughput, but slightly affects lifetime

negatively The modified protocol offers. in terms of packet

loss percentage by on average nearly almost 93.4%

Simulation results showed that The updated protocol

considerably outperforms EAP.

Harish Kumar et al. in 2013[7] Energy consumption is

prominent and critical issue faced by wireless sensor

networks. The maximum quantity of energy is consumed

once the sensors communicate with one another. That’s why

energy efficient routing mechanisms are required. In this

paper, a routing scheme based on the fisheye state routing

with a difference in route selection mechanism has been

proposed to ensure the reduction in the overall energy

consumption of the network. This format is termed as

Energy-Aware Fisheye State Routing (EA-FSR). It is

simulated considering varied parameters victimisation

QualNet5.0. Performance of EA-FSR has been compared

with the first optical lens state routing algorithmic rule that is

additionally simulated within the same setting. varied

parameters For comparison like end-to-end delay average,

energy consumption and outturn are thought-about.

J. Gnanambigai et al. in 2014[8] the fastest growing

technology that would dominate the future world of wireless

communication is Wireless Sensor Networks (WSNs). The

essential issue in WSNs is energy. Energy ought to be

employed in Associate in Nursing economical manner, so as

to enhance the period of time of the network. to enhance

energy potency of wireless sensing element networks many

routing protocols has been developed. The routing protocol


45

could also be standard sort or hybrid sort. the benefits of 2

totally different protocols the hybrid sort integrates. In this

paper, a new hybrid routing protocol called Quadrant Based

Low Energy Adaptive clustering Hierarchy (QB-LEACH ) is

developed where lifetime improvement is vital. This protocol

integrates the Quadrant based mostly Directional

Routing(Q-DIR), AN Ad-hoc routing algorithmic rule and

Low Energy adaptational clump Hierarchy (LEACH), a

routing algorithmic rule for WSNs. The performance nature

of the protocol is evaluated and discovered that this protocol

shell the opposite in terms of energy conservation and

network amount.

Table 1 Routing Protocols in WSN’s

III. Conclusion

A hybrid multi-hop routing algorithm by combining flat and

hierarchical multi-hop routing algorithms can resolve the

problem of the isolation of the sink caused by the battery

exhaustion of nodes around it. The hybrid multihop routing

algorithm is a promising solution for the hotspot problem and

extending the network lifetime. A distributed cluster and

routing formula referred to as DFCR for wireless device

networks that are energy economical similarly as fault

tolerant. A General Self organized which is novel Fuzzy

based, Tree based Energy Balance routing protocol proposed,

which builds a routing tree using a process where, for each

round BS assigns a root node and broadcast to all sensor

nodes. Equally each node selects its parent by considering

only itself and its neighbor’s information, thus making a

dynamic protocol. And the results show that proposed

protocol performance is better than other protocols. an

Energy economical ANd QoS aware multipath routing

protocol (abbreviated shortly as EQSR) that maximizes the

network period of time through equalisation energy

consumption across multiple nodes. within an acceptable

delay to reach the sink node Uses the theory of service

differentiation, reduces the end to end delay through

spreading out the traffic across multiple paths, and increases

the throughput through introducing data redundancy. EQSR

uses the residual energy, node available buffer size, and

Signal-to-Noise Ratio (SNR) to predict the best next hop

through the paths construction phase. EQSR protocol

employs a queuing model to handle both real-time and

non-real-time traffic, Based on the concept of service

differentiation.

References

[1]. Bara’a A ,Enan A. Khalil. ,Attea,”Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks”, Swarm and Evolutionary Computation, 2011 ,pp. 195–203

[2]. Ahmed E.A.A. Abdulla , Hiroki Nishiyama, Nei Kato,” Extending the lifetime of wireless sensor networks: A hybrid routing algorithm”, Computer Communications 35,2012, pp. 1056–1063

[3]. Md Azharuddin, Pratyay Kuila, Prasanta K. Jana, ”Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks”, Computers and Electrical Engineering, 2014, pp.1-7

[4]. A.SARANYA, R.SENTHIL KUMARAN, Dr. G.NAGARAJAN, ”Enhancing Network Lifetime using Tree Based Routing Protocol in Wireless Sensor Networks”, INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION SYSTEM , 2015, pp.1-10

[5]. Jalel Ben-Othman, Bashir Yahya, ”Energy efficient and QoS based routing protocol for wireless sensor networks”, J. Parallel Distrib. Comput. 70,2010, pp. 849_857.

[6]. Basma M. Mohammad El-Basioni , Sherine M. Abd El-kader , Hussein S. Eissa , Mohammed M. Zahra, ”An Optimized Energy-awareRouting Protocol for Wireless Sensor Network”, Egyptian InformaticsJournal ,2011- 12, pp.61–72.

[7]. Harish Kumar , Harneet Arora , R.K. Singla, ”Energy-Aware Fisheye Routing (EA-FSR) algorithm for wireless mobile sensor networks”, Egyptian Informatics Journal , 2013- 14, pp.235–238.

[8]. J. Gnanambigai, N. Rengarajan, N.Navaladi, ”A CLUSTERING BASED HYBRID ROUTING PROTOCOL FOR ENHANCING NETWORK LIFETIME OF WIRELESS SENSOR NETWORK”, 2nd International Conference on Devices, Circuits and Systems (ICDCS), 2014, pp.1-4


46

Hundred Percent Secure And Pure Steganography

without Password Protection

Alok Sharma Nidhi Sharma Dr.V.K Srivastva

Ph.D Scholar Assistant Professor Professor

Dept of CSE, Baba Mast Nath Dept of CSE, TIT&S, Bhiwani Dept of CSE, Baba Mast Nath

University,Rohtak Bhiwani, India University,Rohtak

Rohtak,India [email protected] Rohtak, India

[email protected]

Abstract—

In this digital world, various security schemes and

algorithm are intended to protect information from intruder,

attacker and unwanted parties. This is the example of pure

steganography which donot take help of cryptography and

password protection. Normally steganography without the help

of cryptography is not considered hundred percent secure.

Along with cryptography steganographic algorithms available

in market are protected with password security. In this paper

steganographic algorithm witch is hundered percent secure and

impossible to break by any tool available in the market is

explained which can not be detected by any technique or tools

available in the market with attacker , unwanted parties and

intruder. In this paper, a Algorithm to Secure data in online

transmission is proposed which is example of pure

steganography and provides hundered percent security to

online data.

1. INTRODUCTION

Transfer of information and sharing of information to distant locations has increased to large extent in todays digital world. So it has become compulsory to secure this information transferred over internet. There are lots of techniques available to secure information transferred over internet example public key cryptography, private key cryptography, hashing algorithms and steganographic techniques.[1] This algorithm will cover main advantage of steganographic techniques to keep the existence of secret message unrevealed along with the benefit of making it impossible for intruder , attacker and third party to retrieve even a single bit of secret message . This algorithm is NP-Hard algorithm on network for intruder, attacker and third party but polynomial at receiver end.[2]

2.DESCRIPTION OF ALGORITHM

In this algorithm both receiver and sender first mutually

agrees on one BIT Table with values 0 and 1 in table and

retains one (Same) copy of BIT Table with each and

maintains it secret as the case of physically key exchange

method in key based algorithm in cryptography where both

parties take their key physically (By transportation) not

online.[3] This is the case of steganography as this BIT

Table is made from random selection of LSB (least

significant bits )from binary values of color bit values of

image. We are aware that all images are made up of color

and every pixel has color intensity for RGB (red green blue)

between 0-255.These color intensity comes out as binary

values. So above mentioned BIT Table is made from binary

values of color of image. As we know all communication on

computer is in binary form that is 0 and 1. So message to be

sent is converted into binary and it is spread in bit table such

that 0 of message match with 0 of bit table and 1 with 1 as

explained in example given below. After spreading binary

bits of message in bit table locations of bit table are stored

where bits of message are spread and location array with

these locations is transferred over network as explained in

example given below. On the receiver end values in location

array location are retrieved from bit table available at

receiver end received initially physically. [4]These values

are converted into character form which gives original

message. Important point in this algorithm is that it is two

way algorithm as sender can become receiver any time and

receiver can become sender any time as both parties have

same bit table mutually agreed and received physically (By

Transporation) as keys are exchanged in key based

algorithms in the starting session.[5]

3. ALGORITHM TO PROVIDE HUNDER PERCENT

SECURITY IN ONLINE TRANSMISSION

Encryption Algorithm( Explained in Fig 1 below)

1.Retain copy of BIT Table mutually agreed upon byreceiver and sender in starting of session

2. Secret message is taken

3.Secret message is converted into binary

4. the binary values of secret message are spread such that 0and 1 of message match with 0 and 1 of BIT Table

5.The location of BIT Table are stored where digits of secretmessage are stored in their sequence.

6Location array of BIT Table locations is made

7 The location array is transferred over internet to receiver.


47

Decryption Algorithm (Explained in fig 1 below)

1Read those values on Reciever side of BIT Table as given in location array

2These values are Stored in sequence

3These values are Converted from binary to text form

4On conversion Original message is retrieved.

5Decryption is complete.

EXAMPLE

Suppose original messageis numeric 24 which we want to

send online using this algorithm. First we convert 24 into binary, the binary is

0 0 0 1 1.

Suppose in the starting receiver and sender mutually agrees

upon BIT Table given below, copy of given below bit table

is retained by both (receiver and sender) in start of session

physically as key exchange mechanism in key based

algorithm

Reciever end Sender end

1 1 0 1 1 0

0 1 0 0 1 0

1 0 1 1 0 1

1 0 0 1 0 0

Sender spread the message in underlined locations that is

1 1 0

0 1 0

1 0 1

1 0 0

So location array of BIT Table is

1,3 first row third column

2,1 second row first column

2,3 second row third column

3,1 third row first column

3,3 third row third column

We send this location array online over network and at receiver end we read the values of BIT Table at locations

given in Location array which is

1,3 first row third column value retrieved is 0

2,1 second row first column value retrieved is 0

2,3 second row third column value retrieved is 0

3,1 third row first column value retrieved is 1

3,3 second row third column value retrieved is 1

When these values are changed to character gives 24 which

is original message.

4. Case 2 Application of This algorithm in Oral TelephonicConversation Even this basic algorithm can be used to secure oral

communication on telephone. Now a days this is major

problem that our secret plans of defense, politicians,

scientists ect are Intercepted and privacy of message is

spoiled completely. The necessary information goes in the

hands of illegal persons. This way basic need of

communication to provide integrity and security is spoiled.

To overcome this problem this case 2 application of above

algorithm is suggested which will provide hundred percent

integrity and security to oral communication.

5. Princple:

In this algorithm application both receiver and sender has to

mutually agree on one alpha-numeric table in the starting of

session. In this table we write numeric values numbered one

to thirty and above these numbers we write alphabets

randomly rather than in sequence. One copy of this alpha-

numeric table is mutually agreed upon by both receiver and

sender in the starting of session, taken physically by

transportation not online as key-exchange mechanism in key

based algorithms in cryptography. This is the basic condition

to take this alpha-numeric table physically to make session

hundred percent secure life long time. Once alpha-numeric table is exchanged we have to just send

message of digits or by oral communication. Example:

Suppose we want to send excellent

And alpha-numeric table agreed in starting is

v z x y w s r u t o

1 2 3 4 5 6 7 8 9 10

q p l k n m j i

11 12 13 14 15 16 17 18

h g f e d c b a

19 20 21 22 23 24 25 26


48

q y p m

27 28 29 30

To use this algorithm we have to send

By message or orally

22, 3, 24, 22, 13, 13, 22, 15, 9

And receiver will pick the values above these numeric values That is

22 numeric gives e

3 numeric gives x

24 numeric gives c

22 numeric gives e

13 numeric gives l

13 numeric gives l

22 numeric gives e

15 numeric gives n

9 numeric gives t

On combining this original message

Is excellent

6. SECURITY ANALYSIS AND HARDNESS OF

ALGORITHM

There are three type of hardness of any algorithm. i.e.

Polynomial algorithm, NP-Complete algorithm, NP-Hard

algorithm.

1. Polynomial algorithm

This type of algorithm are solvable in polynomial

time , are easy and have defined solution. This type

of algorithms can be solved by computer in defined time without any difficulty.[6]

2. NP-Complete algorithm

This type of algorithms are harder than

polynomial algorithms. There exists only one

solution which can be got through hit and trial

method by trying all possible solutions. This

method take very high processing time of CPU as

computer has to try all possible solutions.

3. NP-Hard

This type of algorithm are hardest as no solution exists for such algorithms and can not be solved to

get the solution. Such algorithm take infinite time means impossible to solve .[7]

So the above mentioned algoritms case 1 and case 2 are case

of NP-Hard for intruder/attacker/hacker as they cannot solve

these algorithms to get the original message by any means.

The reason behind this is that by online medium we are

sending only address values from where we have to pick

values but not the values. The message values data is

transported physically in the starting of algorithm as is the

case with key based algorithms in cryptography. On the

other hand at the receiver side it is a polynomial algorithm as

we have both data values and data addresses.[8] Secondly once image/BIT Table/ Alpha-numeric table transported physically in starting of session once gives

hundred percent secure communication for whole life. This

session can be established any time in life. Thirdly this is a two way communication. Reciever and sender can mutually exchange each other any time. This analysis confirms that it is hundred percent secure

algorithm to transfer data over internet with zero cost of

algorithm and within approach of every civilian. What is

required to use this algorithm is internet connection with

basic knowledge of computer fundamental.[9]

7 CONCLUSION

This algorithm confirms hundred percent security and

integrity of data transferred using this algorithm. This algorithm is free of cost as any internet user with basic

knowledge of computer fundamental can use it without any

external requirement.

REFERENCES

[1] https://books.google.com/books?id=Z8WiAwAAQBAJ

[2] http://www.ijsce.org/attachments/File/v3i5/E190011351 .pdf

[3] J. Fridrich, M. Goljan, and R. Du, “Detecting LSB steganography in color, and gray-scale images,” IEEE Multimedia, vol. 8, no. 4, pp. 22– 28, Oct. 2001.

[4] D. Wu and W. Tsai, “A steganographic method for images by pixel value differencing,” Pattern Recognit. Lett., vol. 24, pp. 1613–1626, 2003

[5] Z. Ni, Y.Q. Shi, N. Ansari, W. Su, “Reversible data hiding” , IEEETransactions on Circuits and Systems for Video Technology , 16 , 3,PP 354–362,2006.

[6] X. Li, T. Zeng, and B. Yang, “Detecting LSB matching by applying calibration technique for difference image,” in Proc. 10th ACM Workshop on Multimedia and Security, Oxford, U.K, pp. 133–138, 2008.

[7] D. Wu and W. Tsai, “A steganographic method for images by pixel value differencing,” Pattern Recognit. Lett., vol. 24, pp. 1613–1626, 2003

[8] L.M. Marvel, C.G. Boncelet , & C. Retter, “Spread Spectrum Steganography”, IEEE Transactions on image processing, 8,8, PP 160-178, 2007.

[9] Y.B. Mao,G. Chen. S.G. Lian,”A novel fast image Encryption scheme based on the 3D chaotic baker map,”Int. j. Bifurcate Chaos, vol. 14, pp.3613-3624,2004.


49

Stock Market Data Analysis using Apache

Abhinav Juneja, Shubham Jain, Ekta Gandhi

Department of Computer Science and Engineering,

BM Institute of Engineering and Technology, Sonepat, Haryana, India

Abstract---It this era of digitization, a relatively new

term has started to emerge in the field of Information

Technology. The term is BIG DATA. Big Data refers to any

data that is too large to be processed by traditional database

systems effectively. It requires a lot of computat

to handle such large amounts of data generated everyday

from various sources such as Social Media, Traffic Control,

Weather, Navigation, Stock Market etc.Big Data analysis

allows us to obtain useful information from such gigantic

amounts of data. The data available can be structured or

semi-structured or unstructured.Stock Market data is

Structured data generated from the trading on the stock

market. Every day millions of shares are traded on an

average day at any stock exchange in the world. An

this data requires a lot of computational power and that too

takes a lot of time. Apache Hadoop offers a framework

based on Distributed computing which helps us analyze that

data in a cost effective manner and at a much faster rate.

1. INTRODUCTION

Big datais an evolving term that describes anyvoluminous amount of structured, semistructured andunstructured data that has the potential to be mined forinformation.

Fig1: big data

II .3V of BIG DATA

A.Volume

Volume means amount of data stored. Data nowadaysis more than just text, Large amount of data is createddaily in the form of videos, images. Volume of datastored in enterprise repositories have grown frommegabytes and gigabytes to petabytes. This big volumecomprises Big data


Hadoop Abhinav Juneja, Shubham Jain, Ekta Gandhi


BM Institute of Engineering and Technology, Sonepat, Haryana, India

digitization, a relatively new

term has started to emerge in the field of Information

Technology. The term is BIG DATA. Big Data refers to any

data that is too large to be processed by traditional database

systems effectively. It requires a lot of computational power

to handle such large amounts of data generated everyday

from various sources such as Social Media, Traffic Control,

Weather, Navigation, Stock Market etc.Big Data analysis

allows us to obtain useful information from such gigantic

a. The data available can be structured or

structured or unstructured.Stock Market data is

Structured data generated from the trading on the stock

market. Every day millions of shares are traded on an

average day at any stock exchange in the world. Analysis of

this data requires a lot of computational power and that too

takes a lot of time. Apache Hadoop offers a framework

based on Distributed computing which helps us analyze that

data in a cost effective manner and at a much faster rate.

Big datais an evolving term that describes any voluminous amount of structured, semistructured and unstructured data that has the potential to be mined for

Data nowadays Large amount of data is created videos, images. Volume of data

stored in enterprise repositories have grown from megabytes and gigabytes to petabytes. This big volume

Big data

B.VelocityThe rapid growth in data and social media explosionhave changed how we used to look at the data. Velocitymeans the speed of data processing. processes such as catching fraud, big data must be usedas it streams into your enterprise in order to maximize itsvalue.

C.VarietyVariety refers to different types of data and data sources.The data variety may vary from structured data tounstructured, semi structured, video, audio, XML etc.This large variety of data represent Big data.

3. PROBLEMS IN BIG DATA PROCESSING

•••• Heterogeneity and Incompleteness

When humans analyse data , they can comfortably

tolerate heterogeneity. But machine analysis algorithms

need homegeneous data for processing. So, before

processing the data for analysis, it must be carefully

structured.

•••• Scale

“Big Data” describes a large database with structured as

well unstructured data. Managing a large database is a

very tedious task.Earlier, this problem was

solved by the processors getting faster but now data

volumes are becoming huge and processors are static.

World is moving towards the Cloud technology, due to

this shift data is generated at a very high rate. New and

efficient storage systems are needed to store this big

amount of data.

•••• Timeliness

The big challenge with large databases is speed. It is very

obvious that, The larger the database, longer it will take

to analyze.Speed of analyzing the data is a big challenge.

•••• Privacy

This is the another big problem with the Big data.

the data privacy issues there are strict laws in some

countries. Privacy is both a technical and social issue

which must be addressed jointly to fulfill the promise of

Big data.

•••• Human Collaboration

In this era of technology , we have advanced

computational models but there are m

computer cannot detect. The field of Big Data is not


he rapid growth in data and social media explosion have changed how we used to look at the data. Velocity means the speed of data processing. For time-sensitive processes such as catching fraud, big data must be used

in order to maximize its

Variety refers to different types of data and data sources. The data variety may vary from structured data to unstructured, semi structured, video, audio, XML etc. This large variety of data represent Big data.

PROBLEMS IN BIG DATA PROCESSING

Heterogeneity and Incompleteness

When humans analyse data , they can comfortably

tolerate heterogeneity. But machine analysis algorithms

need homegeneous data for processing. So, before

it must be carefully

“Big Data” describes a large database with structured as

well unstructured data. Managing a large database is a

very tedious task.Earlier, this problem was

solved by the processors getting faster but now data

are becoming huge and processors are static.

World is moving towards the Cloud technology, due to

this shift data is generated at a very high rate. New and

efficient storage systems are needed to store this big

e with large databases is speed. It is very

obvious that, The larger the database, longer it will take

to analyze.Speed of analyzing the data is a big challenge.

This is the another big problem with the Big data. Due to

there are strict laws in some

countries. Privacy is both a technical and social issue

which must be addressed jointly to fulfill the promise of

In this era of technology , we have advanced

computational models but there are many patterns that a

computer cannot detect. The field of Big Data is not


50

automatic i.e. it needs human intervention. Hence,

Experts from various field have to design the model and

work to constantly improve it for better analysis.

4. HIVE

It is a Data Warehouse Software built in ApacheHadoop for Querying and Managing Large DistributedDatasets. Apache Hiveis a component of HortonworksData Platform(HDP). Hiveprovides a SQLto data stored in HDP. Hiveprovides a database queryinterface to Apache Hadoop.

6. SOLUTION FOR BIG DATA PROCESSING

HADOOP

Hadoop is a Programming framework used to supportthe processing of large data sets in a distributedcomputing environment.

The need of Hadoop emerged with the avalanche ofBig Data. The World Wide Web was generating data at atremendous rate. The data generated had a lot of potentialafter analysis but the cost of doing so was alsotremendous.

The idea for Hadoop evolved in 2003 when Googlepublished a paper named Google File System fby MapReduce: Simplified Data Processing on LargeClusters.

This inspired Doug Cutting and Mike Cafarella tocreate Hadoop, an open source framework called Hadoopwhich offered Big data processing on a Distributedplatform.

Hadoop was designed with a simple writestorage infrastructure. It was initially developed as afilesystem which offered much faster data accession aswell as stored multiple copies of the data providing a failsafe storage solution. HDFS or Hadoop Distributed FileSystem is a reliable storage infrastructure that stores thedata in the form of blocks. These blocks are replicated ondifferent servers for reliable storage.

Hadoop has moved far beyond its beginnings in webindexing and is now used in many industries for a hugevariety of tasks that all share the common theme of lotsof variety, volume and velocity of data – both structuredand unstructured.

It is now widely used across industries, includingfinance, media and entertainment, government,healthcare, information services, retail, and otherindustries with big data requirements but the limitationsof the original storage infrastructure remain. Hadoop isincreasingly becoming the go-to framework for largescale, data-intensive deployments. Hadoop is built toprocess large amounts of data from terabytes to petabytesand beyond. With this much data, it’s unlikely

The Current Apache Hadoop ecosystem consists ofthe Hadoop Kernel, MapReduce, HDFS and numbers ofvarious components like Apache Hive, Base andZookeeper. HDFS and MapReduce are explained infollowing points.

A.Hadoop Distributed File System (HDFS)

Hadoop File System is the default file system on theservers in a Hadoop Cluster. It is developed usingdistributed file system design. It is run on commodity

automatic i.e. it needs human intervention. Hence,

Experts from various field have to design the model and

work to constantly improve it for better analysis.

It is a Data Warehouse Software built in Apache Hadoop for Querying and Managing Large Distributed

Apache Hiveis a component of Hortonworks Data Platform(HDP). Hiveprovides a SQL-like interface to data stored in HDP. Hiveprovides a database query

SOLUTION FOR BIG DATA PROCESSING-

Hadoop is a Programming framework used to support the processing of large data sets in a distributed

The need of Hadoop emerged with the avalanche of The World Wide Web was generating data at a

tremendous rate. The data generated had a lot of potential after analysis but the cost of doing so was also

The idea for Hadoop evolved in 2003 when Google published a paper named Google File System followed by MapReduce: Simplified Data Processing on Large

This inspired Doug Cutting and Mike Cafarella to create Hadoop, an open source framework called Hadoop which offered Big data processing on a Distributed

h a simple write-once storage infrastructure. It was initially developed as a filesystem which offered much faster data accession as well as stored multiple copies of the data providing a fail-safe storage solution. HDFS or Hadoop Distributed File

s a reliable storage infrastructure that stores the data in the form of blocks. These blocks are replicated on

Hadoop has moved far beyond its beginnings in web indexing and is now used in many industries for a huge variety of tasks that all share the common theme of lots

both structured

It is now widely used across industries, including finance, media and entertainment, government,

rvices, retail, and other industries with big data requirements but the limitations of the original storage infrastructure remain. Hadoop is

to framework for large intensive deployments. Hadoop is built to

arge amounts of data from terabytes to petabytes and beyond. With this much data, it’s unlikely

The Current Apache Hadoop ecosystem consists of the Hadoop Kernel, MapReduce, HDFS and numbers of various components like Apache Hive, Base and

and MapReduce are explained in

Hadoop Distributed File System (HDFS)12

Hadoop File System is the default file system on theservers in a Hadoop Cluster. It is developed using distributed file system design. It is run on commodity

hardware. Generally Distributed System are not designedto be Fault tolerant but HDFS is highly fault tolerant. Itstores multiple copies of data on low-helps in reducing the latency time as well as high dataavailability. The multiple copies helps HDFS recover thelost data in the event of a disk failure and ensures no datais lost. HDFS also makes applications available toparallel processing.

HDFS is designed using MASTERarchitecture. The Master node in this case is theNAMENODE and the slave node is the DATANODE

Fig2:Hadoop Master Slave Architecture

• Namenode

It is the manager of data on the entire HadoopCluster. Whenever a file is stored onto the HDFS, it issplit into blocks. The location of these blocks is managedby the Name Node. It also makes multiple copies of thedata for better reliability and availability. Since all theinformation is stored on namenode is very important,hence the Name Node is of High quality hardware and abackup of it is stored on a Network Drivefailure.

• Data Node

A Hadoop cluster comprises of several data nodes tostore the data. Each datanode is divided into blocks andeach block is allocated by the Name Node. Each blockcontains a part of the original file and no otherinformation about the other parts is available on the datanode.

B.Map-Reduce Architecture

Fig:3 Map reduce Architecture

MapReduce is the processing pillar in the Hadoopecosystem. It is mainly used for parallel processing oflarge sets of data stored in Hadoopframework an operation is applied on a huge data set,divide the problem and data and run it in parallel. Thiscan be done in multiple dimensions. For example a largedata set can be divided into smaller subsets where theoperation can be applied. In Hadoop, this is done bywriting MapReduce functions in java

hardware. Generally Distributed System are not designed to be Fault tolerant but HDFS is highly fault tolerant. It

-cost hardware. This helps in reducing the latency time as well as high data

copies helps HDFS recover the lost data in the event of a disk failure and ensures no data is lost. HDFS also makes applications available to

HDFS is designed using MASTER-SLAVE architecture. The Master node in this case is the

E and the slave node is the DATANODE

Fig2:Hadoop Master Slave Architecture

It is the manager of data on the entire HadoopCluster. Whenever a file is stored onto the HDFS, it is split into blocks. The location of these blocks is managed

ame Node. It also makes multiple copies of the data for better reliability and availability. Since all the information is stored on namenode is very important, hence the Name Node is of High quality hardware and a backup of it is stored on a Network Drive in the event of

A Hadoop cluster comprises of several data nodes tostore the data. Each datanode is divided into blocks and each block is allocated by the Name Node. Each block contains a part of the original file and no other

on about the other parts is available on the data

MapReduce is the processing pillar in the Hadoop ecosystem. It is mainly used for parallel processing of large sets of data stored in Hadoop cluster. In this framework an operation is applied on a huge data set, divide the problem and data and run it in parallel. This can be done in multiple dimensions. For example a large data set can be divided into smaller subsets where the

applied. In Hadoop, this is done by


51

These MapReduce program can be written easily using high level languages like Hive and Pig. The output of these programs is written back to either HDFS or in traditional data warehouse.

Two functions in MapReduce algorithms are as follows:

Map – This function takes some Key/value pairs as its input and generates an immediate output as key/value pairs.

Reduce – It is responsible for collaborating all the values with the same key together.

7. STOCK MARKET DATA ANALYSIS

There is high volume of data available from stock market trading. Every stock exchange manages the data whether it be NSE, BSE, NYSE, NASDAQ. The data obtained is completely unstructured. Hence, analysis of the data is difficult. The data needs to be transformed into meaningful format for analysis. Analysis of this data requires a lot of computational power and that too takes a lot of time.

The structured data obtained is loaded into the Hadoop Distributed File System. Once the data is loaded, the Hadoop File system makes copies of that data on different servers. This feature of HDFS provides high availability of data as well as Fault tolerance. If a single node fails, the backup copies are present.

The data is then mapped first using the map function and then reduced using the reduce function.

Alternatively, Apache PIG can do this job.

The Hadoop cluster can also be used for static analysis of data. The data set can be imported into the HDFS and processed.

Hence, the analytics of historic data can be done as well. Hadoop also supports live streaming of data for real-time analysis.

REFERENCES

1. Sulochana Panigrahi and S Mohan Kumar, “A survey onsocial data processing using apache Hadoop, Map-Reduce,” International Journal of Scientific and TechnicalAdvancements, Volume 2, Issue 2, pp. 121-123, 2016

2. Harshawardhan S. Bhosale , Prof. Devendra P. Gadekar,”A Review Paper on Big Data and Hadoop“,”InternationalJournal of Scientific and Research Publications, Volume 4,Issue 10, October 2014”

3 .Rahul Beakta,”Big Data And Hadoop: A Review Paper”,”Recent Innovation in Electronics, Electrical & Computer Science Engineering – 2015”

4. Ashwini A. Pandagale & Anil R. Surve,”Big Data AnalysisUsing Hadoop Framework”,”International Journal ofResearch and Analytical Reviews 2016”

5. Harin C Naik, Divyesh Joshi,”A Hadoop FrameworkRequire to Process Bigdata very Easily andEfficiently”,”International Journal of Scientific Researchin Science, Engineering and Technology-2016”

6 .Apache Hadoop at https://hortonworks.com/apache/hadoop/

7. Veereshetty Dagade, Abhishek Akkatangerhal, AmrutaDeshpande, Rucha Bhandiwad,”Big Data Stock Analysisusing Hadoop”,”International Journal of EmergingTechnology in Computer Science and Electronics- April2015”

8. Big Data from https://en.wikipedia.org/wiki/Big_data

9. 3Vs definition from http://whatis.techtarget.com/definition/3Vs

10. http://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data

11. The Challenges of Block Chain Indexing fromhttps://medium.com/@lopp/the-challenges-of-block-chain-indexing-30527cf4bfbd

12. HDFS from https://www.dezyre.com/article/hadoop-components-and-architecture-big-data-and-hadoop-training/114


52

Generation of Business Intelligence by

Sentimental Analysis through Big Data and

Hadoop 1Abhinav Juneja,

2Prayans Jain,

3Siddharth

1Associate Professor, Department of CSE, BMIET, Sonepat

2,3B.Tech, CSE, 4

th Year, BMIET, Sonepat

Abstract--Online networking gives clients a platform

to discuss successfully with companions, family, and

partners, and gives them a stage to discuss their top

pick (and minimum most loved brands). This

"unstructured" discussion can provide organizations

crucial understanding into how buyers see their image,

and enable them to effectively settle on business choices

to keep up their horizons. Fast in the volume of

conclusion rich online networking on the web has

brought about an expanded enthusiasm among

specialists with respect to Sentimental Analysis and

Opinion Mining. In any case, with so much web-based

social networking accessible on the web, Sentiment

Analysis is presently considered as a Big Data

assignment. The concentration of the examination was

to discover such a system, to the point that can

productively perform Sentiment Analysis on Big Data

sets. In this paper Sentiment Analysis was performed

on an extensive informational index of tweets utilizing

Hadoop and the execution of the strategy was

measured in type of speed and precision. The test result

demonstrates that the procedure displays great

effectiveness in taking care of huge feeling

informational collections. Today in the era of cloud and

matrix involving the incorporation of information from

heterogeneous databases is unavoidable. This will end

up plainly complex when size of the database is

exceptionally tremendous. MapReduce is another

system particularly actualized for preparing vast

datasets on conveyed sources. Hadoop has inner

complex structure like MapReduce to execute the faster

execution on inquiry and provides the quick outcome.

To improve the execution, we are utilizing Hadoop

stage which has ability to deal with Big data.

I. INTRODUCTION

Big Data is upcoming area of research in Computer Science and, Sentiment Analysis is one of the most important component of this research area. Big Data is considered as very large amount of data which can be found easily on web, Social media networks, remote sensing data and medical services records etc. in form of structured, semi-structured or

unstructured data and we can utilize this data for Sentiment Analysis.

Sentimental Analysis is all about to get the real voice of people towards specific product, services, organization, movies, news, events, issues and their attributes[1]. By using approaches, methods, techniques and models of defined branches, we can categorize our unstructured data which may be in the form of news articles, blogs, tweets, movie reviews, product reviews etc. into positive, negative or neutral sentiment according to the sentiment expressed in them.

A. Level of sentiment

1) Attribute-Level Analysis

Attribute level analysis provides a sentiment foreach object in a sentence .This behavior is the default level of analysis for Pulse. Attribute analysis identifies the objects of a sentence and any sentiment expressed regarding those objects.

2) Sentence -Level Analysis

A sentence level analysis provides the overallsentiment of each sentence in a document. If a sentence is contains both positive and negative sentiments, it appears as mixed [1].

3) Document -Level Analysis

Document level analysis provides the overallsentiment of an entire document. If you wanted to know if a movie review was positive, negative, or mixed, a document level analysis could provide that information. Document level analysis gives both the overall sentiment score and a mixed rating if the sentiment is not exclusively positive or negative [1].

II. RELATED WORKS

A. Hadoop

Apache’s Hadoop is an implementation of Map Reduce. Hadoop has been applied successfully for


53

file based datasets. The Apache Hadoop project develop open-source software for reliable distributed computing system. Existing tools are not designed to handle such large amount of data. Hadoop avoids the drawbacks by effectively storing and providing computational capabilities over substantial amounts of data.

B. Map Reduce

Hadoop MapReduce (Hadoop Map/Reduce) is a software application framework for the distributed processing of large data sets on compute clusters of commodity hardware. It is a sub-project of the Apache Hadoop project. The framework takes care of scheduling tasks, monitoring them and re-executing of any failed tasks.

According to The Apache Software Foundation, the primary objective of Map/Reduce is to split the input data set into independent blocks that are processed in a dominanantly parallel manner. The Hadoop MapReduce framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically, both the input and the output of the job are stored in a file system.

Map: The master node takes the input and divides it into smaller sub problems and distributes them to worker nodes. A worker node may consider to do this repeatedly, leading to a multi-level tree structure. The worker nodes processes the small problems and sends the results back to its master node [2].

Reduce: The master node in turn collects the results to all the sub problems and integrates them in some way to form the final output – the result to the problem it was originally trying to solve. As the various tasks are run in parallell, it manages all communications and data transfers between the various parts of the system [2].

C. Sentimental analysis

Sentiment analysis (sometimes known as opinion mining or emotion AI) refers to the utilization of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Generally speaking, sentiment analysis is applied to determine the attitude of a speaker, writer, or other subject with respect to some topic or the overall

polarity contextually or emotional reaction to some document, interaction, or event. The attitude may be a judgment or evaluation (see appraisal theory), affective state (that is to say, the emotional state of the author or speaker), or the intended emotional communication (that is to say, the emotional effect intended by the author or interlocutor).

III. EXISTING METHOD

Vocabulary Based systems take a shot at a presumption that the aggregate extremity of a report or sentence is the entirety of polarities of the individual words or expressions. A portion of the noteworthy works done utilizing this system are:

Kamps [3] utilized a basic system in view of lexical relations to perform grouping of content.

Andrea [4] utilized word net to characterize the content utilizing a suspicion that words with comparative extremity have comparative introduction.

Ting-Chun [5] utilized a calculation in view of pos (grammatical feature) patter. A content expression was utilized as a question for a web crawler and the outcomes were utilized to characterize the content.

Prabhu [6] which utilized a straightforward vocabulary based method to remove opinions from twitter information.

Turney [7] utilized semantic introduction on client surveys to distinguish the fundamental estimations.

Taboada [8] utilized dictionary based way to deal with extricate estimations from smaller scale web journals.

Assumption examination for smaller scale sites is all the more difficult as a result of issues like utilization of short length status message, casual words, word shortening, spelling variety and emojis. Twitter information was utilized for sentimental analysis [9].

Negative word can switch the extremity of any sentence. Taboada performed notion investigation while taking care of nullification and elaborating words. Part of refutation was reviewed. Minquing [10]. grouped the content utilizing a basic dictionary based approach with highlight identification. It was watched that a large portion of these current strategies doesn't scale to huge informational collections effectively. While different machine learning systems shows preferable exactness over vocabulary based methods, they take additional time in preparing the calculation and henceforth are not reasonable for huge informational indexes. In this


54

paper, dictionary based approach is utilized to groupthe content as per extremity.

IV. PROPOSED WORK

A. System Architecture

In the system Architecture it shows thesentimental analysis of the selected product using theBig Data [11]. We are taking the Twitterwhich is huge data and increasing day by day. Forhandling this big data, we used Hadoop platformwhich is developed by Java and which have internalframework like Map Reduce [12]. For Hadoop werequired the Linux operating system hence we arecreated virtual machine. Twitter Data is passed to theHadoop core (VMware) by using SCP tool. Hadooptakes the twitter data which processed it and generatethe structure data. By analyzing the sentiment whichcustomer is posted on twitter like good feedbackbad feedback. After analyzing the data we are goingto show feedback system is going to display ingraphical format.

Fig1. System Architecture

B. Proposed Work

We are developing a system that analyzessentiment posted on twitter. Like positive tweets ornegative tweets. The sentimental analysis is doneusing tweets on twitter. Also, the system is going tohandle big data which will be continuouslyincreasing and our system will analyze based on treal-time data. To optimize the performance, we areusing Hadoop platform which has capability tohandle the big data. After analyze the data we aregoing to show sentiment

dictionary based approach is utilized to group

PROPOSED WORK

In the system Architecture it shows the sentimental analysis of the selected product using the

. We are taking the Twitter Database which is huge data and increasing day by day. For handling this big data, we used Hadoop platform which is developed by Java and which have internal

. For Hadoop we required the Linux operating system hence we are

ated virtual machine. Twitter Data is passed to the Hadoop core (VMware) by using SCP tool. Hadoop takes the twitter data which processed it and generate the structure data. By analyzing the sentiment which customer is posted on twitter like good feedback or bad feedback. After analyzing the data we are going to show feedback system is going to display in

a system that analyzes the sentiment posted on twitter. Like positive tweets or negative tweets. The sentimental analysis is done using tweets on twitter. Also, the system is going to handle big data which will be continuously increasing and our system will analyze based on the

time data. To optimize the performance, we are using Hadoop platform which has capability to handle the big data. After analyze the data we are

C. Proposed Procedure

The focus of this project was to discover anapproach that can perform Sentiment Analysis on thegrounds that huge volume of information shouldhave been harvested. Likewise, it must be ensuredthat exactness isn't traded off excessively whileconcentrating on speed of decision making.Prediction Analysis on Big Data is accomplished byworking together on Big Data with Hadoop

The proposed approach is a word reference basedstrategy i.e. a lexicon of slant bearing words wasutilized to group the content into positive, negativeor nonpartisan conclusion. Machine learningtechniques [13] are not utilized in light of the factthat in spite of the fact that they are more exact thanthe word reference based methodologies, they take toan extreme degree an excessive amount of timeperforming Sentiment Analysis as they must beprepared first and thus are not effective in takingcare of huge supposition information.

1. Real Time Data and Features:

• Length

The maximum length of a tweet is about 140characters. This is very different from the previoussentiment classification research that focused onclassifying longer bodies of work, such asreviews.

• Data Availability

Another difference is the magnitude of dataavailable. With the Twitter API, twitter4j [14very easy to collect millions of tweets for trainingwhich allows the developer an access to 1% oftweets tweeted at that time basis on the particularkeyword.

• Language Model

Twitter users post messages from many differentmedia, including their cell phones. The frequency ofmisspellings and slang in tweets is much higher thanin other domains.

• Domain

Twitter users post short messages about a varietyof topics unlike other sites which are tailored to aspecific topic. This differs from a large percentage ofpast research, which focused on specific domainssuch as movie reviews.

D. Sentimental Dictionary

The dictionary contains all forms of a word i.e.every word is stored along with its various verbforms e.g. applause, applauding, applauded,applauds. Hence eliminating the need for stemming

The focus of this project was to discover an an perform Sentiment Analysis on the

grounds that huge volume of information should have been harvested. Likewise, it must be ensured that exactness isn't traded off excessively while concentrating on speed of decision making.

ta is accomplished by Big Data with Hadoop

The proposed approach is a word reference based strategy i.e. a lexicon of slant bearing words was utilized to group the content into positive, negative or nonpartisan conclusion. Machine learning

are not utilized in light of the fact in spite of the fact that they are more exact than

the word reference based methodologies, they take to an extreme degree an excessive amount of time performing Sentiment Analysis as they must be prepared first and thus are not effective in taking

huge supposition information.

Real Time Data and Features:

The maximum length of a tweet is about 140 characters. This is very different from the previous sentiment classification research that focused on classifying longer bodies of work, such as movie

Another difference is the magnitude of data h the Twitter API, twitter4j [14], it is

very easy to collect millions of tweets for training which allows the developer an access to 1% of

me basis on the particular

Twitter users post messages from many different media, including their cell phones. The frequency of misspellings and slang in tweets is much higher than

messages about a variety of topics unlike other sites which are tailored to a specific topic. This differs from a large percentage of past research, which focused on specific domains

The dictionary contains all forms of a word i.e. every word is stored along with its various verb forms e.g. applause, applauding, applauded, applauds. Hence eliminating the need for stemming


55

each word which saves more time. The also contains the strength of the polarity Dictionary of every word. Some word depicts stronger emotions than others. For example, good and great are both positive words but great depict a much stronger emotion.

E. Handling Invalidation AND Daze nullification

(Negation and Blind Negation)

Invalidation words are the words which turn around the extremity of the feeling associated with the content. For instance, „the film was not good‟. Despite the fact that the word „good‟ delineates a positive opinion the invalidation – „not‟ turns around its extremity. In the proposed approach at whatever point a negation word is experienced in a tweet, its polarity is reversed [13,15,16,].

Daze nullification words are the words which work on the sentence level and calls attention to a component that is wanted in an item or administration. For instance, in the sentence „the acting should have been better‟, „better‟ delineates a positive assumption however the nearness of the visually impaired invalidation word-„needed ‟suggests that this sentence is really portraying negative notion. In the proposed approach at whatever point a visually impaired invalidation word happens in a sentence its extremity is instantly named as negative.

V ALGORITHM

Algorithm: ALGO_SENTICAL

Input: Tweets, SentiWord_Dictionary

Output: Sentiment (positive, negative or neutral)

BEGIN

1)For each tweet Tido the following

2)Initialize SentiScore = 0;

3)For each word Wj in Ti that exists in

Sentiword_Dictionary.

If polarity[Wj] = blind negation then Return

negative.

Else

If polarity[Wj] = “acceptable ” then increment

seniscore by 1.

Else If polarity [Wj] = “average OR good” then add

2 to sentiscore.

if polarity [Wj] = “better OR best” then increment

sentiscore by 3.

Else If polarity [Wj] = “Excellent” then add 4 to

sentiscore.

Else If polarity [Wj] = “below average ” then

decrement sentiscore by 1.

Else If polarity [Wj] = “bad “then subtract 2 from

sentiscore.

Else If polarity[Wj] = “worse ” then decrement

sentiscore by 3.

Else If polarity[Wj] = “not acceptable “then subtract

4 from sentiscore.

If Sentiscore of Ti>0 then Sentiment = positive.

Else If Sentiscore of Ti<0 then Sentiment = negative.

Else Sentiment = neutral

4)Return Sentiment

5)END

Fig. 2 Different Categories of Expressions

V. SCOPE OF THE SYSTEM

The current technique involves sentiments of the user which is plain text format like tweet from twitter and it is on big data which is continuously increasing.

For performance of the system we are using Hadoop platform which is going to handle big data.

This system is useful to improve the quality of the product and customer satisfaction which will be useful for business growth.

VI. CONCLUSION AND FUTURE

WORK

Sentimental Analysis is being utilized for various applications and can be utilized for a few others in future. It is clear that its applications will grow to more zones and will keep on encouraging increasingly exploration in the field. In this project work, fundamental concentration was on performing Sentiment Analysis rapidly with the goal that Big Data sets can be taken care of effectively. The work can be additionally extended by presenting procedures that expand the exactness by handling issues like non conventional expressions, articulations and certain assumptions which still should be settled appropriately. Additionally, as clarified prior, this work is being actualized on a solitary hub arrangement and despite the fact that it is normal that it will perform much better in a


56

multimode venture level setup, it is attractive to check its execution in such condition in future.

REFERENCES

[1] Bing Liu, Sentiment Analysis and Opinion Mining, Morgan

and Claypool Publishers, May 2012.p.18-19,27-28,44-45,47,90-

101.

[2] D.Gillick, A.Faria, and J.DeNero, “MapReduce”:

DistributedComputing for Machine Learning”, IEEE Transaction,

Dec 2006 .

[3] Kamps, Maarten Marx, Robert J. Mokken and Maarten De

Rijke, “Using wordnet to measure semantic orientation of

adjectives”, Proceedings of 4th International Conference on

Language Resources and Evaluation, pp. 1115-1118, Lisbon,

Portugal, 2004.

[4] Andrea Esuli and Fabrizio Sebastiani, “Determining the

semantic orientation of terms through gloss classification”,

Proceedings of 14th ACM International Conference on

Information and Knowledge Management,pp. 617-624, Bremen,

Germany, 2005.

[5] Ting-Chun Peng and Chia-Chun Shih , “An Unsupervised

Snippet-based Sentiment Classification Method for Chinese

Unknown Phrases without using Reference Word Pairs”, 2010

IEEE/WIC/ACM International Conference on Web Intelligence

and intelligent Agent Technology JOURNAL OF COMPUTING,

VOLUME 2, ISSUE 8, AUGUST 2010, ISSN 2151-9617 .

[6] Prabu Palanisamy, Vineet Yadav, Harsha Elchuri, “Serendio:

Simple and Practical lexicon based approach to Sentiment

Analysis”, Serendio Software Pvt Ltd, 2013.

[7] Peter Turney and Michael Littman. 2003. Measuring praise

and criticism: Inference of semantic orientation from association.

ACM Transactions on Information Systems 21(4):315–346.

[8]Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll

and Manfred Stede. 2011. Lexicon-based methods for sentiment

analysis. Computational linguistics, volume 37, number2, 267–

307, MIT Press.

[9] Albert Bifet and Eibe Frank. 2010. Sentiment knowledge

discovery in twitter streaming data, Discovery Science 1–14,

Springer.

[10] Minqing Hu, Bing Liu. Mining and Summarizing Customer

Reviews, Department of Computer Science, University of Illinois

at Chicago, Research Track Paper.

[11] Xindong Wu, Fellow,Xingquan Zhu, Gong-Qing Wu, and

Wei Ding ”Data Mining with Big Data”, IEEE Transaction,

JANUARY 2014.

[12] ].Ralf Lammel. Google's MapReduce Programming Model

Revisited.Science of Computer Programming archive. Volume 68,

(2008).

[13] Long-Sheng Chen, Cheng-Hsiang Liu, Hui -Ju Chiu, “A

neural network based approach for sentiment classification in the

blogosphere”, Journal of Informetrics 5 (2011) 313–322.

[14] http://twitter4j.org/en/index.html.

[15] Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly

Voll and Manfred Stede. 2011. Lexicon based methods for

Sentiment Analysis. Computational linguistics, volume 37,

number2, 267–307, MIT Press. .

[16] Michael Wiegand, Alexandra Balahur, Benjamin Roth,

Dietrich Klakow, Andrés Montoyo. 2010. Asurvey on the role of

negation in Sentiment Analysis. Proceedings of the workshop on

negation speculation in natural language processing 60–68,

Association for Computational Linguistics.


57

Dynamic Update and Public Auditing with Dispute

Arbitration for Cloud Data

Dr.B.Mahesh

Associate Professor, Department of CSE, Malla Reddy Engineering College and Management Sciences

Medchal, Telangana, India

[email protected]

Abstract—Cloud computing is a kind of computing that relies

on allocation computing resources rather than having local

servers or personal devices to handle applications. Storage

outsourcing became a upward trend with the arrival of the cloud

computing promoting the secure remote data auditing to be

appeared in the explore. Moreover this explore considers the

problem of data dynamics support, public verifiability and

dispute arbitration concurrently. The data dynamics problem in

auditing is solved by introducing an index switcher to keep a

mapping amid block indices and tag indices, and purge the

passive effect of block indices in tag computation lacking

incurring much overhead. We provide equality guarantee and

dispute arbitration in our scheme, which ensures that both the

data owner and the cloud cannot act up in the auditing process or

else it is easy for a third-party arbitrator to find out the devious

party. The system is extended by implementing the data

dynamics and fair arbitration on groups in future.

Keywords—Integrity auditing, public verifiability, dynamic

update, arbitration, fairness.

I. INTRODUCTION

Data outsourcing is a key application of cloud computing, which relieves cloud users of the heavy burden of data management and infrastructure maintenance, and provides fast data access autonomous of physical locations. However, outsourcing data to the cloud brings about lots of new security threats. Firstly, regardless of the powerful machines and well-built security mechanisms provided by cloud service providers (CSP), secluded data still face network attacks, hardware failures and administrative errors. Secondly, CSP may regain storage of rarely or never accessed data, or even hide data loss accidents for standing reasons. As users no longer physically hold their data and consequently lose direct control over the data, direct employment of traditional cryptographic primitives like hash or encryption to make sure remote data’s integrity may lead to many security loopholes. In particular, downloading all the data to check its integrity is not feasible due to the classy communication overhead, especially for large-size data files. In this sense, message authentication code (MAC) or signature based mechanisms, while extensively used in secure storage systems, are not suitable for integrity check of outsourced data, because they can only verify the integrity of retrieved data and do not work for infrequently accessed data (e.g., archive data). So how to ensure the rightness of outsourced data without possessing the original

data becomes a challenging task in cloud computing, which, if not effectively handled, will impede the wide deployment of cloud services.

Data auditing schemes can facilitate cloud users to check the integrity of their remotely stored data without downloading them locally, which is termed as blockless verification. With auditing schemes, users can periodically interact with the CSP through auditing protocols to check the accuracy of their

outsourced data by verifying the integrity proof computed by the CSP, which offers stronger assurance in data security because user’s own conclusion that data is integral is much more persuasive than that from service providers. Generally speaking, there are several trends in the development of auditing schemes. First of all, earlier auditing schemes usually require the CSP to generate a deterministic proof by accessing the whole data file to perform integrity check, e.g., schemes in [1], [2] use the entire file to execute modular exponentiations. Such plain solutions gain expensive computation overhead at the server side, hence they lack efficiency and practicality when dealing with large-size data. Represented by the ”sampling” method in ”Proofs of Retrievability” (PoR) [3] model and”Provable Data Possession” (PDP) [4] model, later schemes [5], [6] tend to present a probabilistic proof by accessing branch of the file, which clearly enhances the auditing efficiency over past schemes. Secondly, a few auditing schemes [3], [7] offer private verifiability that need only the data owner who has the private key to do the auditing task, which may potentially overload the owner due to its limited computation capability. Ateniese el al. [4] was the first to suggest to enable public verifiability in auditing schemes. In disparity, public auditing schemes [5], [6] allow anyone who has the public key to perform the auditing, which makes it possible for the auditing task to be delegated to an external third party auditor (TPA).

A TPA can perform the veracity check on behalf of the data owner and truthfully report the auditing result to him [8]. Thirdly, PDP [4] and PoR [3] mean to audit static data that are hardly ever updated, so these schemes do not supply data dynamics support. But from a universal viewpoint, data update is a very common requirement for cloud applications. If auditing schemes could only deal with static data, their viability and scalability will be limited. On the other hand, direct extensions of these static data oriented schemes to


58

support dynamic update may source other security threats, as explained in [6]. To our knowledge, only schemes in [6], [9], [10] provide built-in support for fully data dynamic operations(i.e., amendment, addition and removal), but they are deficientin providing data dynamics support, public verifiability andauditing efficiency simultaneously, as will be analyzed in thesection of related work. From these trends, it can be seen thatgiven that probabilistic proof, public verifiability and datadynamics support are three most crucial characteristics inauditing schemes. Among them, providing data dynamicssupport is the most challenging. This is because nearly allexisting auditing schemes intend to implant a block’s indexinto its tag computation, e.g., H(i||v) in [4] or H(name||i) in [5],which serves to authenticate challenged blocks. Conversely, ifwe insert or delete a block, block indices of all subsequentblocks will change, then tags of these blocks have to be re-computed. This is unacceptable because of its highcomputation overhead. We tackle this problem bydifferentiating among tag index (used for tag computation)and block index (indicate block position), and rely an indexswitcher to keep a mapping between them. Upon each updateoperation, we assign a new tag index for the operating blockand update the mapping connecting tag indices and blockindices. Such a layer of indirection among block indices Bandtag indices enforces block authentication and avoids tag re-computation of blocks after the operation positionsimultaneously. As a result, the efficiency of handling datadynamics is greatly enhanced. Furthermore and important, in apublic auditing scenario, a data owner always delegates hisauditing tasks to a TPA who is trusted by the owner but notbasically by the cloud. Current explore usually assumes antruthful data owner in their security models, which has aninborn inclination toward cloud users. However, the fact is,not only the cloud, but also cloud users, have the motive toconnect in deceitful behaviors. For example, a malicious dataowner may intentionally claim data corruption adjacent to anhonest cloud for a money reimbursement, and a dishonest CSPmay delete rarely accessed data to save storage. Therefore, itis of serious importance for an auditing scheme to providesprite guarantee to settle potential disputes between the twoparties. Zheng et al. planned a fair PoR scheme to avoid adishonest client from reproving an honest CSP, but theirscheme only realizes private auditing. Kupccu [12] plannedgeneral arbitration protocols with automated costs using fairsignature exchange protocols [13]. Our stab also adopts theidea of signature exchange to make sure the metadata accuracyand protocol fairness, and we concentrate on combiningefficient data dynamics support and fair dispute arbitrationinto a single auditing scheme. To address the fairness problemin auditing, we introduce a thirdparty arbitrator(TPAR) intoour threat model, which is a specialized institute for conflictsarbitration and is trusted and payed by together data ownersand the CSP. Since a TPA can be viewed as a delegator of thedata owner and is not unavoidably trusted by the CSP, wedistinguish among the roles of auditor and arbitrator.Moreover, we implement the idea of signature exchange toensure metadata correctness and offer dispute arbitration,where any conflict about auditing or data update can be fairlyarbitrated.

II. DYNAMIC AUDITING SCHEME

Let G1, G2 and GT be multiplicative cyclic groups of prime

order p, g1 and g2 be generators of G1 and G2, respectively. Let

e : G1 × G2 → GT be a bilinear map, and H(·) : 0, 1 ∗ → G1

be a secure public map-to-point hash function, which maps a

string 0, 1 ∗ uniformly into an element of G1. Let Sigsk(seq,

Ω) ← (h(seq||Ω))sk

denote a signature on the concatenation of

a sequence number seq and the index switcher Ω using the

private key sk. Let skc and sks denote the private key of the

client and the CSP, respectively. Then the scheme can be

described as follows.

KeyGen. The data owner randomly chooses α ← Zp and u ←

G1, computes v ← g α and w ← u α. The secret key is sk = α

and the public key is pk = (v, w, g, u).

TagGen. Given a data file F = m1, m2, . . . , mn. For each

block mi , the owner computes its tag as σi = (H(ti) · umi

) α,

where ti denotes the tag index of the block. Denote the tag set

by Φ = σi1≤i≤n. Initially, tag indices and block indices are

the same sequence 1, 2, . . . , n, so tag computation can be

simplified as σi = (H(i)·umi

) α, and the TPAR can easily

construct his version of the index switcher. Then, the owner

computes his signature on the index switcher Sigc = Sigskc

(seq0, Ω0), where seq0 is initialized to 0 and Ω0 = (i, ti =

i)1≤i≤n. Finally, the owner sends F, Φ, Sigc to the CSP for

storage and sends pk to the TPAR. The owner deletes its local

copy of F, Φ and keeps the index switcher Ω.

Commitment. This procedure is to avoid a malevolent owner

from generating incorrect tags at the initial stage so that he can

falsely accuse the cloud at a later time. The cloud generates

deterministic proof from all received blocks and tags

according to algorithm ProofGen and verify its validity with

algorithm ProofVerify. If the substantiation succeeds, the

cloud can be convinced that all tags are correctly computed

from received blocks, and then he sends his signature on the

index switcher Sigs = Sigsks (seq0, Ω0) to the client for storage,

where seq0 = 0 and Ω0 = (i, ti = i)1≤i≤n. The client also

verifies the correctness of Sigs, if succeeds, he keeps it;

otherwise he contacts the TPAR for arbitration.

III. SCHEME DESCRIPTION

In presented public auditing schemes [4], [5], [6], [14] mainly focus on the assignment of auditing tasks to a third party auditor (TPA) so that the transparency on clients can be offloaded as much as possible. However, such models have not critically considered the fairness problem as they usually assume an truthful owner against an untrusted CSP. Since the TPA acts on behalf of the owner, then to what level could the CSP trust the auditing result? What if the owner and TPA get together together against an honest CSP for a financial compensation? In this sense, such models diminish the practicality and applicability of auditing schemes. In a cloud scenario, both owners and CSP have the reason to cheat. The CSP makes turnover by selling its storage capacity to cloud users, so he has the motive to recover sold storage by deleting rarely or not at all accessed data, and even hides data loss accidents to maintain a reputation. Here, we assume the CSP


59

is semi-trusted, namely, the CSP behaves appropriately as prescribed contract most of the time, but he may try to pass the truthfulness check without possessing correct data. On the other hand, the owner also has the motive to wrongly accuse an honest CSP, e.g., a malicious owner intentionally claims data corruption despite the fact to the contrary so that he can get a compensation from the CSP. Therefore, disputes between the two parties are inescapable to a certain degree. So an arbitrator for dispute decision is indispensable for a fair auditing scheme. We extend the threat model in accessible public schemes by differentiating the auditor (TPAU) and the arbitrator (TPAR) and putting dissimilar trust assumptions on them. Because the TPAU is mainly a delegated party to check client’s data integrity, and the potential dispute may occur between the TPAU and the CSP, so the arbitrator should be an impartial third party who is different to the TPAU. As for the TPAR, we consider it honest-but-curious. It will behave truthfully most of the time but it is also inquisitive about the content of the auditing data, thus the privacy protection of the auditing data should be measured.

Figure 1: System Architecture

As illustrated in Fig. 1, the system model involves five

different entities:

1. The data owner, who has a huge amount of data to be stored

in the cloud, and can dynamically update his data (e.g., add,

remove or adjust a data block) in the future.

2. The cloud service provider (CSP), who has immense

storage space and computing power that users do not hold,

stores and manages user’s data and related metadata.

3. The third party auditor (TPAU) is a public verifier with

information and capabilities for auditing, and is trusted and

payer by the data owner (but not necessarily trusted by the

CSP) to validate the integrity of the owner’s remotely stored

data.

4. The third party arbitrator (TPAR), is an entity for

impending conflict arbitration and trusted by both the owner

and the CSP, and is different to the role of TPAU.

5. Users, who can seek and download multiple files at a time

from the cloud with the owner’s permission in secure fashion.

Only the files activated by the TPAR can be available to

download. Data Owner rely on the CSP for data storage and

protection, and they may access and renew their data. To

assuage their burden, cloud users can delegate auditing tasks

to the TPAU, who periodically performs the auditing and

truthfully reports the result to users. For potential disputes

between the data owner, auditor and the CSP, the TPAR can

fairly settle the disputes on proof verification or data update.

IV. EXPERMENTAL RESULTS

The volume of the blocks of the data file is equal. For

example, if the volume of the test data is 9 KB, then it is

divided into 3 block volume of fragmentation each of volume

3 KB.. I calculate the performance of the auditing scheme

from three aspects: tag/token generation time, proof invention

time and proof confirmation time. For data dynamic update

and dispute arbitration, we test the update in the clouds by

adding, removing and renewing some blocks.

Figure 2: Cost of Blocks

For data dynamics, I experiment the in the clouds of adding,

removing and renewing 1 block and corresponding tag, as

illustrated in Fig 2. The graph in Fig 3 shows how the future

scheme search algorithm searches many files in the same time

when compared to presented schemes where one file can be

searched at a time. Since it takes fewer time to search and

hence its performance is better.

Figure 3: Search File Performance

V. CONCLUSION

In this paper we revise the need of a fair and dynamic auditing

scheme to avert a lying client reproving an honest CSP. But

their scheme only realizes private auditing, and is hard to be


60

extended to support public auditing. Compared to these

schemes, here is we join public verifiability, data dynamics

support and dispute arbitration simultaneously. The system is

extensive by implementing the data dynamics andfair

arbitration on groups in future.

REFERENCES

[1] Y. Deswarte, J.-J.Quisquater, and A. Sa ıdane, “Remote integrity checking,” in Proc. 5th Working Conf. Integrity and Intl Control in Information Systems, 2004, pp. 1–11.

[2] D.L. GazzoniFilho and P.S.L. M. Barreto, “Demonstrating data possession and uncheatable data transfer.” IACR Cryptology ePrint Archive, Report 2006/150, 2006.

[3] A. Juels and B. S. KaliskiJr, “Pors: Proofs of retrievability for large files,”in Proc. 14th ACM Conf. Computer and Comm. Security (CCS07), 2007, pp. 584–597.

[4] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L.Kissner, Z. Peterson, and D. Song, “Provable data possession at untrusted stores,” in Proc. 14th ACM Conf. Computer and Comm. Security (CCS07), 2007, pp. 598–609.

[5] H.Shacham and B. Waters, “Compact proofs of retrievability,” in Proc. 14th Intl Conf. Theory and Application of Cryptology and Information Security: Advances in Cryptology (ASIACRYPT 08), 2008, pp. 90–107.

[6] Q. Wang, C. Wang, J. Li, K. Ren, and W. Lou, “Enabling public verifiability and data dynamics for storage security in cloud computing,” in Proc. 14th European Conf. Research in Computer Security (ESORICS 08), 2009, pp. 355–370.

[7] M. A. Shah, R. Swaminathan, and M. Baker, “Privacypreserving audit and extraction of digital contents.” IACR Cryptology ePrint Archive, Report 2008/186, 2008.

[8] C. Wang, K. Ren, W. Lou, and J. Li, “Toward publicly auditable securecloud data storage services,” Network, IEEE, vol. 24, no. 4, pp. 19–24, 2010.

[9] C. Erway, A. Küpc ¨ u, C. Papamanthou, and R.Tamassia, “Dynamic provable data possession,” in Proc. 16th ACM Conf. Computer and Comm. Security (CCS 09), 2009, pp. 213–222.

[10] Y. Zhu, H.Wang, Z. Hu, G.-J.Ahn, H. Hu, and S. S. Yau, “Dynamic audit services for integrity verification of outsourced storages in clouds,” in Proc. ACM Symp. Applied Computing (SAC 11), 2011, pp. 1550–1557.

[11] Q. Zheng and S. Xu, “Fair and dynamic proofs of retrievability,” in Proc. 1st ACM Conf. Data and Application Security and Privacy (CODASPY 11), 2011, pp. 237–248.

[12] A. Küpc ¨ u, “Official arbitration with secure cloud storageapplication,” The Computer Journal, pp. 138–169,2013.

[13] N. Asokan, V. Shoup, and M. Waidner, “Optimistic fair exchange ofdigital signatures,” in Proc. 17th Intl Conf. Theory and Applications ofCryptographic Techniques:Advances in Cryptology (EUROCRYPT98), 1998,pp.591–606.

[14] C.Wang, Q.Wang, K. Ren, andW. Lou, “Privacypreserving publicauditing for data storage security in cloud computing,” in Proc. IEEEINFOCOM, 2010, pp. 1–9.

[15] C. Wang, S. S. Chow, Q. Wang, K. Ren, and W. Lou,“Privacy preserving public auditing for secure cloud storage,” IEEE Trans. Computers, vol. 62, no. 2, pp. 362–375, 2013.

[16] B. Wang, B. Li, and H. Li, “Oruta: Privacy-preserving public auditing for shared data in the cloud,” IEEE Trans. Cloud Computing, vol. 2, no. 1, pp. 43–56, 2014.

[17] “Proofs of retrievability with public verifiability and constant communication cost in cloud”, J.Yuan and S.Yu,International workshop on security in cloud computing, may 2013 .

[18] “Provable Multicopy Dynamic Data Possession in Cloud Computing Systems”,Ayad F. Barsoum and M. Anwar Hasan, IEEE Transactions On Information Forensics And Security, march 2015.


61

A Survey on Big Data Storage Issues in Cloud

Computing Environment Ashima Arya

1 Research Scholar, Department of Computer Engineering Chitkara University,Punjab.

Jagpreet Sidhu2 ,Associate ProfessorDepartment of Computer Engineering Chitkara University,Punjab.

Abstract- Big data is the collection of large set of data

which holds many intelligence and raw information. In the

digital world, handling and updating large volume of data

in the real time environment is a challenging task. This

paper presents a compressive discussion on analytical

techniques and processing methods for analysing the

applications of big data with the purpose of constructing

valuable information in cloud computing environment.

This research aims to evaluate the existing research and

discusses on security issues associated with big data in

cloud computing.

Keywords--Big data, Cloud Computing, Analytics,

Security.

1. INTRODUCTION

Big Data

The term big data is defined as “a new generation of technologies, architectures and frameworks that are designed to economically separate valuable information from very large variety of data, by enabling high velocity capture, discovery, extraction and analysis” [1]. In the digital era, large amount of data have been generated from various sources such as social network, internet of things, scientific experiments, healthcare applications etc. at a rapid speed.

According to the renowned IT companies, the total amount of data in the world has increased nine times within five years [2]

.The big data is explained by 3Vs as Volume, Velocity, Variety .The term volume refers to the size of the data, velocity refers to the speed of incoming and outgoing data, and variety describes the sources and types of data .To define the big data efficiently more factors such as veracity, value, variability, validity and vagueness are added to some complementary explanation of data.

Traditional methods of collecting, storing, and analyzing data have become insufficient in managing the rapidly growing volume of data. To handle the big data, there is need to design efficient frameworks to process large amount of data arriving at very high speed from various sources. The big data offers both an opportunity as well as a challenge to researchers to deal with huge data [3].The motive for big data implementation is to store data, retrieve the valuable information and constructing features from raw data.

Cloud Computing

Cloud computing is a type of computing and it is used for the delivery of hosted services over the Internet. In other words, Cloud computing relies on sharing computing resources and hardware’s rather than having personal devices or local servers to manage the real time applications .

Components of cloud computing are offered by Cloud Providers that include Infrastructure as a Service (IaaS), Software as a Service (SaaS) or Platform as a Service (PaaS).

Cloud computing is defined by five attributes as Multitenancy ,Massive Scalability, Elasticity, Pay as You go and Self-Provisioning of resources. Nowadays, due to the present availability of low-cost computers, high-capacity networks and storage devices as well as the widespread adoption of hardware virtualization, service-oriented architecture, and autonomic and utility computing have led to a growth in cloud computing. [4].

Big Data and Cloud

Big Data requires large volume of storage space. But the cost of storage space continues to turn down; the businesses posing financial difficulties for the resources are required to influence big data. Storage using cloud computing is a feasible alternative for small to medium sized businesses considering the use of Big Data analytic techniques. [5]

II. SECURITY ISSUES ASSOCIATED WITH BIG

DATA IN CLOUD COMPUTING

1) To protect and prevent huge size of confidentialbusiness, government, or regulatory data from malicious intruders and advanced threats.

2) Lack of awareness and standards about how cloudservice providers securely maintaining the huge disk space and erase existing big data.

3) Lack of standards about auditing and reporting ofbig data in public cloud.

4) The fact that sensitive cloud resources can beaccessed from anywhere on the Internet therefore strong authentication and authorization becomes a critical concern. Different Encryption/Decryption techniques and authentication methods such as administrative rights


62

for nodes are implemented to provide authenticity to the user as well as data.

III. RESEARCH CHALLENGES

Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness.

• Networks have increased and require even greaterlevels of security than ever before. Traditional security measures include firewalls and intrusion detection systems were designed to protect facilities and equipment with defined parameters that have with minimal entry points into the networks and devices.

• Cloud-based environments have multiple datacenters, spread across multiple vendors that are managing different categories of datasets which need to be available to different users with different access rights. As a result, firewalls and IDS tools are configured to permit traffic to remote users’ tablets, mobile devices and laptops from anywhere they are. This would reduce effectiveness of the legacy security tools.

• In order to achieve integrity, confidentiality andavailability of these diverse systems and datasets, organizations need to change their security mechanisms from these legacy perimeter and detection-based tools to a focus on implementing efficient protection at the data levels and application levels.

IV. REVIEW

Objective of literature review is to identify the existing security mechanism to handle the security issues and to find the efficient solution to maintain the integrity of data and privacy of user’s personal information from intruder. [9-20].

Table1: Literature Review

Year Author/s Title

2017 Mehdi Sookhak, Abdullah

Gani, Muhammad

Khuram Khan,Rajkumar

Burya

Dynamic remote data

auditing for securing big

data storage in cloud

computing

2017 Deepak Puthal, Surya

Nepal, Rajiv Ranjhan,

Jinjun Chen

A dynamic prime number

based efficient security

mechanism for big sensing

data stream.

2016 Yoon-Su Jeong,Seung-Soo

Shin

An efficient authentication

scheme to protect user

privacy in seamless big

data services.

2016 Yong Yu,Liang lue,Man

Ho Au,Willy usilo

Cloud data integrity

checking with an identity

based auditing mechanism

from RSA

2016 Zheng Yan,Wenxiu

Ding,Xixun Yu,Haiqi Zhu

,and Robert H.Deng

Deduplication on

encrypted big data in cloud

2016 Yibin Li,Keke Gai,

Longfei Qiu, Hui Zhao

Intelligent cryptography

approach for secure

distributed big data storage

in cloud computing

2016 Zhiwei Wang,Cheng

Cao,Nianhua Yang, Victor

Chang

ABE with improved

auxiliary input for big data

security

2016 Muhammad Uaman , Mian

Ahmad Jan,Xiangjjan He

Cryptography-Based

secure data storage and

sharing using HEVC and

public clouds

2016 Yinghui Zhang ,Xiaofeng

Chen,Jin Li,Duncan S.

Wong,Hui Li,IIsun You

Ensuring attribute privacy

protection and fast

decryption for outsourced

data security in mobile

cloud computing

2016 Wei Song, Bing Wang

,Qian Wang, Zhiyong

Peng,Wenjing Lou, Yihui

Cui

A privacy preserved full

text retrieval algorithm

over encrypted data for

cloud storage applications

2016 Uthayanath Suthakar ,Luca

Magnoni, David Ryan

Smith, Akram Khan, Julia

Andreeva

An efficient strategy for

the collection and storage

of large volumes of data

for computation

2015 Gang Chen,Sai Wu,Yuan

Wang

The evolvement of Big

data systems:from the

perspective of an

information security


63

application

2015 Jun Sho ,Rongxing

Lu,Xiaodong Lin,Kaital

Liang

Secure bidirectional proxy

re-encryption for

cryptographic cloud

storage

2014 Chingfang Hsu,Bing Zeng

,Maoyuan Zhang

A novel group key transfer

for big data security

V. TOOLS and TECHNOLOGY

Large amounts of data generated from various sources are not organized and straightforward. This section examines various important processing technologies and methods to handle big data in practice [23].Description of different tools that are used to handle large amount of data is described in table 2.

Table 2 : Batch Processing Tools

Batch Based

Processing Tools

Description

Hadoop It performs the data-intensive application

processing. It uses a Map/Reduce

programming Model.

Skytree Server It processes large amount of data at a very

high speed. The main focus of Skytree

Server is real-time data analytics.

Talend open Studio It provides a graphical environment to

conduct an analysis for big data

applications.

Jaspersoft It provides fast data visualization on

renowned storage platforms such as

including Mongo DB, Couch DB,

Cassandra, Riak, Redis, and Hadoop.

Dryad To improve the capability of processing

from a small to a large number of nodes for

parallel and distributed programs.

Table 3: Stream Processing Tools

Stream Based

Processing Tools

Description

Storm To perform real-time processing of large

amounts of data

Splunk Generates reports that capture indexes

and correlates with real time data.

S4 Data stream processed efficiently.

SAP Hana Analysis of business processes in real

time

SQL stream s-

server

To analyze the data of services and log

files data in real-time processing

VI. ANALYTICAL TECHNIQUES

Analytics will play an important role in making sense of big data in real world. This will, in turn, aid the mining of heterogeneous data sets for revealing hidden knowledge, patterns, and relationships. Big data requires development in algorithm and architectures to cope with the associated challenges of high dimensionality, velocity, and variety [24].Big data techniques are required to efficiently analyze large amounts of data within a limited time period. Currently, few techniques are applicable to be applied on analysis purposes given in Table 4.

Table 4: Analytical Techniques

Analytical Techniques Description

Machine learning To evolve behaviors based on empirical

data.

Data mining Extracting useful information or

knowledge from the structured/

unstructured data and databases.

Social network To view social relationships in terms of

network theory

Web mining To discover a pattern from large web

repositories

Optimization methods To solve quantifiable problems.

Associative rule learning To discover relations between variables

in large databases.

VII. CONCLUSION

Big Data requires large volume of storage space. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. From the existing research studied, we observed that big data storage in cloud computing environment needs to be improved. This research work is mainly focused on security issued associated with big data and find out the existing solutions to improve the security mechanism. Deduplication of data will be done to provide data availability to reduce the storage space.


64

REFERENCES

[1] P.Jain, M.Gyanchandani and N.Khare, Big data privacy:a technological perspective and review. Journal of BigData .2016, pp. 3-25.

[2] I.Yadooq, I.A.Hashem, A.Gani, Big data: Frombeginning to future: ELSEVIER,2016, InternationalJournal of Information Management,pp.1231–1247.

[3] A.Ali, J.Qadir, R.U.Rasool, A.sathaiseelan, A.Zwitter,Big data for development: applications and techniques:Big Data Analatyics,2016,pp.1-24.

[4] S.Sahar, D Ibrahim, A Review on Cloud Computing andInternet of Things: International Journal of Computer,Electrical, Automation, Control and InformationEngineering Vol:11, No:4, 2017

[5] G. Skourletopoulos , C. Mavromoustakis,, G. Mastorakis,

et.al, Big Data and Cloud Computing: A Survey of

the State-of-the-Art and Research Challenges

:SPRINGER 2017, Advances in Mobile Cloud

Computing and Big Data in the 5G Era. pp 23-41.

[6] G.Somani ,M.S.Gaur,D.Sanghi et.al, DDoS attacks in

cloud computing : Issues,taxonomy, and future

directions:ELSEVIER,2017,ComputeCommunications, pp 30- 48.

[7] I.Lee, Big data: dimensions , evolution impacts and

challenges: ELSEVIER 2017 , Business Horizons.

[8] D.Broeders,E.Schrijvers,B.Sloot,et.al, Big data and

security policies :towards a framework for regulating

the phases of analytics and use of big data:

ELSEVIER 2017 ,Computer Law and security review.

[9] U.Suthakar, L.Magnoni, D.Ryan ,A.Khan,et.al, An

efficient strategy for the collection of large volume of

data for computation: Springer 2016,Journal of Big

Data pp 3-21.

[10] Y.Li,K.Gai,L.Qiu,H.Zhao,Intelligent cryptography

approach for secure distributed big data storage in

cloud computing: ELSEVIER 2016,Information

Sciences.pp 1-13.

[11] M.Sookhak,M.khuram,A.Gani,R.Burya, Dyanamic

remote data auditing for securing big data storage in

cloud computing :ELSEVIER 2017,Information

Sciences.

[12] Y.Yu,L.Xue,M.Au,W.Susilo,et.al, Cloud data integrity

checking with an identity based auditing mechanism

from RSA: Future Generation Computer Systems 2016.

[13] M.Usman, M.Ahmad ,X.He ,Cryptography-based secure

data storage and sharing using HEVC and public

clouds : ELSEVIER 2016,Information Sciences.

[14] W.Song ,B.Wang, Q.Wang,et.al,A privacy preserved

full-text retrieval algorithm over encrypted data for

cloud storage applications: Journal of Parallel and

Distributed Computing 2016.

[15] Z.Wang, C.Cao, N. Yang , et.al, ABE with improved

auxiliary input for big data security

ELSEVIER 2016, Journal of Computer and System

Sciences.

[16] Y.Zhang, X.Chen, J.Li,et.al, Ensuring attribute

privacy protection and fast decryption for outsourced

data security data security in mobile cloud computing:

ELSEVIER 2016 , Information Sciences. pp 17-37.

[17] Z.Yan, W.Ding.X.Yu,H.Zhu ,et,al, Deduplication on

encrypted data in cloud: IEEE Transactions on Big

Data.2016.

[18] C.Hsu,B.Zeng,M.Zhang, et,al, A Novel group key

transfer for big data security :ELSEVIER

2014,Applied Mathematics and Computation pp

436-443.

[19] D.Puthal,S.Nepal,R.Ranjhan,et.al,A dynamic prime

number based efficient security mechanism for big

sensing data stream: ELSEVIER 2017, Journal of

Computer and System Sciences.

[20] G.Chen, S.Wu,Y.Wang, The evolvemet of big datasystem from the perspective of an information

security application: ELSEVIER 2015,Big DataResearch.

[21] J.Sho,R.Lu,X.Lin,et.al,Secure bidirectional proxy re-

encryption for cryptographic cloud storage:

Pervasive and Mobile Computing 2015.

[22] Y.Jeong, S.Shin, An efficient authentication scheme to

protect user privacy in seamless big data services:

SPRINGER 2015,Wireless Press Commun.

[23] C.Tsai, C.Lai, H.Chao, et.al big data analytics: a

survey: SPRINGER 2015, Journal

of Big Data, pp 2-21.

[24] A.Gandomi, M.Haider, Beyond the hype :big data

concepts ,methods, and analytics: ELSEVIER 2015,

International Journal of Information Management pp

137-144.


65

Constraints and Limitations in Software

Reliability Prediction1Dr. Archana Kumar2Abhinav Juneja,3Sapna Bajaj

1Director,DITM, Gannaur

2,3Ph.D. Scholar,AFSET,Faridabad

Abstract-Software as entered into our lives in all

aspects. We are now so dependent on technology that it

has become very critical for our lives. All the modern

technological development, be it in any field

incorporates the usage of software directly or indirectly.

Due to this the need for reliable softwares is also gaining

importance. A software in deeply tested before being

brought into its professional application but still it is

very difficult to guarantee its reliability and own that its

is 100 percent free from bugs and will not lead to

failures. In this paper we have discussed various aspects

of software development which are very critical for its

reliability and the various limitations associated with

the true prediction of the degree to reliability of the

software. There is always a possibility that the Software

may have latent bugs even after passing all levels of test.

There has been a lot of research in this area and

statisticians have given a number of Software

Reliability Growth Models for reliability prediction,

still there are issues while choosing the appropriate

model and making a compliance with the standard

assumptions of the selected models. Due to this

reliability prediction accuracy is still a complex area.

Index Terms Calender Time, Error, Failure

data, Failure Rate, Hardware Reliability,

Software Reliability.

1. INTRODUCTIONComputers are bringing revolutionary changes to

our life with their involvement in most human-made

systems through sensing, communication, control,

guidance and decision-making. When the requirements for and dependencies on computers increase, the crises of computer failures also

increases. The impact of hardware and software

failures range from issues like malfunctions of home appliances, economic loss like compromise of banking systems to life-critical like failures of flight

systems and medical software. As the applicability of computer operations becomes more essential and complicated in the modem society, the need for reliability of computer software becomes more

important and critical. In fact, computer software had

already become the major source of reported outages in many systems. This trend has been stressed by the

fact that hardware components of a system become

increasingly reliable, and software starts to dominate

the cause of computer system failures and outages.

With the increase in demand of Software driven

application gadgets, its size, complexity, and criticality also increases. Today, the growth in

utilization and dependency on software components

is largely responsible for the high overall complexity of many system designs, since it is the integrating potential of software that has allowed designers to contemplate more ambitious systems encompassing a broader and more multidisciplinary scope[1,3,4].

The intention of this paper is to describe the Software Reliability Engineering, its various implementation techniques and the limitations in prediction of reliability associated with any so called tested software. There is a lot of gap between the reliability that we can predict for our software under testing and the actual reliability encountered when the software is actually tested and operated in the actual user environment. The paper unfolds certain aspects that limit the analysts in accurate prediction of inherent faults in the software. We start our discussion with a brief distinction between software and hardware reliability. After that we appreciate the underlying differences between fault and failure associated with a software. In the proceeding sections we discuss the reliability incorporated at different stages of software development process , overview of different software reliability models based on their approach to make software reliable and then we finally discuss the underlying unreliability which still limits and constraints the accurate prediction of remaining and latent errors in the Software that may or may not lead to its failure at a later stage.

Software versus Hardware Reliability

There are some foundational differences between hardware and software failures that impact the analysis between Hardware versus Software reliability. The primary difference between the two is in the underlying mechanisms causing failures. With hardware, failures are often initiated by physical processes related to stresses imposed by the operating environment. Specifically, failures are due to ageing or the components degrading with time lapse, deteriorating, or being subjected to environmental


66

shocks. Contrastingly in Software, there is nothing to “wear out”. The major stimulus mechanism for failures are the remaining faults within the software which have not either been expected or unfolded. These faults may be the result of errors in coding or implementation of design or requirements specifications. However, just the presence of a fault is not enough to cause a failure. First the software must be executing, and second the input being processed during execution must be such that the fault will be encountered under just the right set of conditions that result in a failure[1]. Reliability isgenerally a key driver in many system performance criteria, e.g., mission reliability and life-cycle cost[2]. Similarly, reliability and quality analysis within each stage of the design process can greatly reduce the life-cycle cost of a product.

Failure in contrast with Fault

Maintaining a clear demarcation between failures and faults is very critical while applying Software Reliability. Since Software Reliability is integrally associated with Software Failures, any discussion of software reliability must start with a definition of Software Failure [1]. A formal definition of a Software Failure is a deviation of the operation of a software system from its requirement specification. While using the concept of Software Reliability, The notion of requirement specification is not limited to paper agreements done while preparing the Software Requirement Specification rather may be extended to cover anything relating to the customer’s satisfaction with the product to be more precise it should be a validation by the customer and not mere verification. Examples of software failures might be simply the absence of part of the output report or some format even, Some software failure might result in the system being completely inoperable but recoverable by reinitializing the system software and any such condition not desired by the customer may be associated with software failures.

Conversely, a fault is a defect that has not been discovered in the software during its different phases of development. Examples of a fault might simply be an uninitialized variable ,an incorrectly coded program statement, an incorrect implementation of a design or requirement specification. Software failures are an external manifestation of the presence of a fault. However, there is not necessarily a one-to-one correspondence between fault and failure. A fault may stimulate many failures if the fault is not neutralized after the first failure is encountered. Also, different faults may cause failures to occur at different rates. On the other hand, a fault may stimulate no failure at all This would be the situation if the customer uses the software product in a way that the fault is never encountered under the right conditions to cause a failure. Customers are not

concerned per se with how many faults there are in a software product. They are concerned with how often the software will fail for their intended use and how costly each failure will be to them.

II.INCORPORATING RELIABILITY IN

SOFTWARE

The software product life cycle into four phases: product definition, product design and implementation, product validation, and product operation and maintenance [1].

I. Definition of Product

Proper product definition is essential for having a successful product on the market. The primary output of the definition phase is a requirement specification for the product. Reliability objectives should be included as an explicit part of the requirements specification. The first step in setting reliability objectives is to define what a failure is from the customer’s perspective. Next, failures should be categorized by the impact they have on customers. A key step is understanding customers tolerance to failures of different categories and customers willingness to pay for reduced failure rates in each failure category. The information developed in each of these steps can then be used to develop reliability objectives for the product. After such objectives are established for the product as a whole, they must be allocated among the hardware and software components within the product. In effect, a reliability budget is being established for each of the components within the product. Once reliability objectives are established for software components, the following two important items are needed to proceed.

1.An Operational Profile that determines how theCustomer will use the Product (field of use for the product being developed).

2.Estimates Relating Calendar Time or Execution

Time(CPU time).

2.. Design And Implementation Of Product

The primary purpose of the design and implementation phases is to turn a requirement specification for a product into a design specification, and then to implement the design into the product. One activity during the design phase is allocating and budgeting software reliability objectives among software components. An analysis must be conducted to ascertain whether the reliability budget can be attained within the proposed design. Another important activity should be to certify the reliability of “reused’’ software (not only application software but also system software such as operating system software and communication interface software). There is a strong push to reuse software developed


67

for one product within another product. However, the reliability of the reused software may not be sufficient to satisfy the needs of the new product. Before reusing software in a new product, its reliability should be established through reliability testing under the operating conditions intended for the new product. It is important to use the operational profile for the new product in reliability testing. These are many verification activities that should also be going on during the implementation phase. Verification activities such as inspections, unit testing, and integration testing are intended to “verify” that what was actually produced within a development stage is the same as what was intended to be produced. Verification activities, such as inspections, can be applied to such development stages as specifying requirements, design, and coding (unit implementation).The intention of verification activities is to minimize the propagation of the number of faults introduced in one development stage to another stage. At this time, there is little that can be done to estimate software reliability using measures from such product verification.

III. Product Validation Phase

The primary thrust of validation activities is to certify the product is suitable for customer use. Product validation is associated with evaluating a software product at the end of development to ensure it complies with the initial requirements for the product. Product validation activities for software products generally include system test and field trial activities. Software reliability measurements are particularly useful in this phase in conjunction with reliability testing to monitor the progress of testing and to help in making product release decisions. The sequence of activities during testing typically proceeds as follows. Failures and the corresponding execution time (from the start of testing) are recorded

during testing, Statistical techniques are used to estimate the parameters of software reliability execution time model components based on the

recorded failure data.

IV. Product Operations and Maintenance

The primary thrust of the operations and maintenance phases is to transfer the product into the

customers day-today operations, to support the customer in the use of the product, and to repair or

fix faults within the software that are impacting the customers use of the product. The reliability of the

currently operating software should be great enough so that introducing the new software release will not

reduce the reliability below a “tolerable” level for the end user of the software product. Failure data

collected in the customer’s operating environment can be used to verify the customer’s perceived level

of reliability to the measured reliability of the

product.

IV.SOFTWARE RELIABILITY ASSESSMENT

MODELS

Nearly all existing models of software reliability

may be categorized into four basic types[6]:

a)Failure Count Category: These models take into

account number of faults or failures uncovered in

specific intervals of time. Keys assumptions of these

models here are that the testing intervals are independent to each other, faults uncovered during

non overlapping time interval are independent of

each other[6].Typical examples of these models are

Shooman’s exponential model, GO-NHPP model.

b)Time between failure Category: These modelsprovide estimate of the times between failures. Major assumptions of these models include independent times between failures, equal probability of exposure for each fault, no new faults injected during correction. Typical examples of such models are Goel and Okumoto’s imperfect debugging model.

c)Fault Seeding Category: This type includesmodels to predict number of faults in the program at initial time via seeding of external faults. Mojor assumptions in these models include that seeded faults are distributed randomly in the program and seeded faults have equal probability of being detected. Typical example of this category is Mill’s seeding model.

d)Input Domain based category: These modelspredict the reliability of a program when the test cases are sampled randomly from well known operational distribution of input program. Major assumptions here are that input profile distribution is known, random testing is used, we may partition the

input domain into equivalence classes. Typical example of this category is Nelson’s model.

Constraints and Limitations in Accurate Software

Reliability Prediction

Although Software reliability is accepted as a key attribute in software quality, and is defined as the probability of failure free software operation for a

specified period of time in a specified environment

.The residual faults in the software system directly contribute to the failure rate, causing software unreliability. The problem of measuring software

unreliability can be approached by obtaining the

estimates of the residual number of faults in the software. Assessing software reliability in a testing phase of a software development process is one of the

important issues to develop a highly reliable software system[7]. The number of faults that remain in the


68

code is also an important measure for the software

developer, from the point of view of planning

maintenance activities

[3].Most of the reliability models which have

been designed so far have been designed keeping in

view the assumptions that

1.A software fault is fixed immediately upon

detection, and no new faults are introduced during the debugging process. This assumption of instantaneous

and perfect debugging is impractical, and should be

amended in order to present more realistic testing

scenarios.

2.The time lag between the detection and

debugging of a fault is not explicitly accounted for in the traditional software reliability models, as it complicates the failure process significantly, making it impossible to obtain closed-form expressions for various metrics of interest. However, the estimates of the residual number of faults in the software is influenced not only by the detection process, but also by the time required to debug the detected faults.

3.Debugging process affects the number of faultsremaining in the software and consequently its reliability, and makes a direct impact on the quality of a software product.

4.The other stringent assumption is that of perfectdebugging. Studies have shown that most of the faults encountered by customers are the ones that are reintroduced during debugging of the faults detected during testing. Thus imperfect debugging also affects the residual number of faults in the software, and can at times be a major cause of its unreliability, and hence customer dissatisfaction.

5.Conventional Software reliability models cannot account for this difference between the detected and debugged faults[2], simulation offers a

powerful, yet simple alternative to take this

difference into consideration.

There is a need to design such models which keep into account the reliability deterioration due to

debugging process.

V.CONCLUSION

Software Reliability prediction is a very complex process and it needs the selection of an applicable

Software Reliability Growth Model(SRGM) to give a

reliable prediction. An SRGM may suit one particular Software Development but may fail in the other.The standard assumptions taken into account by SRGM’s

are also not validated in actual run time environment. There is no thumb rule to quantify a particular model

that may suit well for all softwares under

development.

REFERENCES

[1] William W . Everlett “Software Reliability Measurement”. IEEE transaction on selected areas in communication Vol.8,No.2, Feb 1990.

[2] Stephen W. Ormon, C. Richard Cassady, and Allen G.Greenwood” Reliability Prediction Models to Support Conceptual Design”, IEEE Transcactions on Reliability,VOL. 51, NO. 2, JUNE 2002

[3] Swapna.S.Gokhale , Michael R. Lyu , Kishore S. Trivedi“Software reliability analysis incorporating fault detection and debugging activities” IEEE.

[4] Wen-Li-Wang, Mei–Hwa Chen”Heterogeneous Softwarereliability Modelling”,proceedings of the 13th international symposium on software reliability engineering (ISSRE’02).

[5] John D.Musa “Introduction to Software Reliability Engineering and Testing” article in IEEE,1997.

[6] Zuzunz Krajcuskova,”Software Reliability models”, Deptt. Of Radio Electronics, Slovak University of Technology,SlovakRepublic,IEEE 2007.

[7] Shinji Inoue, Member, IEEE, and Shigeru Yamada, Member, IEEE,”Generalized Discrete Software Reliability Modeling With Effect of Program Size” IEEE Transactions on Sysbernetics-Part-A:Systems and Humans,Vol. 37, NO. 2, MARCH 2007.

[8] Google.com

[9] Amazon.com


69

A REVIEW ON THE INTELLIGENT SCHEMES FOR

AUTOMATIC GENERATION CONTROL IN MODERN POWER

SYSTEM

SALONI

Assistant Professor

Dept. of EEE, BMIET, Sonepat

VISHAL JAIN

Associate Professor

Dept. of EE, BMIET, Sonepat

Dr. DEVENDER SAINI

Assistant Professor

Dept. of EE, UPES, Dehradun

Abstract— Automatic generation control (AGC) is

one of the important control problems in the design and

operation of interconnected power system, and is

gaining popularity today due to the growing size,

varying structure, upcoming renewable energy sources

and new uncertainties, and complexity of power

systems. AGC system requires increased intelligence

and flexibility to guarantee balance between load and

generation. The modern AGC systems surely must be

intelligent, and should be capable of handling complex,

multi-objective optimization problems characterized by

diversification in policies, control strategies. The

foundation of such systems should be based on

intelligent algorithms, advanced devises, and rapidly

changing Information technology. This paper presents a

review of the intelligent algorithms used in the modern

AGC by the researchers.

Keywords--Automatic generation control (AGC),

Area control error (ACE)

I. INTRODUCTION

Operations in actual power system is dynamic where, the load is continuously changing. Due to the physical and technical constraints the generation is not able to deal with the changing load; as a result there is a disparity between the actual and the scheduled generation which is known as frequency error [1]. Automatic generation control (AGC) is a major control system that maintains stability between the load and generation in power systems at nominal cost [2]. The main task of AGC system is to maintain nominal frequency, power interchange, and economic dispatch.

Intelligent automatic generation control offer a systematic understanding of the fundamentals of power system AGC, and proposes various new schemes using intelligent control methodologies for minimizing the system frequency deviation and tie-line power changes, which is essential for the operation of interconnected power systems [3-4]. The coming sections illustrate various intelligent control strategies for modern AGC.

II. TECHNIQUES USED FOR AGC

A. Fuzzy Logic AGC

Today Fuzzy logic is extensively used in almost all fields of operation and control because of robustness and reliability. The conventional control strategies are generally based on the linearized mathematical models of the systems to be controlled; however the fuzzy control methodology is based on the expert knowledge and experiences of the domain. Vast literature is available for the fuzzy-logic-based AGC design [5].

A lot of fuzzy logic controller structures exist for AGC, depending upon the number and type of inputs outputs, fuzzy sets, membership functions, control rules and type of deffuzification method.

The Fuzzy logic controlled AGC applications are classified into three classes:

(1) Dynamic fuzzy controller known as fuzzylogic controller (FLC),

(2) Fuzzy logic system used for tuning the gainsa proportional integral (PI) (or proportional-integral-derivative [PID]) controller

3) Fuzzy logic used for economic dispatch.

Fig. 1 FLC Structure for AGC

B. Neuro-Fuzzy and Neural-Networks-

Based AGCLearning capabilities of neural networks are used

to tune the fuzzy controllers enabling them to be adaptive. The synthesis of neural networks and fuzzy logic builds a neuro-fuzzy controller, which uses a learning algorithm based on the training samples available in the neural network theory [6]. The


70

combination of ANN and fuzzy logic is presented in several research articles applied for intelligent-based AGC design and the novelty in both designs are utilized in a single hybrid AGC system.

Fig 2: A Hybrid Neuro Fuzzy Controller for AGC

C. Genetic-Algorithm-Based AGCA genetic algorithm (GA) is a bio-inspired

algorithm that is based on natural selection, genetics and evaluates the fitness of individuals. The GAs are a general purpose optimization algorithms which has been extensively employed over the years to solve complex powers. Fig 3 depicts the basic evolution process starting from the random initialization of population, selection, crossover, and mutation. The loop continues till the average fitness function of the whole population improves.

Fig. 3. A Simple GA Flowchart

GAs have been efficiently used to tune the parameters for diverse AGC schemes, e.g., gains of PI, PID, FLC etc.[7]. GA prove to be a suitable optimization technique to tune the membership functions, and rule sets for fuzzy gain scheduling of

frequency controllers of interconnected power systems, thus improving the dynamic performance.

Fig. 4 Use of GA for AGC controllers

D. Hybrid Intelligent Techniques in AGC

In view of latest advances in Artificial Intelligence in control and evolutionary computations, various hybrid intelligent control methodologies have been proposed to address the problem of AGC [8].

Various hybrid techniques such as GA-simulated annealing (SA)-based fuzzy AGC scheme, a particle swarm optimization (PSO) combined with fuzzy technique, the reinforcement learning (RL) approach is used to design AGC system [9-11]. The literature reveals that the performance of hybrid techniques is better as compared with the simple GA techniques and classical methods.

III. CHALLENGES IN MODERN AGCDeregulation and introduction of new uncertainty

and changeability in the power systems add new challenges and economical reforms linked with AGC systems synthesis and analysis. With the growth of the electric industry, huge generation if power is required and a lot of efforts will be needed to effectively handle these distinctive operating and planning characteristics. A key facet will be to make the AGC system robust and utilize the energy resources.

The modern AGC system must be able to handle complex exchanges between control areas, grid, interconnections and changes in the generating capacity and load demand. It is imperative to have an intelligent controller based on advanced computing algorithm to realize optimal and adaptive AGC schemes.

IV. CONCLUSIONThe problem of AGC in power systems can be

simply handled using artificial intelligent techniques by converting them into performance optimization problem. This has led to emerging trends of application in computational intelligence and soft

FUZZY

CONTR

OLLER

AN

N

AGC

PARTICIPATING

UNITS

LO

AD

de

l f

Ar

ea

Co

nt

rol

Er

ro

r


71

computing in the power system AGC. The most important intelligent AGC methodologies based on fuzzy logic, neural network, neuro-fuzzy, genetic algorithm, and hybrid techniques have been discussed in this paper. The challenges for modern power system are listed giving directions for further research in the field of intelligent AGC.

REFERENCES

[1] O. I. Elgerd, “Electric Energy Systems Theory anIntroduction,” 2nded. New Delhi, India: TataMcGraw-Hill, 1983, pp. 299–33. [2] Hadi Sadat,“Power system analysis”, Tata McGraw-Hill, Edition2002.

[3]N.Jaleeli, et.al."Understanding Automatic GenerationControl" IEEE Transactions on Power systems, vol. 7,pp. 1106-1122, August 1992.

[4] D.M. Vinod Kumar, “Intelligent controllers forAutomatic Generation Control”, IEEE, pp. 557-574,1998.

[5] G.A. Chown and R.C.Hartman, “Design andExperience with a Fuzzy Logic Controller forAutomatic Generation Control (AGC)”, IEEETransactions on Power Systems, Vol.13, No.3, pp.965-970, August 1998.

[6] S. Bhongade, H. O. Gupta, and B. Tyagi, “Artificialneural network based automatic generation controlscheme for deregulated electricity market,” in 2010Conference Proceedings IPEC, 2010, pp. 1158–1163.

[7] Y. L. Karnavas, “AGC Tuning of an InterconnectedSystem after Deregulation Using GeneticAlgorithms”,vol. 2005, pp. 218–223, 2005.

[8] H. Bevrani et.al, “Intelligent Automatic GenerationControl: Multi-agent Bayesian Networks Approach”,2010 IEEE International Symposium on IntelligentControl Part of 2010 IEEE Multi-Conference onSystems and Control Yokohama, Japan, September 8-10, 2010.

[9] Hou Guolian, Qin Lina, Zheng Xinyan, and ZhangJianhua, “Application of PSO-based fuzzy PIcontroller in multi-area AGC system afterderegulation,” in 2012 7th IEEE Conference onIndustrial Electronics and Applications (ICIEA),2012, pp. 1417–1422.

[10] K. Wadhwa, J. Raja, and S. K. Gupta, “BF basedintegral controller for AGC of multiarea thermalsystem under deregulated environment,” in 2012 IEEEFifth Power India Conference, 2012, pp. 1–6.

[11] S. Pati, B. K. Sahu, and S. Panda,“Hybrid differentialevolution particle swarm optimisation optimised fuzzyproportional–integral derivative controller forautomatic generation control of interconnected powersystem,” IET Gener. Transm. Distrib., vol. 8, no. 11,pp.1789–1800,Nov.2011.


72

Abstract— Internet of Things is a today’s Technology.

Research on this technology is in early ages. In IOT Ocean, it is a

collection of devices, domains, components, middleware, and

protocols. IOT is such atechnology, which is improving day by

day. IOT is now becoming a part of daily human life. It opens

new challenges and research area for researchers. This paper is

just aneffort to find research possibilities and identifying breaths

and diversity of existing IOT research in various fields. This

paper reviews the architecture of IOT domains, elements,

standards and platforms. Themain purpose of this paper is to

summarize the analysis of recent trends in IOT domain. Here.

This effort is somewhat different other survey papers because

ofitspresent research possibilitieswith research variables of each

domain of IOT.It will be helpful for theresearcher to find new

research areas in IOT.

Protocol Keywords— RFID- Radio Frequency Identification,

IOT- Internet of Things,CoAP- Constrained Application

I. INTRODUCTION

Kevin Ashton first proposed the term Internet of Things in

1999. Internet of Things is such a technology in which data

collected from real life objects and processed intelligently to

make it well informed. The trend of computing is also

moving from static page pages to social networking web and

then ubiquitous computing. IOT enables many of objects on a

global network. IoTpromises to create a global network,

which will support ubiquitous computing. Radio Frequency

Identification (RIFD) and Wireless Sensor technologies

(WSN) will be contributing to fulfilling these challenges. IOT

will empower to connect things with physical entities and

virtual components. Internet of things can be recognized in

three patterns, Middleware, Sensors, and Knowledge. In this

concept, the low layer includes sensors, actuators, cameras

that are collecting the data and pass to the communication

channel. The Middleware is a software layer that receives

the data and processes and then it sends to the IOT repository

by using the internet as a communication medium. Raw data

collected from the communication medium is processed.

Process raw data takes shape of knowledge. [1]

Various researchers have defined in their own ways.

“A dynamic global network infrastructure with self-

configuring capabilities based on standard and interoperable

communication protocols where physical and virtual ‘Things’

have identities, physical attributes, and virtual personalities

and use intelligent interfaces and are seamlessly integrated into

the information network” (Kenenburg,2008) [1]

“A concept: Anytime, anywhere and any media, resulting into

asustained ratio and man around 1:1” (Srivastava 2006). [1]

“Things having identities and virtual personalities operating in

smart spaces using intelligent interfaces to connect and

communicate within social, environmental and user context”

(Networked Enterprises & RFID & Micro & Nano system)”

[1]

In this paper, the survey is focused on finding IOT research

areas and identifying breaths and diversity of existing IOT

research in all fields. This paper is the result of analysis on 200

research papers on IOT. It was publishedbetween 2010-17 and

found the recent trends in IOT and its future perspective.

II. SURVEY OF ELEMENTS OF IOT

This module, we present about Components of IOT. It gives

abrief introduction to various hardware, software, middleware

and service-oriented architecture.

1. Hardware

A. Radio Frequency Identification (RFID)

In IOT, identification of real objectsis of primary

importance. Before IOT, the industry has been used some of

these technologies named Magnetic Ink Character Recognition

(MICR), bar codes, smart cards and magnetic tapes. The

major issues with these technologies are to read from the

reader and put back in original place. Radio Frequency

Identification (RIFD) provide the solutions to overcome the

problem occurring the previous technologies. The RIFD

system consists a radio frequency tag, which holds the

information about things. RIFD tag is attached to the thing to

be identified. Another important part of RFID system is

Reader, which queries the tag using radio frequency waves.

The reader is capable of storing information in the tag and is

capable of altering the stateof the tag. The most important

feature of RIFD technology is non-contact sensors, radio

waves or microwave. RFID tag can be read by a reader from

few cm to few meter far with touching RFID tag which is

attached to a real object. RFID provides small volume, low

cost, low power consumption and high reliable devices. There

are two types of RFID tags: active and passive tags. Active

RFID tags have a small battery with RFID chip. On the other

hand, passive RIFD tags active when they come contact with

radio wave e.g smart cards. RFID technology can be used in

IOT. Real objects with RIFD technology connects the Internet

as a thing in IOT Architecture. The major issue regarding is

Rajeev Kapoor, Jagpreet Singh Sidhu, Dr. Subash Chander

Assistant Professor, Punjabi University Neighbourhood Campus Jaito

Associate Professor, CUIET, Chitkara University, Punjab, India,

Assistant Professor University College Jaito, Faridkot

Internet of Things: A Survey of Architectures

and Recent Research Trends


73

that addressing scheme used for identifying the real object.

There are two approaches commonly used one is Electronic

Product Code (EPC) and another is Ubiquitous Identifier

(Uid).[2]

A.1 Electronic Product Code

Electronic Product Code (EPC) is known as the universal

identifier of real objects. This technique provides a unique

identity to every real object anywhere in the globe. Industries

follow the EPCglobal Tag Data Standard which is a common

structure for identifying all objects. The Structure of

EPCglobal Tag Data Standard is given in the Figure1.

Figure 1 EPC Message Format

This structure contains four parts: Header, EPC manager

number, Object Class, Serial Number.Header: It contains

length, type, structure, version, and generation of EPC

code.EPC Manager Number: Entity is responsible for

maintaining the subsequent partitions. Object class- It

identifies a class of objects. Serial Numbers –Identifies the

instance of the product

EPC identifiers presently support seven identification keys

which are known as GS1 System of Identifiers. This EPC

Code can be used in the RFID tags. The low-cost passive

RFID Tags are specially designed for this purpose.EUPC Code

is written in RFID Tag chip which has Binary Encoding.In the

survey of EPC code with RFID Tag, it provides the industry

standard which provides object visibility. It provides object

location and status of the object in the real time.[3]

A.2 Ubiquitous Identifier (Uid)- Ucode

Ucode is used as another alternative identifier of IOT things. It

is 128 bits ubiquitous code which is used for an identifier for

real life objects. These are assigned to tangible objects of the

real world and are stored on the tag. This tag is known as

ucode tag and used in RIFD Tags, Smart cards. The normal

length is 128 and can be extended by an integral multiple of

128,256,384 and 512. The Unicode has five fields which are

given below

Ucode = Version+Top level code(TLDC) + class code+

second level domain code (SLDC) +Indenfication code. [3]

B. Near Field Communication (NFC)

NFC is an improved version of RIFD Technology, known as

Near Field Communication NFC Devices. These are very

short range communication standard where devices

communicate

each

TABLE I

SOA Architecture of IOT[5]

Layer Name Description

Sensing

Layer

This Layer is responsible for

interaction with existing hardware

RFID, NFC, Sensors, actuators. It

also responsible for gathering

information from these devices. It

means sensing the data from the

Real World

Networking

Layer

This layer is responsible for

providing the basic network

support to things. It provides the

best route for communication in a

Heterogeneous network by using

wired or wireless network.

Service

Layer

This Layer plays the main role in

this architecture. It is responsible

create the service and manages

services. It provides the services

to the user as per their needs.

This Layer includes the key

components

a. Service Discovery

It means a service which

finds objects that can

provide the needed

service and data in the

proper way.

b. Service Composition

It provides that service

which is enabled to

communicate and

interact with all

connected things.

c. Trustworthiness

management

It provides such service

which device trust and

reputation mechanism

which evaluates the

information of two

things.

d. Service API

This component

provides the service to

the user, they can

interact with the system

through API interfaces.

Interface

Layer

This Layer provides interaction

methods of API to the user. it

also provides a method how can

application can interact with

system Interfaces by API

programs.


74

other when touching each other or come near each other.

Each NFC tag contains Unique Identification (UID), which

shows device uniqueness. This technology is using inNFC in

a smartphone for transferring data with each other. Another

type

of NFC tag is a passive one which becomes active when radio

waves are passing through thetag and transfer the data to each

other.

C. Wireless Sensor Network (WSN)

Jennifer Yick [4] explains Wireless Sensor Network is a key

technology in IOT. It is the network of Smart Wireless

Nodes. All smart nodes are connectedwireless gateway. These

nodes contain small sensors with limited processing capacity.It

also contains the limited and in expensesresources than

traditional sensors. They are capable of sense, measure and

collect information from real life environment. After this

process, they transmit data processing unit for further data

processing. [4]

D. SERVICE ORIENTEDARCHITECTURE OF IOT

Li Da aXuexplains IOT architecture consists Four Layers.

which are Sensing layer, Networking Layer, Service Layer and

Interface Layer. The details are given in Table I. [5]

E. MIDDLEWARE

M. A Razzaque explains, a middleware is a software layer

which provides an interface between a user of applications of

IOT and Infrastructure of IOT. It interacts communications

devices, things, and communication network. It provides

common services for applications. [6]

III. SURVEY ON DOMAIN TREE OF IOT

Figure 2 Smart Society

A. Smart Societies

This module, we present the previous research work is the

done by the researchers in the area of Smart Society in IOT

environment and covers the research gaps and requirements to

improve it on differentparameters. A smart society can be

classified according to the application of technology to

different domains. They are given in figure 2

A.1. Smart City

A Smart City issuchacity in which all physical Infrastructure,

transportation, and social infrastructure are creating a whole

environment with ICT. The data are gathered from sensors of

different infrastructure in the city. These data are sent to

therepository on thecentral server and an intelligent decision

can be made on facts are collected from these data. This

whole environment includes smart infrastructure, smart

surveillance, smart electricity and water distribution, smart

services. The sensing is the main key component which is used

to monitor and provide awareness to how to use their resources

and infrastructure in an efficient way. There are some

communication standards namely Dash 7, Zigbee, LTE, 3G,

and NFCwhich are used to implement this concept. [7] The

Author [8] provides a complete vision to theway of using

sensing technologies in smart cities and describe the how to

implemented in water distribution system, Electricity

distribution systems, smart building, and homes. [8]

A.2 Smart Home

The smart home is an intelligent and automated building. It is

equipped with smart objects. All smart objects connect with a

residential gateway. The data from smart objects are transfer to

the server through a residential gateway. This server is a

repository of information and includes algorithms used for

taking intelligent decisions. This technology can monitor the

internal environment and activities of doing in the house. The

author Baoan LI [9] describes other issues regarding family

security, Family medical treatment, family data processing,

family entertainment and family businesses. For the family

security, it includes cameras, smoke detectors, sensors etc.

For Family healthcare, household medical devices are

connected to IOT network and family doctors, hospitals. This

facility is more beneficial for children and elderly peoples

because they can easily monitor health condition by the

doctors and take necessary action when it requires. With

increasing usage of internet, family data is also increasing day

by day. It includes films, audio, video clips etc. this family

data must be stored on repository server which is connected

through IOT technology. The Author [10] Moataz Soliman

describes the system architecture for the smart home. The

author includes some major compo The smart home is an

intelligent and automated building. It is equipped with smart

objects. All smart objects connect with a residential gateway.

The data from smart objects are transfer to the server through a

residential gateway. This server is a repository of information

and includes algorithms used for taking intelligent decisions.

This technology can monitor the internal environment and

activities of doing in the house. The author Moataz

Soliman[11] describes the system architecture for the smart

home. This system architecture includes some major

components like Microcontroller – enable sensors,


75

Microcontroller –enable actuators, Data store, Server, API

layer and web applications for this purpose. [10-15]

A.3 Smart Grid

The author Xi Fang describes [17]; the term smart grid is used

improve version of traditional power Grids. Smart Grid is

known as an intelligent grid. It contains features: digital, two-

way communication, distributed generation, Sensors,

throughput, self-monitoring, self-healing, adaptive and

islanding, pervasive control, Many customer choices. Smart

Grids are different from traditional power grids. The

traditional grids have merely job to generate electricity and

distribute to the customers. The Important feature of SG is

two-way commutation, automatically load handling and

enhance security feature. Today SG has three major systems

from technical point view: Smart Infrastructure, Smart

management system and smart protection systems. The

requirement of adopting Smart power grid is improving

reliability and quality, improving resilience to distribution,

automating maintenance and operation, improve grid security

etc. As per thedefinition of National Institute of Standards and

Technology(NIST), “The Smart Grid is a grid system that

integrates many varieties of digital computing and

communication technologies and services into the power

system infrastructure. It goes beyond smart meters for home

and business as the bidirectional flows of energy and two-way

communication and control capabilities can bring in new

functionalities.” [18] The NIST provides a conceptual model

for a Smart Grid. It divides into seven domains. Each domain

contains one or more actors which include devices, systems,

and programs. Customers, Markets, Service providers,

Operations, Bulk Generations, Transmission, and Distribution

are seven domains of NIST conceptual model of Smart

Grid.[19-25]

A.3 Smart Tourism

The term Smart Tourism starts anew era of the tourism in

Tourism Industry. The aim of Smart Tourism provides

meaningful information to tourist at the right time. It also

provides the mobile connectivity for intelligent and

meaningful information between tourist and tourist service

providers. The basic aim of smart tourism improves the

tourism services. It enhances tourism industry and provides

new job opportunities for tourism Industry. In these days,

tourists are using e-services like online booking of hotel,

plane, cab and destination stations. With using the smart

objects, the compile information store at base server point and

deliver to the tourist like road traffic information, weather

information, accommodation information and route

information to tourism. China and developing countries like

India, Nepal, Bhutan, Singapore are taking interest to develop

thetourism industry. China National Tourism Administration

(CNTA) has officially announced “Beautiful China, 2014-

Year of as Smart Tourism”. It is anas important initiative in

China's Tourism to develop smart Tourism for Tourism

Industry. In western countries, they rarely take interest in

smart tourism. [26-30]

A.4 Smart Industry

The use of IOT in the industry, it introduces arevolution in

industrial practice. This begins the era of industry which is

called Industry 4.0. It is known as fourth industrial revolution.

It starts anew era where physical objects contain embedded

electronics like RIFD Tags, sensors etc. These objects are

connected to the Internet. It creates asmart network of smart

objects and plays the active role in business. Management can

watch and monitor the ongoing production to finish product.

This enables machines and plants adapt behavior according to

thesituation and operating condition. [31,32,33,34]

B. Healthcare

Healthcare using IOT things opens a chapter of research in

healthcare. In this concept, healthcare devices are used as

things and connected to the Internet. From this from children

to elderly can avail benefit for smart healthcare services.

According to M.R Isam [35], he has classified IOT in two

Classification One Services and second is Applications of

IOT. Services include ambient assisted living, Internet of m-

Health, Adverse Drug reactions, community healthcare,

Children health information, Wearable device access,

Semantic medical access, Indirect emergency healthcare,

embedded gateway configuration and embedded context

prediction etc. The second classification is Application of

IOT. He further classified in two classification namely Single

condition and Clustered conditions. The single condition

includes Glucose level sensing, ECG monitoring, Blood

Pressure monitoring, Body temperature monitoring and

oxygen saturation monitoring. In the clustered condition

includes rehabilitation system, Medication management,

Wheelchair management, Imminent healthcare and

TABLE II

Design consideration for Industrial IOT

application

Layer Name Description

Energy How long can an IOT device operate

with the limited power supply?

Latency How much time is a need for message

propagation and processing?

Throughput What is the maximum amount of data

that can be transported through the

network?

Scalability How many devices are supported?

Topology Who must communicate with whom?

Security &

Safety

How secure and safe is theapplication?


76

Smartphone healthcare solutions etc. The author Luca

Catarinucci [36], propose a Smart Hospital System which

monitoring and tracking patients, personnel and biometric

devices. He proposes SMS architecture in three parts Hybrid

Sensor Network, Smart Gateway System and User Interface

for Local and Remote Users. This system provides power

effective remote patient monitoring and immediate handling of

emergencies. [37-48]

IV IOT STANDARDS

A. Infrastructure protocols

6LOWPAN

6LOWPAN is a Network architecture, whichsupports for tiny

devices in IOTenvironment. It is an open Standard defined in

RFC 6228 which is developed by Internet Engineering Task

Force (IETF). It works on 2.4 GHzband with 250 kbps

transfer speed and creates own Mesh Network. The powerful

feature of this technology is tinydevices which are capable

ofcommunicatingwith theouter world. The structure of this

network is mess Network. In this Network, there are two

devices are used. One name is arouter and thesecond one is

thehost. The job of arouter is determining the best route while

thehost is known as end devices. Hosts act as sleepy devices

which are periodically active and check its parent router for

network existence. It consumes low powercommunication for

implementing this technology. When this technology is

sending data on MAC Layer and Physical Layer, it uses the

adaptation layer for this purpose. This technology uses

header compression, fragmentation & assembly, and stateless

autoconfiguration.[49,50,52]

RPL ( IPV6 routing for low power and lossy Network).

The RPL (IPV6 routing for low power and lossy Network) is

default routing protocol for Internet of Things (IOT). It opens

the doors of Internet for Tiny devices and embedded network

devices. It standardized by IETF in 2011 for establishing

acommon standard for interoperable commercial appliances in

growing IOT age. Each IOT device has anidentification

number. IPV6 has become acommon mechanism to provide

the unique id to each smart device. Therefore, RPL is rapidly

considered as thede-facto routing protocol for IoT devices.

RPL uses the Distance vector routing for storing routing

information. It creates a destination-oriented acyclic

graph(DODAG) which one edge node edge router node,

multiple routers node, and ahost node. It supports two-way

traffic flowspattern from root to devices that are known as

Point-to –Multipointcommunication in RPL. The RPL

protocol works on two operationalmodes: Storing and Non-

storing mode. In the storing mode, every node contains a

routing table which provides amapping between all

destinations node. In the non–storing mode, the root is the only

network master node which maintains the routing information,

the root node is responsible for diverting the traffic from

source to destinations. It contains capabilities to loop

detection and DODAG repair and control traffic flows in RPL

technology. [51,52]

IEEE 802.15.4

The IEEE 802.15.4 protocol: Low Rate Wireless Personal

Network (LR –WPANS) is created for thelocaland

metropolitan network. This standard specifies sub-layer for

Medium Access Layer and Physical Layer which provides

connectivity for low power consumption and zero battery

wireless portable devices. In this standard devices are

operative on three frequencies 868–868.6 MHz, 902–928

MHz, and 2400–2483.5 MHz bands. This standard provides

the low cost, low data rate and high throughput of messages.

The main objective of this standard is providing the easiness of

installation, reliable transfer of data, low cost and flexibility to

maintain this standard. It supports two type of network a full-

function device (FFD) and reduced function device. First

fullfunction device (FFD), in which a device serves as

coordinator for PAN. On the other hand, (RFD) is quite simple

in which no device serves as Coordinator. It is proposed for

applications that are simple and not need to send a large

amount of data at atime. This standard operated on two

topologies: Star topology and peer to peer topology. In

astartopology, all devices are communicated with a special

device Coordinator of PAN. This device has anassociation

with theapplication and acts as initiation point or termination

for PAN. The Peer-to-peer topology is a complex network

implementation. It is mesh network which allows multiple

hopes. Any hope can communicate to any hope in the network.

It has also a coordinator device and acts as the first device in

the network. The further network structure is constructed in

peer-to-peer shape.[52]

Bluetooth Low Energy -BLE

BLE is also known as Bluetooth Smart in the communication

industry. This standard is an improved version of Bluetooth

technology and developed by Nokia’s project named “Wibree”

in 2006. It operates on the short range radio frequency which

covers range up 50(160 feet) meters. The key feature of this

technology is low energy consumption between 0.01 mW to

10 mW. Now this improved version is merged in main

Bluetooth with name Bluetooth 4.0. Many Smart Phone

Makers have adopted this technology and are using their Smart

Phone products. This standard can also be used in avehicle to

vehicle communication. It is much better than ZigBee because

it is more efficient in thematter of low energy consumption

and transmission bit ratio. The key difference from

theprevious version of Bluetooth technology is low power

consumption, 128-bit AES with counter mode CBC-MAC

level Securityand less latency rate 6ms. In the previous

versions, they consume more battery power for a huge amount

of data between two devices. This technology is not using

streaming and consumes low power battery than previous

versions. The architecture of BLE is divided among the three

section controller, Host, and Application in Figure 3. In the

controller part, the lower part is Physical Layer(PHY) which is

responsible for transmitting and receiving bits from asecond

Bluetooth device. Above the PHY, Link layer is responsible

for providing servicesof medium access layer like

theestablishment of connections, errordetection, and

correction, flow control etc.

Direct Test mode is used for end –product qualification. It

provides the standard and procedures for testing packets. It

operates in two modes: Transmit test mode and Receiving


77

Test Mode.First Transmit mode tests packets which are

generated for transmission. Second Receive Test Mode is

testing received apacket from PHY. It also counts a numberof

received packets are in specific order. Host Controller

Interface (HCI) provides the Interface between controller and

Host. The Logical Link Control and

AdaptationProtocolprovide services fragmentation and

defragmentation of large size packets and

Figure 3 Architecture of BLE Stack

multiplexing for data communication channels. The attribute

protocol is used for efficient data collection from the sensors.

Generic Access Profile (GAP) is used for configuring

thedevice in the different mode scanning or

advertising,initiation, and management of connection. Security

Manager is the three-phase process on the connection: pairing

feature exchange, short term key generation, and transport

specific key distribution. It uses a number of cryptographic

function to secure the packets in the communication. It uses

lower memory and power requirement for encryption and

decryption process. As the result, it saves power.

Figure 4 BLE Star-Bus Topology

BLE use the Star –Bus Topology, like Bluetooth

technologyalso a master device thatcontrols the entire network.

Each slave acts piconet in topology. The slaves create anown

physical channel to communicate with the master node. These

all-physical channels of slave node are connected with master

node device and create star network. BLE master has fewer

power constraints for listening advertisement and making

theconnection between master-slave nodes. BLE master uses

these channels for scan slave nodes. After making

theconnection and data transfer between master and slave

node, they are going to sleep mode. [52]

Z- Wave

Z-wave is a low power wireless communication protocol for

smart homes and medium size commercial units. Danish of

Zen-Sys develop this protocol. Later this protocol is acquired

by Sigma Design in 2008. It covers communication range

about 30 meters between two nodes. It provides transmission

speed up to 100kbits for small packets with reliable and low

latency transfer rate. It operates on different frequencies in

different countries like India 865.2 MHz, 868.42 MHz in

Europe. New versions provide support up to 200 Kbits for

small packets.

Figure 5. Z-Wave Protocol Stack

Z-wave protocol includes five layers: Physical Layer (PHY),

Medium Access Layer (MAC), Transport Layer, Network

Layer and Application Layer. The Physical Layer takes

responsibility of modulation and coding of the message. It also

adds a known pattern as preamble with amessage. It also takes

responsibility for assignment of Radio frequency between two

nodes at adatarate of 9.6/40/100 Kps speed.The Z-Wave MAC

layercontrols medium with collision detection and avoidance

algorithms. It supports automatic retransmission of reliable

data transmission and 232 nodes in the one network.The

TransportLayer is responsible for transmission and reception

of frames in the network. It also takes care for retransmission

of frames. It supports four types of frames: Singlecast frame,

ACK Frame, Multicast Frame and Broadcast Frame. There

are controller and slave two types of node in the network. The

Network layer takes the responsibility find thebest route from

one node to another node. The controller sends commands for

managing the slave nodes in the network. It also keeps routing

of thewhole topology. The Application layer is responsible for

controlling payload of receiving and transmitting frames in the

network. It also responsible fordecoding of receiving frames

and execute commands in thenetwork. [52]

LTA –A


78

Long Term Evolution (LTE –A) is an improved version LTE

Standard. It includes cellular communication protocols, which

provide the higher of data transmission.It increased peak data

rate is down link 3 Gbps, Up Link 1.5 Gbps. The Carrier

aggregation is the new functionality introduced in LTE –A. It

is also suitable for IOT infrastructure, especially for smart

cities projects.Long Term Evolution (LTE –A) is an improved

version LTE Standard. It includes cellular communication

protocols, which provide the higher of data transmission. It

increased peak data rate is down link 3 Gbps, Up Link 1.5

Gbps. LTE-A is highest data rate speed and comparison of

data speed is given TableIV

the Carrier aggregation is the new functionality introduced in

LTE –A. It is also suitable for IOT infrastructure, especially

for smart cities projects. Figure 6 shows LTE-A Advance

Protocol Stack. This protocol stack has two classifications

namely Non-Access Stratum (NAS) and Access Stratum (AS).

Further NAS is also divided into two parts viz. Control Plane

and User Plane. Both Planes side have Four layers but control

plane has a special layer named RRC Layer. PHY (Physical

Layer) frame formation as per TDD or FDD Topology and

asper OFDMA based structure. MAC Layer is responsible for

Multiplexing/de multiplexing of RLC Packet Data Units

(PDUs), scheduling information reporting. [52]

Error correction through Hybrid ARQ (HARQ), Padding and

Local Channel Prioritization.

Figure 6 LTE-A Protocol Stack

Radio Link Control (RLC) is responsible for error correction

through Automatic Repeat request (ARQ). It provides services

like segmentation according to the size of the transport block

and re-segmentation in case a retransmission is needed,

Concatenation of SDUs for the same radio bearer, Protocol err

or detection and recovery and in-sequence delivery. Packet

Data Convergence Protocol (PDCP) provides services like

header compression. Duplicate detection, Ciphering and

integrity protection. Radio Resource Control(RRC) is

broadcast system information which is related to Non-Access

Stratum (NAS) and Access Stratum (AS). It is basically

responsible for theestablishment, maintenance, and release of

RRC connection. It also includes thefunctionalityfor mobility

and QoS management. It also takes care of -NAS direct

message transfer between UE and NAS.Non-Access Stratum

(NAS) is responsible for connection/session management

between UE and the core network.

EPCglobal

EPCglobal is an organizational set which is developing

standards for EPC and RIFD. These standards will be

acceptable in worldwide. This architecture is acceptable

because it is a promising technologyfor future IOT. With this

architecture, all vendors EPC device can communicate each

other. It creates a network viz. EPCglobal Network. Further,

this network is divided into five components: EPC, EPC

middleware, Discovery services,IDsystem,and EPC

Information Services.

Table IV

Comparison Considerationbetween Infrastructural

Protocols

TABLE III[54]

Comparison of data Speed LTE-A and others technologies

3G WiMAX HSPA+

LTE LTE

Advanced

Peak rate 3

Mbps

128

Mbps

168

Mbps

300

Mbps

1 Gbps

Download

rate

(actual)

0.5 –

1.5

Mbps

2 – 6

Mbps

1 – 10

Mbps

10 –

100

Mbps

100 – 300

Mbps

Upload

rate

(actual)

0.2 –

0.5

Mbps

1 – 2

Mbps

0.5 –

4.5

Mbps

5 –

50

Mbps

10 – 70

Mbps


79

Infr

astr

uct

ura

l

Pro

toco

l

Sp

read

ing

Tec

hn

iqu

e

Rad

io B

and

MA

C

Acc

ess

Dat

a

IEEE

802.15.4

DSSS 868/9

15/24

00

TDMA,

CSMA/

CA

20/40/

250 K

BLE FHSS 2400 TDMA 1024 K

EPC

global

DS-

DCMA

868-

960

ALOHA 5

K

LTE-A Multiple

CC

Varie

s

OFDMA 1G(

500 M

(down)

Z-Wave - 868/9

08/24

00

CSMA/

CA

40 K

B. Service Discovery protocols

The domain of IOT is highly scalable. Due to high scalability,

it is frequent to add and remove IOT devices

requires a resource management mechanism that is able to

register and discover the things in the network. The services

must be dynamic, self – configured, efficient and dynamic to

add and remove the devices and update the network

information. For this purpose, service discovery protocols are

used. There is two service discovery protocols are Multicast

DNS(mDNS), DNS Service discovery (DNS-SD).

1.1 Multicast DNS(mDNS)

Multicast DNS(mDNS) is a zero configuration service of

DNS for asmall network. It does not include a local name

server in thesmall network. It issuch service which can

perform thetask of unicast DNS. In this, DNS name space

resides in local space without bearingextra configuration

expense. mDNS is a suitable choice for Internet based IOT

devices. The main features of mDNSare 1) No need to manual

configuration 2). It is able to run service with infrastructure 3)

it is continuing to provide service even if

infrastructure has happened. This protocol is

by using an IP multicast message with UDP.

needs to resolve thename in the small network, i

multicast request to all node in the network. This message

query asks to all devices. The name is matched with its name.

If thenameis matched with hostname, then host send IP

address to receiving end. Each node holds network

configuration file inside thehost. It works fine in

Dat

a

Rat

e

Sca

lab

ilit

y

20/40/

250 K

65 K

1024 K 5917

5-640

K

-

1G(P)

500 M

(down)

-

40 K 232

nodes

. Due to high scalability,

IOT devices in real time. It

requires a resource management mechanism that is able to

and discover the things in the network. The services

configured, efficient and dynamic to

add and remove the devices and update the network

information. For this purpose, service discovery protocols are

cols are Multicast

SD).[52]

zero configuration service of

network. It does not include a local name

service which can

of unicast DNS. In this, DNS name space

extra configuration and

for Internet based IOT

1) No need to manual

service with infrastructure 3)

it is continuing to provide service even if thefailure of

resolving names

UDP. If any client

in the small network, it sends an IP

multicast request to all node in the network. This message

query asks to all devices. The name is matched with its name.

with hostname, then host send IP

address to receiving end. Each node holds network

. It works fine in thesmall

network if mDNS is working with “. local” extension

thename is not found. Thelocal file

name server for this purpose. [52]

1.2 DNS Service Discovery (DNS-

This protocol provides amechanism

network like printer service, mail servic

This service discovery mechanism

Discovery(DNS-SD). The clients can

network with coordination of mDNS

is Zero configuration to connect machines. There are two steps

to be taken to perform thejob of DNS

find host names of required services.

pairing the hostname with IP address. In this

the hostname is important because IP address

nature. IP address changes periodically.

message asks to host for IP address and the hostname will be

attached with IP address and port Id.

C. Application Protocols

ConstrainedApplication Protocol (CoAP)

ConstrainedApplication Protocol (CoAP) is a web transfer

protocol, which is used with constraints nodes,

network in IOT. This protocol is designed for Machine

Machine (M2M) applications. It works on

with 10 kb RAM and 100 kb code space.

protocol is toenable tiny

computationcapacity, and communication with

technology. This protocol is classified in two sublayers: the

messaging sublayerand Request/response

sublayer is responsible for detecting duplications and

providing reliable communication by using UDP. But UDP

does not support in built error recovery mechanism. This job is

done by REST technique. To fulfilled

it uses four types of messages confirmable message, non

confirmable message, rest message and

message. This protocol four type of modes of response

messages:confirmable response message, Non

messages, piggyback responses and

messages. COAP includes some of

Resource observation, block wise resource

discovery, interacting with HTTP and security

Message Queue Telemetry Protocol MQTT

MQTT is an application layer

lightweight messaging protocol.

Figure 7 MQTT on TCP Protocol

It works on top of TCP protocol.

protocol is to connect the embedded devices. It also connects

with “. local” extension file. If

file then it uses the unicast

-SD)

mechanism to discover service in the

network like printer service, mail service, GPS service etc.

This service discovery mechanism is known as DNS Service

SD). The clients can discover a service in the

of mDNS. Like mDNS, DNS-SD

configuration to connect machines. There are two steps

of DNS-SD.The first step is to

ired services. The second step is

pairing the hostname with IP address. In this technique finding

the hostname is important because IP address is dynamic

periodically. Multicast query

to host for IP address and the hostname will be

attached with IP address and port Id.[52]

Protocol (CoAP)

Protocol (CoAP) is a web transfer

protocol, which is used with constraints nodes, and constraints

network in IOT. This protocol is designed for Machine-to-

Machine (M2M) applications. It works on amicrocontroller

with 10 kb RAM and 100 kb code space. The aim of this

devices with power,

communication with based on REST

This protocol is classified in two sublayers: the

response sub layer. Messaging

for detecting duplications and

reliable communication by using UDP. But UDP

in built error recovery mechanism. This job is

fulfilled reliable communication,

it uses four types of messages confirmable message, non-

confirmable message, rest message and acknowledgment

This protocol four type of modes of response

message, Non-confirmable

responses and separate response

messages. COAP includes some of thefeatures, whichare

wise resource transport, resource

nteracting with HTTP and security.[52]

Message Queue Telemetry Protocol MQTT

MQTT is an application layer protocol, which uses as

MQTT on TCP Protocol

It works on top of TCP protocol. The basic aim of this

the embedded devices. It also connects


80

tiny devices to network with applications. It is designed for

aremote connection where the limited bandwidth is available.

It delivers messages through three modes: MQTT 3.1, MQTT-

SN and third mode is specially designed for sensor networks.

It uses a publish/ subscribebe pattern for Machine-to-Machine

communication. [52]

Figure 8: Communication pattern between Publisher, Broker, and Subscriber

It contains three components: Subscriber, publisher,and

broker. The subscriberis responsible for receiving amessage

from clients. The publisher is responsible for sending

messages from the clients in the form of Publish/ Subscribe

pattern. The broker is found in the middle between Subscriber

and Publisher. The broker provides services to Publisher for

sending amessage to Subscriber. It is an ideal messaging

protocol for communication between M2M and IOT. It also

provides routing facility for small, low battery and low

memory devices. It contains verities of messages as

CONNECT (Client request to request to server), CONNACK

(Connect Acknowledgement), PUBLISH (Publish

Message),PUBACK (Publish Acknowledgement),PUBREC

(Publish Received), PUBREL (Publish release), PUNCOMP

(Publish Complete), SUBSCRIBE (Client Subscribe Request),

SUBACK (Subscribe acknowledgment), UNSUBSCRIBE

(Unsubscribe Request),UNSUBACK (Unsubscribe

Acknowledgement)and DISCONNECT (Client is

disconnecting). The format of MQTT contains fields: Message

Type, UDP, QOSLevel, Retain, remaining Length are acore

part of themessage format. Variable Length and Variable

Length Message Payload are two optional fields in MQTT

message Format.[52] .Figure 9 shows MQTT message format.

Figure 9: MQTT Message Format

MQTT-SN

MQTT- SN is a special light weighted protocol, which is

design for Wireless Sensor Networks. It is running on the top

of ZigBee APS layer. It is optimal for sensor and wireless

devices, which consist small, low cost and low batteries.

Figure 10 MQTT-SN Architecture

This architecture includestheircomponents: MQTT-SN Client,

MQTT-SN Gateway, MQTT broker. MQTT-SN client is tiny

sensor device that can directly connect the network. It requires

converting the message into network format. MQTT-Client

approaches MQTT-SN Gateway for sending amessage to

thenetwork. MQTT-SN attaches with MQTT Broker. MQTT-

Broker sends amessage to Subscriber with using the Internet.

[52]

Extensible Messaging and Presence Protocol – (XMPP)

XMPP is an instant message protocol, which is useful for

Multi-user chatting, video calling, file transfer and IOT

devices commutation. It was design and developed by Jabber

communities in 1999. It is an open source protocol. This

protocol allows users communicate with each other with

Instant message mechanism.Figure 11 shows XMPP

architecture.

Figure 11 XMPP Architecture

This architecture includes three components XMPP gateway,

XMPP-Server, and XMPP-Client.XMPP isa client-server

architecture. XXPPworks as SMTP protocol and uses text

messages for communications. XXPP-Client communicates its

server. Other devices like SMS client cannot communicate

directly. SMS client can communicate with its server. Then

SMS-Server communicatesXXPP-Gateway, which provides

interaction service to XXPP protocol. Due to open protocol,


81

there is no centralized Server in the Globe. For this reason,

each organization createsown XXPP-Server. Each component

has aunique address. Address of each component is similar to

email id. The format of theaddress is “node @

domain/terminal”. It also provides the security, authentication

and encryption services to its. Due to text message format, it

is limited to do binary communication.

Advance Message Queuing Protocol-AMQP

AMQP is an open standard business messaging protocol. It

guarantees reliable communication by using message delivery

mechanism. This provides facility like message Orientation,

routing, queuing, security and reliability. For providing

reliable delivery of messages, it uses reliable protocol TCP for

exchanges messages. There are mainly two components handle

communication namely Exchange and message queues. The

main responsibility of exchange is to route the messages to

their queues. The messages are stored in the queues. This

protocol uses publish/subscribe model for communication. It

contains two types of message. [52]

Data Distribution Service –DDS

Data Distribution Service is known as middleware for

Machine-to-Machine communication in IOT. It operates on a

Publish-Subscribe pattern, which is developed by Object

Management Group (OMG). The main feature of this service

is broker fewer communications. It uses multicasting

broadcasting for communication between sender and receivers.

Its architecture contains mainly two layers: thefirst layer is

Data-Centric Publish-Subscribe Layer and responsible for

delivering messages to receivers. Another Layer is Data Local

Reconstruction Layer and responsible for providing interface

first layer. DDS Service contains five entities in its

architecture 1) Publisher 2) Data Writer 3) Subscriber 4) Data

Reader and 5) Topic. It is exclusively data centric middleware

service, which is a best for IOT applications. It provides

controlled, secure and manages service for IOT data

communication. [52]

V IOT PLATFORMS

Before discussing asurvey of IOT Platform, it is essential

firstly define the term Platform. The platform is a thing which

allows us to deploy and execute our applications. A platform

can be a combination of Hardware and Software bundles

which allow executing other applications. The platform is the

upper layer of theoperating system while operating is also

above the hardware. The platformprovides an environment to

execute easily without interacting directly with theoperating

system. IOT Platform provides an environment which

provides application-independent functionalities. These

functionalities could be used for application development. It is

also known as avirtual solution. It means information is

collected from thesmart object and send to another object is

known as data. The main objective of IOT Platform is

translating smart things data to useful information. There are

at least 50 IOT platform exit in the Global market. These IOT

platforms fulfill the requirement of different domain people

like healthcare, transportation, agriculture, government, and

manufacturing units. This module presents different IOT

Platforms to serve the different functionalities to adifferent

domain of IOT. The comparison study of different IOT

Platform is shown in Table V

TABLE V

IOT Platform Features [52,53]

Platform

Su

pp

ort

of

het

ero

gy

no

us

Dev

ices

Ty

pe

Arc

hit

ectu

re

Da

ta A

cces

s C

on

tro

l

Ser

vic

e D

isco

ver

y

RE

ST

Co

AP

XM

PP

MQ

TT

AirVantage ^ % C L - + - - +

Axeda + % C F - + - - -

ARM

mbed

E % C U - C

*

+ - -

Carriots + P C S - + - - + Device

Control

+ P C - - N - -- -

Everyware ^ P C N No + - - -

Evry Thing + % C F

G

- + - -- -

Exosite + P C N NO + - - -

Fosstrack R S* C* L No - - - -

Nimbits + S* C* 3

L

No + - + -

Ninja

Platform

^ P C O NO + - - -

RealTime.io ^ P C S

A

O + - - -

Sensor

Cloud

N

O

P C N

o

NO + - - -

Tempo DB N

O

P C N

O

No + - - -

ThingWorx + % C N

O

+ + - - +

Xively + P C O

S

+ + - - +

^ Need Gateway, % M2M PaaS, C- Cloud Based, L-Library

only, +Yes, -No, O- OAuth2, E-Embedded Devices, U-User

choice. F-Facebook, C*- CoAP,P-PaaS, S-Squire access,N-

Not Applicable, FG-Fine Grain, R-RIFD, S*-SERVER, C*-

Centralized, L-Locally stored, 3L-three level, OS-Open Source

[52-60]

VI RESEARCH CHALLENGES AND FUTURE TRENDS

A. Research Challenges of IOT

.

There are some technical challenges of IOT is listed below


82

1. Designing a service oriented architecture of IOT

2. Handlingof complicated heterogeneous Network

3. Lack of common standard for commutations and

Interoperability of IOT Platform and IOT device

4. Use of traditional ICT technology.

5. Resource discovery, resources management, data

management, event management and code management

are challenges still exist in IOT Middleware

6. RPL suffers from internal attacks and

Issues.

7. There are many security issues in Network layer of

I6LoWPAN.

8. IOT Virtualization is limitations for implementing IOT

things properly.

9. New feature and technologies of IOT cannot

previous Security protocols, whichare

Internet.

10. High availability of theresource is also a

IOT.

11. Reliability is achallenge in IOT; the system must be

working as per specification. Here, Hardware and

software must work properly as a

eachLayer of IOT.

12. Mobility is a challenge of IOT. In IOT,

mobile users. Providing most of theservices

users is abig challenge in IOT.

13. Evaluation of performance of IOT services

challenge. There are so component and

adding, developing andimproving every

challenging task to evaluate the performance whole IOT.

14. Scalability is achallenge for IOT. It must provide

theability to add new services, devices

without affecting previous active services.

be unaware of these changes.

15. Security is an important research challenge

It is difficult to provide securityguarantee

standardization and architecture of IOT devices.

B. Survey Result & Future Trends.

The study of larger number papersprovides an

IOT domain. This study provides the search results about

different research areas and research possibilities, which

existing in IOT. The survey shows what trends are going on

beyond 2015. The Table VI shows the number of papers

hasstudied by authors and trends are shown in figure 10. At

the end Table,VII provides alistof IOT Research domains

research possibilities variables.

TABLE VI

Survey Trends of IOT

IOT Domain Sub Domain No of Articles

study

Survey papers 22

Healthcare 18

IOT Platform 15

Designing a service oriented architecture of IOT

Network

commutations and

Interoperability of IOT Platform and IOT device

Resource discovery, resources management, data

management and code management

Middleware.

rom internal attacks and Performance

in Network layer of

IOT Virtualization is limitations for implementing IOT

New feature and technologies of IOT cannot secure by

are based on the

abig challenge in

the system must be

, Hardware and

aspecification in

IOT, almost users are

services to mobile

IOT services is a

ent and services are

developing andimproving every day. It is

evaluate the performance whole IOT.

It must provide

devices, and functions

previous active services. The user must

challenge for the IOT.

securityguarantee due to wear

standardization and architecture of IOT devices.

provides an overview of

IOT domain. This study provides the search results about

possibilities, which are

IOT. The survey shows what trends are going on

the number of papers

and trends are shown in figure 10. At

Research domains and

o of Articles

study

IOT Routing

IOT Software

IOT Master

Thesis

IOT Ph.D.Thesis

Networking

Security Issue

Smart Society

Smart City

Smart Devices

Smart Grid

Smart home

Smart industry

Smart Tourism

Web of Things

Architecture

M2M

Cloud & IOT

Fog & IOT

RIFD

Total Research Papers Studied

Figure 10 IOT Research Areas and Future Trends

TABLE VII

IOT Research Areas and Future beyond 2015

Research Domains Research Needs & possibilities

Hardware Devices Polymer based memory, Molecular

Sensors, Biodegrade antennas

Autonomous

Communication Self-configuring, Protocol seamless

15%

7%

6%

5%

2%2%

22%

IOT Research Trends

Survey papers Healthcare

IOT Platform IOT Routing

IOT Software IOT master thesis

IOT PhD thesis Networking

Security issue

07

06

05

02

02

22

17

10

10

6

4

5

5

10

15

10

5

4

200

IOT Research Areas and Future Trends

IOT Research Areas and Future beyond 2015

Research Needs & possibilities

Polymer based memory, Molecular

Sensors, Biodegrade antennas,

Autonomous devices

configuring, Protocol seamless

23%

18%

IOT Research Trends

Healthcare

IOT Routing

IOT master thesis

Networking


83

Technologies Network

Network

Technologies

Need Based Network

Software and

Algorithms

Context aware, Self- Reusable,

Self-configurable, Self –Healing,

Self-Management, Self-generating,

Platform for Object Intelligence

Power and Energy

Storage Technologies

Paper based Batteries, Power

generation for harsh environment

Security and privacy Context based Security algorithms,

Cognitive Security systems, Service

triggered Security, Object

Intelligence

Standardization Dynamic Standards, Evolutionary

Standards,

Standards of Interacting devices

and Personalized devices

Healthcare IOT Smart Rehabilitation, Smart Patient

Monitoring, Smart Hospital

management, Smart Human

Resource Monitoring, Smart

Medicine ATMs, Wound Analysis

for diabetes patients, Wheelchair

management, Cough detection,

Oxygen saturation monitoring

Smart Cities Smart Traffic Monitoring, Smart

Building, Smart Water Supply

System, Smart Roads and Public

Infrastructures

Smart Tourism Smart Tourist places monitoring,

Tourist guidance GPS, Smart

Hotels monitoring, Smart Tourist

Vehicles monitoring

Smart Grids New time series forecasting

methods,communications

infrastructure for self-healing grids,

enhanced reliability and power

quality studies,

improvementinpower

flowoptimization,new EV battery

techniques to prolong their useful

life,practical methods for large

scale RES integration, cloud based

control and management,new and

improved battery systems,battery

wearing in V2G

Smart Industries Smart Machines,Smart

Devices,Smart Manufacturing

Processes, Smart Engineering,

Manufacturing IT, Smart

logistics,Factory visibility and

optimized decision-making,Remote

monitoring,Proactive maintenance

Smart Suppliers

Fog & IOT Latency Constraints, Network

Constraints, Resource Constraints

Devices, Uninterrupted Services

connectivity to Cloud, Security

technique

Mobile Phone with

IOT

Heterogeneity, Continue Sensing,

Crowd Sensing, Persuasion, Search

and Discovery

RPL Recovery mechanism form

falsification attacks, Byzantine

attacks, Selective –forward attacks,

Sinkhole attacks, gray hole attacks,

black hole attacks, version number

manipulation attacks, Routing

information Replay, Secure

connection, and authentication

mechanism

Routing Self-stabilization, Location Privacy,

Light weight Computations, secure

routing protocols for Tiny devices,

Effective node identification

system

VII CONCLUSION

This survey article is the summary of thecurrent state of IOT

research. It provides aglance on research domains existing in

the IOT by examining 200 literature surveys. This paper

identifies current trends andinnovations, which are helpful in

describing the challenges existing in IOT. It also keeps the

promise of improving thelives of thelife of people. The

potential shown in this paper can prove to save people and

money. It can be helpful for an organization to improve

decision and outcomes in the wide range of research area in

IOT. Different technologieslike WSN, RFID, EPC,

Middleware etc. have been overviewed in this paper. The

application of IOT domain trees like smart city, smart home,

smart tourism, smart industries, and healthcare gives aview of

asmart network of smart objects for providing meaningful

information for people. It also describes asurvey of IOT

platforms, which provides different features of different IOT

Platform. A number of Challenges arefocused on the overall

picture of IOT domains, which highlights possible research

opportunities for future IOT Researchers as a whole.

VIII REFERENCES

[1].Ray pp, “A survey of iot cloud platforms”, future

computing and informatics journal (2017), doi:

10.1016/j.fcij.2017.02.001.

[2] Jayavardhana Gubbia, Rajkumar Buyyab, Slaven Marusic,

Marimuthu Palaniswami, “Internet of Things (IoT): A vision,

architectural elements, and future directions”, Future

Generation Computer Systems 29 (2013) 1645–1660.

[3]. EPC Information http://www.epc-rfid.info/

[4]. Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal,

“Wireless sensor network survey”,* Department of Computer


84

Science, University of California, Davis, CA 95616, United

States.

[5]. Shancang Li & Li Da Xu & Shanshan Zhao, “The internet

of things: a survey”, Inf Syst Front DOI 10.1007/s10796-014-

9492-7.

[6]. M.A. Razzaque, Marija Milojevic-Jevric, Andrei Palade,

Siobhá Clarke, “Middleware for Internet of Things: A

Survey”,DOI 10.1109/JIOT.2015.2498900, IEEE Internet of

Things Journal.

[7]. Gerhard P. Hancke, Burno de carvalhoshiva and Gerhard

P. Hancke, “The Role of Advance sensing in smart cities”,

Sensors (Basel). 2013 Jan; 13(1): 393–425. Published online

2012 Dec 27. doi: 10.3390/s130100393.

[8]. Ejaz Ahmed, Ibrar Yaqoob, Abdullah Gani, Muhammad

Imran, and Mohsen Guizani, “Internet-of-Things-Based Smart

Environments: State of the Art, Taxonomy, and Open

Research Challenges”, IEEE Wireless Communications •

October 2016.

[9].Er. Vineet Biswal, Er. Hari M. Singh, Dr. W. Jeberson, Er.

Anchit S. Dhar, “Greeves: A Smart Houseplant Watering and

Monitoring System”, International Journal of Science,

Engineering and Technology Research (IJSETR), Volume 4,

Issue 7, July 2015.

[10]. Xi Fang, Student Member, IEEE, Satyajayant Misra,

Member, IEEE, Guoliang Xue, Fellow, IEEE, and Dejun

Yang, Student Member, IEEE, “Smart Grid – The New and

Improved Power Grid: A Survey”.

[11] Li Jiang, Da-You Llu, Bo yang, “Smart Home

Research”,Proceedings of the Third International Conference

on Machine Learning and Cybernetics, Shanghai, 2629 August

2004.[12]. Gao Chong Ling Zhihao, Yuan Yifeng, “The Research

and Implement of Smart Home System Based on Internet of

Things”, This paper received the support of National 863

project (2009AA04Z144) and Shanghai Leading Academic

Discipline Project (No. B504).

[13]. Mohsen Darianian, Martin Peter Michael,"Smart Home

Mobile RFID-based Internet-Of-Things Systems and

Services",2008 International Conference on Advanced

Computer Theory and Engineering.

[14].Moataz Soliman, Tobi Abiodun, Tarek Hamouda, Jiehan

Zhou, Chung-HorngLung,"Smart Home: Integrating Internet

of Things with Web Services and Cloud Computing",2013

IEEE International Conference on Cloud Computing

Technology and Science.

[15]. Baoan Lia, Jianjun Yub,"Research and application on the

smart home based on component technologies and Internet of

Things",Advanced in Control Engineering and Information

Science.

[16]. Vincent Ricquebourg, David Menga, David Durand,

Bruno Marhic, Laurent Delahoche, Christophe, “The Smart

Home Concept: our immediate future”.

[17].Xi Fang, Satyajayant Misra , Guoliang Xue, and Dejun

Yang, “Smart Grig- The New and Improved Power Grid: A

Survey”

[18]Office of the National Coodinator for Smart Grid

Interoperabilty. NIST framework and roadmap for smart grid

interoperablity standards 2012.

[19] Maria LorenaTuballa, MichaelLochinvarAbundo ,"A

review of the development of Smart Grid

technologies",Renewable and Sustainable Energy Reviews59

(2016)710–725.

[20]. Xi Fang, Student Member, IEEE, Satyajayant Misra,

Member, IEEE, Guoliang Xue, Fellow, IEEE, and Dejun

Yang, Student Member, IEEE," Smart Grid – The New and

Improved Power Grid: A Survey",IEEE Communications

surveys & tutorials, vol. 14, no. 4, fourth quarter 2012.

[21].Elisa Spanò, Luca Niccolini, Stefano Di Pascoli, and

Giuseppe Iannaccone, Senior Member, IEEE,"Last-Meter

Smart Grid Embedded in an Internet-of-Things

Platform",IEEE TRANSACTIONS ON SMART GRID, VOL.

6, NO. 1, JANUARY 2015.

[22].Vehbi C. Güngör, Member, IEEE, Dilan Sahin, Taskin

Kocak, Salih Ergüt, Concettina Buccella, Senior Member,

IEEE, Carlo Cecati, Fellow, IEEE, and Gerhard P. Hancke,

Senior Member, IEEE," Smart Grid Technologies:

Communication Technologies and Standards",IEEE

transactions on industrial informatics, vol. 7, no. 4, november

2011.

[23].Fangxing Li, Senior Member, IEEE, Wei Qiao, Member,

IEEE, Hongbin Sun, Member, IEEE, Hui Wan, Member,

IEEE, Jianhui Wang, Member, IEEE, Yan Xia, Member,

IEEE, Zhao Xu, Member, IEEE, and Pei Zhang, Senior

Member, IEEE,"Smart Transmission Grid: Vision and

Framework",IEEE transactions on smart grid, vol. 1, no. 2,

september 2010.

[24]. Ye Yan, Yi Qian, Hamid Sharif, and David Tipper,"A

Survey on Smart Grid Communication Infrastructures:

Motivations,Requirements and challenges", IEEE

communications surveys & tutorials

[25] Ling Zheng, Shuangbao Chen, Shuyue Xiang,

YanxiangHu,"Research of architecture and application of

Internet of Things for smart grid",2012 International

Conference on Computer Science and Service System.

[26]. Giacomo DelChiappa,RodolfoBaggio, “Knowledge

transferring smarttourismdestinations:Analyzing the

effectsofanetworkstructure", Journal of Destination Marketing

&

Management",http://dx.doi.org/10.1016/j.jdmm.2015.02.001i.

[27]. C. Derrick Huang, C. Derrick Huang,"Smart Tourism

Technologies in Travel Planning: The Role of Exploration and

Exploitation.

[28]Li, Y., et al., The concept of smart tourism in the context

of tourism information services, Tourism Management (2016),

http://dx.doi.org/10.1016/j.tourman.2016.03.014.

[29].ThirumalaisamyRagunathana, Sudheer Kumar Battulab,

JorikaVedikac, V Anithad, T. Tarune, M. Shiva Prasadf, M.

Uma Kalyanig, “ITTS: Intelligent Transport and Tourism

System”,scientific committee of 2nd International Symposium

on Big Data and Cloud Computing (ISBCC’15) doi:

10.1016/j.procs.2015.04.089.

[30]. Fernando Zacariasa, RosalbaCuapab, Guillermo De Itaa,

Daniel Torresa,"Smart Tourism in 1-Click”, doi:

10.1016/j.procs.2015.07.234.

[31]. ShiyongWang,JiafuWan,DaqiangZhang, Di Li

,ChunhuaZhang ,"Towards smart factory for industry 4.0: a

self-organized multi-agent system with big data based

feedback and coordination",Computer Networks 101 (2016)

158–168.


85

[32]. F. Shrouf1,2, J. Ordieres2, G. Miragliotta, “Smart

Factories in Industry 4.0: A Review of the Concept and of

Energy Management Approached in Production Based on the

Internet of Things Paradigm”, Proceedings of the 2014 IEEE

IEEM.

[33]Ioan Ungurean, Nicoleta-Cristina Gaitan and

VasileGheorghitaGaitan, “An IoT Architecture for Things

from Industrial Environment”

[34]. Li Da Xu (Senior Member, IEEE), Wu He, Shancang Li,

“Internet of Things in Industries: A Survey”, DOI

10.1109/TII.2014.2300753, IEEE Transactions on Industrial

Informatics.

[35].S. M. Riazul islam1, (member, IEEE), Daehan Kwak2, d.

Humaun Kabir1, Mahmud Hossain3, and Kyung-sup wak1,

(member, ieee), “The Internet of Things for Health Care: A

Comprehensive Survey”.

[36]Luca Catarinucci, Danilo De Donno, Luca Mainetti, Luca

Palano, Luigi Patrono, Maria Laura ztefanizzi, and Luciano

Tarricone, “An IoT-Aware Architecture for Smart Healthcare

Systems “.

[37].Lee Carman Ka Man, Cheng Mei Na and Ng Chun Kit,

“IoT-based Asset Management System for Healthcare-related

Industries”,International Journal of Engineering Business

Management,Int J Eng Bus Manag, 2015, 7:19 | doi:

10.5772/61821.

[38]S. M. Riazul Islam, (Member, IEEE), Daehan Kwak, MD.

Humaun kabir1, mahmud hossain, and kyung-sup kwak,

(Member, IEEE), “The Internet of Things for Health Care: A

Comprehensive Survey”,Digital Object Identifier

10.1109/ACCESS.2015.2437951.

[39].Y. YIN, The internet of things in healthcare: An

overview, Journal of Industrial Information Integration (2016),

http://dx.doi.org/10.1016/j.jii.2016.03.004.

[40].YLee Carman Ka Man, Cheng Mei Na1 and Ng Chun

Kit,“IoT-based Asset Management System for Healthcare-

related Industries”,International Journal of Engineering

Business Management.

[41]Luca Catarinucci, Danilo De Donno, Luca Mainetti, Luca

Palano, Luigi Patrono, Maria Laura Stefanizzi, and Luciano

Tarricone. “An IoT-Aware Architecture for Smart Healthcare

Systems”

[42].Prosanta Gope, Tzonelih Hwang,"BSN-Care: A Secure

IoT-based Modern Healthcare System Using Body Sensor

Network”,DOI 10.1109/JSEN.2015.2502401, IEEE Sensors

Journal.

[43].Mersini Paschou, Evangelos Sakkopoulos , Efrosini

Sourla,Athanasios Tsakalidis,"Health Internet of Things:

Metrics and methods for efficient data

transfer",http://dx.doi.org/10.1016/j.simpat.2012.08.002.

[44].Mir Sajjad Hussain Talpur, Md Zakirul Alam Bhuiyan

and Guojun Wang,"Energy-efficient healthcare monitoring

with

smartphones and IoT technologies"Int. J. High Performance

Computing and Networking, Vol. 8, No. 2, 2015.

[45].Yared Berhanu,Habtamu Abie, Mohamed Hamdi, “A

Testbed for Adaptive Security for IoT in

eHealth”,http://dx.doi.org/10.1145/2523501.2523506.

[46]. Marco Bazzani, Davide Conzon, Andrea Scalera,Claudia

Irene Trainito,“Enabling the IoT paradigm in e-health

solutions through the VIRTUS middleware",

DOI 10.1109/TrustCom.2012.144

[47].Farahani, et al., Towards fog-driven IoT eHealth:

Promises and challenges of IoT in medicine and healthcare,

Future Generation Computer Systems (2017),

http://dx.doi.org/10.1016/j.future.2017.04.036.

[48]. A.M. Rahmani, et al., Exploiting smart e-Health

gateways at the edge of healthcare Internet-of-Things: A fog

computing approach, Future Generation Computer Systems

(2017), http://dx.doi.org/10.1016/j.future.2017.02.014.

[49]. Geoff Mulligan,"The 6LoWPAN Architecture",

EmNets2007, June 25-26, 2007, Cork, Ireland. Copyright 2007

ACM 1-58113-000-0/00/0004.

[50]. Shahid Raza, Simon Duquennoy, Tony Chung,

DoganYaza, Thiemo Voigt and UtzRoedig, “Securing

Communication in 6LoWPAN with Compressed IPsec”

[51]OanaIova, Gian Pietro Picco, TimofeiIstomin, and

CsabaKiraly, RPL: The Routing Standard for the Internet of

Things… Or Is It?

[52]. Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi,

Mohammed Aledhari, Moussa Ayyash, Internet of Things:“A

Survey on Enabling Technologies, Protocols and

Applications”, DOI 10.1109/COMST.2015.2444095, IEEE

Communications Surveys & Tutorials.

[53]. as: Julien Mineraud, Oleksiy Mazhelis, Xiang Su, Sasu

Tarkoma, A gap analysis of Internet-of-Things platforms,

Computer Communications (2016), doi:

10.1016/j.comcom.2016.03.015.

[54]https://www.digitaltrends.com/mobile/what-is-lte-

advanced-and-why-should-you-care/

[55]. Bhumi Nakhuva and Prof. TusharChampaneria, “Study

of Various Internet of Things Platforms”, International Journal

of Computer Science & Engineering Survey (IJCSES) Vol.6,

No.6, December 2015.

[56].Milan Zdravkovic, Miroslav Trajanovic, Jo~aoSarraipa,

Ricardo Jardim-Goncalves, Mario,Lezoche, et al. Survey of

Internet-of-Things platforms. 6th International Conference on

Information Society and Technology, ICIST 2016, Feb 2016,

Kopaonik, Serbia. 1, pp.216-220. <hal-01298141>

[57]. Partha Pratim Ray, “A survey of IoT cloud

platforms”,http://dx.doi.org/10.1016/j.fcij.2017.02.001.

[58].Oleksiy Mazhelis and PasiTyrväinen, "A Framework for

Evaluating Internet-of-Things Platforms: Application Provider

Viewpoint",2014 IEEE World Forum on Internet of Things

(WF-IoT).

[59].Bhumi Nakhuva and Prof. TusharChampaneria, Study of

Various Internet of Things Platforms”, International Journal of

Computer Science & Engineering Survey (IJCSES) Vol.6,

No.6, December 2015

[60].SergiosSoursos, Ivana PodnarŽarko, Patrick Zwick, Ivan

Gojmerac Giuseppe Bianchi and Gino Carrozzo, “Towards the

Cross-Domain Interoperability of IoT Platforms”

[61] NEETESH SAXENA and SANTIAGO GRIJALVA,

Georgia Institute of Technology NARENDRA S.

CHAUDHARI, Indian Institute of Technology Indore,

“Authentication Protocol for an IoT-Enabled LTE Network”

[62].SHACHAR SIBONI and ASAF SHABTAI, “Advanced

Security Testbed Framework for Wearable IoT Devices”

Figure 9 http://www.rfwireless-world.com/Tutorials/MQTT-

tutorial.html


86

Figure 4 http://www.summitdata.com/blog/ble-overview/


87

STRATEGIES IN HYBRID

EVOLUTIONARY ALGORITHMS FOR

OPTIMIZATION

VISHAL JAIN

Associate Professor

Dept. of EE, BMIET, Sonepat

Dr. DEVENDER SAINI

Assistant Proffesor

Dept. of EE, UPES, Dehradun

SALONI

Assistant Professor

Dept. of EEE, BMIET, Sonepat

ABSTRACT--Evolutionary computing has grown to

be a significant methodology in the field of research.

Robustness and adaptation are some of the prime

features of evolutionary algorithms as compared to

other global optimization techniques. Even though

evolutionary computation is popularly used for solving

several important practical problems in engineering,

business, commerce, etc., there is scope for fine-tuning

its performance. It is not easy to find any one best

algorithm for solving all optimization problems. Hence

there is need for a hybrid algorithm which is capable of

handling several real world challenges such as, noise,

imprecision and uncertainty. This paper presents a

review on the methodologies adopted for hybrid

evolutionary Algorithms.

Keywords--Evolutionary algorithms,Genetic

Algorithm,Particle Swarm Optimization,Ant Colony

Optimization,Bacterial Foraging Optimization

I.INTRODUCTION

Evolutionary Computation, offers multiple advantages for difficult optimization problems including, the effortlessness of the approach, its adaptability, and numerous different aspects [1-4]. Recently the evolutionary algorithms have been vastly used, particularly for practical problem solving. Generally the term evolutionary computation or algorithms are used for the domains of genetic algorithms, and genetic programming [5]. These techniques use the concept of evolution through the basic three operators namely selection, mutation, and crossover. As Compared to other optimization techniques, EA produce good solutions and also are easy to implement. Fig 1 illustrates a basic structure of EA

A detailed survey on evolutionary computational

algorithm reveals that, for many problems a direct

evolutionary algorithm does not produce an optimal

solution [6-9]. This clearly gives a way for the need

for hybridization of evolutionary algorithms with

different optimization algorithms and heuristic

techniques.

Fig.1. Structure of Evolutionary Algorithm

II. REASONS FOR HYBRIDIZATION:

The efficient use of hybridization results in the

following improvement [10]:

1. It improves the quality of Solution obtained

2. It improves the convergence speed of the

Algorithm

3. It incorporates the EA as part of a larger system

III. HYBRID STRUCTURES FOR EA

Fig.2 illustrates the opportunity for incorporation of

other techniques. Population may be initialized by

introducing known solutions. Local search methods

may be incorporated within the initial population

members or among the off-springs. EA may be

hybridized by using distinct operators from same or

other algorithms by incorporating domain

knowledge.

PARENT

OFFSPRING

POPULATI

ON

SELECTI

ON

REPRODUCTI

ON REPLACEM

ENT


88

Fig 2 Hybridization possibilities in EA

Some of most utilized hybrid structures are outlined

as below:

1. ANN based EA

2. Fuzzy Logic based EA

3 PSO based EA

4. ACO based EA

5. Bacterial foraging optimization (BFO) based EA

6 Other heuristic techniques such as SA, Tabu search,

dynamic programming, hill climbing, etc. combined

with EA.

A Artificial Neural Networks Based Ea

The ANN techniques [11] are used for enhancing the performance of evolutionary algorithms. Neural networks (NN) are constructed based on the collected training samples. EA are then used to search good solutions. After the generation of new solutions, the fitness function is determined by NN. However multiple NN’s may be used to provide statistical predicted performance for the evolutionary algorithm.

B Flc Based Ea

Fuzzy logic controller (FLC) is based on the expert knowledge of the system and can be effectively used to frame linguistic control rules and provide a fuzzification interface by means of reasoning. It effectively transforms the crisp data into fuzzy sets. FLCs have been used to design adaptive evolutionary algorithms. Fuzzy Logic Controllers have been efficiently utilized to tune GA parameters [12]. The inputs of the FLC are chosen as different GA performance measures, and outputs may be considered as GA control parameters.

C Pso Based Ea

The concept of PSO originated from the swarming patterns observed in bees, birds or schools of fish, and even human social behavior [13-14]. A variety of hybrid evolutionary algorithm and PSO approaches are proposed in the literature [15]. The hybrid technique implements the two structures simultaneously and chooses P population from every framework for trading after the assigned N cycles.

The individual with superior fitness traits has more probability of getting selected.

A GA and PSO hybrid technique [16] is used to decipher optimization problem. In this method, in each cycle, the population is split into two parts and subsequently evolved with the two techniques, respectively. The two populations are then recombined in the fresh population, that is again divided randomly into two parts in the next iteration for another run of genetic or particle swarm operators.

D Aco Based Ea ACO approach is mimicked from the foraging

behavior of real ants, which are utilized to solve distinct optimization problems [17]. Various hybrid permutations have been used with ACO such as GA, Local search and Tabu Search. The hybridization introduces unique immigration scheme, new crossover schemes and random heuristics which enhances the diversity and helps in promoting the optimization process.

E Bacterial Foraging Optimisation Based Ea

Recent research shows that bacteria have been instrumental in solving optimization problems [18]. The foraging nature of Escherichia coli bacteria is mimicked. The various steps involve chemotaxis, swarming, reproduction, elimination and dispersal. Hybrid genetic algorithm (HGA) [19] and bacterial foraging approach has been used for optimization. In the hybrid structure, the members of population may be considered as a cluster of bacteria foraging in the problem search space. It is evident from the results that the hybrid GA-BFO approach is superior to a direct GA approach.

IV.CONCLUSIONS

Literature reveals that, the hybrid evolutionary

algorithms is gaining immense popularity and is

widely used for handling optimization problems. In

this paper, an attempt has been made to represent the

various strategies for hybridization of an evolutionary

algorithm and also to discuss its advantages over

direct EA. Some of the common hybrid frameworks

reported in the literature is also presented.

REFRENCES

1. Deb, K. 2002. Multi-objective optimization usingevolutionary algorithms Ross, S., & Weber, R., Eds.2nd ed. John Wiley and Sons Ltd.

2. D. E. Goldberg, Genetic Algorithms in Search,Optimization and Machine Learning. Reading, MA:AddisonWesley, 1989.

3. J.P. Rosca and D. H. Ballard, “Learning by adaptingrepresentations in genetic programming,” in Proc. 1stConf. Evolutionary Computation, 1994, pp. 407–412.

4. J. Kennedy, R. Eberhart, and Y. Shi, Swarm Intelligence,1st ed. San Mateo, CA: Morgan Kaufmann, 2001.


89

5. Koza JR (1992) Genetic Programming, MIT Press,Cambridge, MA

6. Li F, Morgan R, and Williams D (1997) Hybrid geneticapproaches to ramping rate constrained dynamiceconomic dispatch, Electric Power Systems Research, 43(11),

pp. 97–103 7. Lo CC and Chang WH (2000) A multiobjective hybrid

genetic algorithm for the capacitatedMulti-point network design problem, IEEE Transactions on

Systems, Man and Cybernetics - Part B, 30(3), pp. 461–470

8. Somasundaram P, Lakshmiramanan R, and KuppusamyK (2005) Hybrid algorithm based on EP and LP forsecurity constrained economic dispatch problem, Electric Power Systems Research, 76(1–3), pp. 77–85

9. Tseng LY and Liang SC (2005) A hybrid metaheuristicfor the quadratic assignment problem, ComputationalOptimization and Applications, 34(1), pp. 85–113

10. Sinha A and Goldberg DE (2003) A Survey of hybridgenetic and evolutionary algorithms, ILLIGALTechnical Report 2003004

11. Wang L (2005) A hybrid genetic algorithm-neuralnetwork strategy for simulation optimization, AppliedMathematics and Computation, 170(2), pp. 1329–1343

12. Lee MA and Takagi H (1993) Dynamic control ofgenetic algorithms using fuzzy logic techniques, InForrest S (Ed.), Proceedings of the 5th InternationalConference on Genetic algorithms, MorganKaufmmann, San Mateo, pp 76–83

13. Kennedy J and Eberhart RC (1995) Particle swarmoptimization, In Proceedings of IEEE InternationalConference on Neural Networks, Perth, Australia, pp.1942–1948

14. Kennedy J (1997) The particle swarm: social adaptationof knowledge, In Proceedings of IEEE InternationalConference on Evolutionary Computation,Indianapolis, IN, 1997, pp. 303–308

15. Shi XH, Liang YC, Lee HP, Lu C, and Wang LM(2005) An improved GA and a novel PSO-GA-basedhybrid algorithm, Information Processing Letters,93(5), pp. 255–261

16. Grimaldi EA, Grimacia F, Mussetta M, Pirinoli P, andZich RE (2004) A new hybrid genetical – swarmalgorithm for electromagnetic optimization, InProceedings of International Conference onComputational Electromagnetics and its Applications,Beijing, China, pp. 157–160

17. Dozier G, Bowen J, and Homaifar A (1998) Solvingconstraint satisfaction problems using hybridevolutionary search, IEEE Transactions onEvolutionary Computation, 2(1), pp. 23–33

18. Passino KM (2002) Biomimicry of bacterial foragingfor distributed optimization and control, IEEE ControlSystems Magazine, 22(3), pp. 52–67


90

Home and Automobile Automation Model

1, Gurminder Kaur,

2 Priyansh Gupta 1Assistant professor B.M. Institute of Engg. & Tech., Sonepat

2B.Tech, CSE , 4TH Year, BMIET Sonepat

Abstract— Wave lash (Home automation expert) is a

mobile web based application that permits its user to control

their home, automobile and monitor both using their

Smartphone. System requires a micro SD card with an OS for

the Raspberry Pi (Raspbian OS/NOOBS OS).Security is one of

the most critical component for its users who use Wave lash for

their home and automobile convenience and protection. For

security the system uses OTP (One Time Password) generation

which will be used as entry password for user. A messaging

API is used which helps in sending text messages to the user

about any activity in house, with the help of a text to speech

plug-in user can record messages which can be played when he

is not at home. Data from all these sensors is continuously

received and processed by Arduino Uno board that finctions as

a microcontroller unit. In case of unexpected situations, the

Arduino will trigger an alarm and alert messages will be sent

to user’s mobile via GSM. Thus the current system ensures

home and automobile safety as well as security with

incorporation of automation.

Keywords—Automation, Embedded System, GPIO Pins,

IOT, RFID

I. INTRODUCTION

In this fast growing world with new technologies emerging every day, Our lives have become much more dependent on electronic gadgets. In today’s world almost all electronic devices may be controlled with a remote wirelessly why should not there be a remote that provides us control over home but as well as over automobiles. Smart phones now being the basic necessity of our lifestyle can be used to bridge the gap and could give us the ability to control these

II. WHAT EXACTLY IS IOT?

Internet of things or IOT can be described as an ecosystem of technologies which communicates through IP networks with the software applications by monitoring the status of physical objects and collects meaningful data from them. Internet of things has evolved immensely with the help of multiple technologies including wireless sensors, real time analytics, and control Systems. It includes cars, machines in production plants, jet engines, oil drills, wearable devices, and more. These “things” collect and exchange data. Theembedded technology in IOT helps them to interact with

internal states or the external environment, which in turn affects the decisions taken.

III. TECHNOLOGIES USED WITH IOT

It incorporates some necessary components that enable the communication between various devices and objects. Each object is augmented with an Auto-ID tag or RFID tag so that the device may be easily identified giving it a unique id. It also allows objects to communicate wirelessly.

IV. HOW IOT CAN HELP

When devices can represent themselves digitally, they can be controlled from anywhere.IOT Platforms can help reduce the cost of organizations by reducing the cost and improving the efficiency and boosting the production, security. With improved tracking of devices with the help of sensors can help in acquiring real time insights and analytics which could help in making smarter decisions.

V. NEED OF IOT

The connectivity then helps us capture more data from more places, ensuring more ways of increasing efficiency and improving safety and IoT security. As objects are reporting real time data, we as users have the ability to make quicker and more accurate decisions for example in connected car with IoT comes in a revolutionary way for us to drive and stay in touch with the user and enabling Real Time Location of the car to provide security features as well. Home Automation means controlling various in-home devices like AC, television automobiles and other electrical devices over a wireless network connection.

VI. CONCEPT REVIEW

These requirements will allow the user to gain knowledge in how he should design the overall system so that it

functions as per the requirements stated below.

• Controlling Light Devices

• Controlling Ventilation Devices

• Bluetooth Connectivity

• Wi-Fi Connectivity

• Detecting Smoke and Gas Leaks


91

• Detecting Unauthorized Entry into House

• Keyless Entry house and Vehicle.

• Voice Integration & Motion

• Finding Current Temperature and Humidity

• Implementing Augmented Reality

• User’s Room Location Detection

• Google Maps API to calculate ETA to Home.

• Motion/Activity Module.

• Car security services.

• Vehicle location tracking and scheduling

solutions.

• SOS

• Smart alerts and notifications

• Connected vehicle sensors

Figure 1 IOT Connectivity Basic Illustration

Advantages of proposed System:

The new system must provide the following features:

• It allows more flexibility through android device.

• It allows a good range of scalability.

• It provides security and authentication.

• Additional vendors can be easily added.

V. SYSTEM DESIGN

The Basic Level DFD diagram for the proposed project that

we would be using is

Figure 2 Data Flow Diagram

System design the flow of the user with sensor works as

follows:

1) The user requests the specific task that he needs to

perform.

2) Following request is then transmitted to the designed

system .

3) then it transfers the query request to the server of Google

Firebase.

4) It then responds back to the hardware with the

corresponding task assigned to it.

5) The embedded system receives the signal changes and

then shows back the physical change with respect to

users query.

VI. SOFTWARE DEVELOPMENT LIFE CYCLE MODEL

The model we would be using would be V-model

Figure 3 Software Development life Cycle(SDLC)

VII. System Interfaces

The system is intended to interact with one single user at a time. The user will be able to interact easily with Graphical User Interface. The android application interacts with the user and simultaneously communicates to the hardware implemented in the appliances through either the Bluetooth or the Wi-Fi technology and retrieves the data in real time and displays it on the user’s phone.

User Interfaces

The user interface provides an ease of access to application.

• Home Activity has a Constraint layout which

contains the user image, the current temperature

and carousal containing the different rooms.

• Each carousal contains some appliances and setting

of one room.

• The google maps activity marks the home of the

user

• The bottom sheet in each room lists the appliances

and refresh button

VIII. Hardware Requirements

i) Electronics

• Arduino UNO

• Raspberry Pi-3 Model B

• PIR sensors


92

• Smoke sensor (MQ 2)

• LDR sensor

• DHT-11 sensor

• ESP8266 Wi-Fi module

• Solderless Breadboard Half Size

• Eight Channel 12V Relay Board

• Resistor(s) 10k ohm• LEDs

• Jumper Cables

• Sample Phone

IX. Software Requirements

i) Electronics:• Python IDE

• Arduino IDE

ii) Android:• Android Studio IDE

• Android Studio SDK

X. Conclusion

This project provides In-House Appliance Control and security along with Automobile IOT (Vehicle Communication) which makes living comfortable and at the same time easily accessible through smartphones. It provides user almost all rights to decide which makes it reliable as it always asks before taking a decision, which helps when there are necessary decisions to be taken and they can be taken fast in case of an emergency.

XI. Future Scope

The Raspberry Pi being a really compact processor which has excellent computing power for its size. With daily research and development in technologies and various portable devices in those devices may be one day the raspberry might also be used as it has multiple GPIO pins which can be programmed and used to interface various devices in the real world and can be controlled with a program in Python.

XII. REFRENCES

1. https://www.irjet.net/archives/V3/i3/IRJET-V3I3133.pdf2. https://www.irjet.net/archives/V3/i4/IRJET-V3I4265.pdf3. http://inpressco.com/wp-content/uploads/2016/05/Paper1.750-

754.pdf 4. http://www.ijrat.org/downloads/ncpci2016/ncpci-45.pdf 5. http://www.jncet.org/Manuscripts%5CVolume-6%5CIssue-

4%5CVol-6-issue-4-M-05.pdf 6. http://ijitech.org/uploads/623514IJIT8573-11.pdf 7. https://www.kaaproject.org/automotive/ 8. https://blog.atlasrfidstore.com/internet-of-things-and-rfid9. AmulJadhav, S. Anand, NileshDhangare, K.S. Wagh “Universal

Mobile Application Development (UMAD) On Home Automation” https://www.arduino.cc/en/main/howto

10. Gayatri Kulkarni, Priyanka Gode, JadiPratap Reddy and MadhuraDeshmukh (2015), Android based smart home system, International Journal of Current Engineering and Technology, vol. 5, pp. 1022-1025. Pierre Raufast (2013), Raspberry Pi Open

CV and camera board, https://thinkrpi.wordpress.com/opencv-andpicamera-board/

11. Lady ada (2014), PIR motion sensors, Pyroelectric (Passive) InfraRed Sensors, https://learn.adafruit.com/pir-passiveinfrared-proximity-motion-sensor/overview

12. Rakesh Ron (2013), L293D Motor Driver IC, http://www.rakeshmondal.info/l293d-motor driver. Jason Barnett (2014), Controlling DC Motors Using Python with a Raspberry Pi, http://computers.tutsplus.com/tutorials/controlling-dcmotors-using-python-with-a-raspberry-pi--cms-20051


93

A New Variant of bat algorithm and Clustering

Approach for optimization problems

Savita Khatri

Department of Computer Science and Engineering, BMIET,

Sonepat, India

[email protected]

Neeraj Dahiya


SRM University, Delhi-NCR Campus, India

[email protected]

Abstract— this paper proposes a new variant of bat algorithm

for optimization problems for solving global and continuous

optimization problems to expand the weakness of BAT. The

planned algorithm uses the genetic bat tactics for improving the

search and to overcome the deficiency, directional echolocation is

introduced to the standard bat algorithm to enhance its

exploration and exploitation capabilities. The outcomes are taken

on six benchmark optimization problems. From upshots, it is

detected that the planned algorithm delivers more augmented

outcomes in contrast to same class of algorithms. The ordinary

test outcomes display the supremacy of the turning bat

algorithm.

Keywords— Bio-inspired algorithm, Optimization, exploration

and exploitation and echolocation

I. INTRODUCTION

Optimization can be an active research area for several decades and provides robust and viable solutions for complex real-world optimizations problems. These problems are complex and take lot of efforts to find optimal solution duet to increasing dimensionality, differentiability, multi-modality and rotation characteristics. So, lot of research has been carried out in this direction to design a real-time numerical optimizer for developing more accurate, fast and computationally efficient optimization algorithms. Clustering is an unsupervised technique which can be applied to understand the organization of data. The basic principle of clustering is to partition a set of objects into a set of clusters such that the objects within a cluster share more similar characteristics in comparison to the other clusters. A pre-specified criterion has been used to measure the similarity between the objects. In clustering, there is no need to train the data, it only deals with the internal structure of data and used a similarity criterion to group the objects into different clusters. Due to this, it is also known as unsupervised classification technique. Clustering has proven its importance in many applications successfully

In literature, Some of our Population-based algorithms, such as differential evolution (DE), evolutionary strategies (ES), genetic algorithm (GA), and particle swarm optimization (PSO), have been extensively used to solve such problems Researchers have proposed various techniques to recover these defective elements method [1], conjugate gradient based method [2, 3], applying genetic algorithm (GA) [4, 5], with the hybridization of GA and fast fourier transform (FFT) [6], applying an adaptive neuronal system [7], with simulated

annealing (SA) [8, 9], particle swarm optimization (PSO) [10–12], and firefly algorithm (FA) [13, 14].

The rest of paper is organized as follows: the section 2 describes the related work in field of bat algorithm. Section 3 gives the introduction of bat algorithm. The proposed new variant of BAT algorithm is illustrated in section 4. Section 5 describes the results of proposed BAT algorithm using benchmark optimization functions and the whole work is concluded into section 6.

II. RELATED WORK

The standard bat algorithm has been proven to be a very powerful optimization tool and Dao et al. (Dao, Pan, Nguyen, Chu, & Shieh, 2014) developed a compact version of the bat algorithm (CoBA), addressing to the hardware devices with limited resources such as the memory size or low price equipment. The bat population is replaced with a probability vector updated based on a single computation. These lead to an algorithm functioning with a modest memory usage. Results show that the CBSO performances are as good as the standard BA despite its modest memory usage. The enhanced bat algorithm (EnBA) proposed in (Yilmaz & Küçüksille, 2015) has been developed through three different methods. An inertia factor has been proposed to balance the search capabilities during the optimization process depending on the requirement of BA. Experimental results reveal that the proposed improvements make the BAT algorithm more effective and significant one for solving global optimization problems. To avoid the premature convergence and preserve the population diversity especially for global optimization problems, Zou et al., have described an improved BAT algorithm based on dynamic group strategy [21]. Further, it is also found that a quantum behaved learning scheme is also induced into learner phase of BAT algorithm for helping to maintain the population diversity. The feasibility of the proposed algorithm is evaluated on eighteen benchmark numerical functions and results reveal that the proposed algorithm is one of the effective and efficient algorithms for solving global optimization problems. The performance of BAT is investigated on twenty benchmark functions and it is found that the BATexhibits better performance than other algorithm being compared.


94

III. BAT ALGORITHM

The standard Bat algorithm is inspired by the echolocation process of bats. By noticing the conduct and features of the micro-bats, Yang (2010) planned the standard Bat in agreement to three main features of the echolocation process of the micro-bats. The recycled venerated rules in BAT are:

Algorithm 1: The standard bat algorithm.

Step1: Define the objective function

Step2: Initialize the bat population

Step3: Define frequencies

Step4: Initialize and loudness

Step5: While (pulse rate ≤ pulse rate max)

Step6: Adjust frequency and Update velocities

Step7: Update locations/solutions

Step8: Select a solution among the best solutions

Step9: Generate a local solution around the selected best

solution

Step10: Generate a new solution by flying randomly

Step11: Accept the new solutions

Step12: Rank the bats and find the current best

Step13: Output results for post-processing

IV. PROPOSED TLBO ALGORITHM

The detailed description of proposed BAT algorithm is given in Algorithm 2. The main steps of proposed algorithm are summarized as below.

V. RESULTS.

This section describes the results of proposed BAT algorithm

with well-known global optimization problems. The proposed

algorithm is implemented in Matlab 2010 (a) environment

using window based operating system having core i7 processor

and 8 GB RAM. The results are taken on average of 90

independent runs and evaluated using mean and standard

deviation parameters. The mean parameter shows the

efficiency of algorithms, whereas standard deviation parameter

defines robustness of the algorithm. The performance of

proposed algorithm is compared with some other algorithms

such as PSO, GA and BAT Table 1 depicts the various

unimodal and multi-modal problems used for experimentation.

TABLE I. THE USED TEST FUNCTIONS FOR EXPERIMENTATION

Table 2 demonstrates the results of proposed algorithm and

other algorithm like PSO, GA, bat and proposed bat. The

performance of the proposed algorithm is measures against

the six well-defined global optimization functions. These

functions are widely adopted to check the performance of

newly developed algorithms The experimental results reveal

that the proposed algorithm provides more optimized and

better results in comparison to existing algorithm. Figs. 1 and

2 show the convergence pattern of proposed bat and original

bat algorithm using Ackley and step functions. From these, it

is also stated that the convergence of the bat algorithm is

significantly improved.


95

TABLE II. COMPARISON OF RESULTS OF PROPOSED BAT AND OTHER EXISTING ALGORITHM ON DIFFERENT NUMERICAL FUNCTIONS

Algorithm Parameter

Function

F1 F2 F3 F4 F5 F6

PSO average 0.0004 26.8437 1.3582 0.1045 0.0721 0.0005

std. 0.0025 17.2699 1.6975 0.0562 0.0506 0.0028

GA average 0.8378 46.2842 1.9000 0.9613 0.89934 0.7879

std. 0.6138 22.6284 1.6921 0.0548 0.3216 0.5645

BAT average 01.0000 27.6567 1.97E−12 0.0098 4.55E−16 2.76E−89

std. 01.0000 2.94E−02 7.66E−12 0.0090 9.32E−32 5.38E−09

Proposed

BAT

average 0.0000 23.9648

1.0000 0.0099 4.39

E−15 3.58E−26

std. 0.0000 3.26E−01 0.0000 0.0000 7.53E−21 5.99E−26

Fig. 1. shows the BAT and Proposed BAT algorithm for Ackley function

Fig. 2. shows the convergence of BAT and Proposed BAT algorithm for Step function

0 50 100 150 200 250 300 10 -14

10 -12

10 -10

10 -8

10 -6

10 -4

10 -2

10 0

10 2

Iteration

Cos

t

Ackley

Proposed BAT

BAT

0 50 100 150 200 250 300 0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5 x 10

5

Iteration

Cost

Step

Proposed BAT

BAT


96

VI. CONCLUSION

In this work, a new variant of BAT algorithm is presented for

solving the global optimization problems. Various operators is

adopted to improve the searching capability and convergence

rate of BAT algorithm. In this work, six benchmark global

optimization functions are used to evaluate the performance of

the proposed algorithm. The results depict that the proposed

BAT algorithm obtains better performance among all other

being compared and produced quality results.

REFERENCES

[1] Stutzle, T. G. ‘‘Local Search Algorithms for Combinatorial Problems:Analysis,Improvements, and New Applications.’’ PhD Thesis, Technical University of Darmstadt, Darmstadt, Germany, 1998.

[2] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing., Science. 220, (1983), 671–680.

[3] Wang, F., Qiu, Y., & Bai, Y. (2005). A new hybrid NM method and particle swarm algorithm for multimodal function optimization. Lecture Notes in Computer Science, 3646, 497–508.

[4] Wang, H. S., Che, Z. H., & Wu, C. (2010). Using analytic hierarchy process and particle swarm optimization algorithm for evaluating product plans. Expert Systems with Applications, 37, 1023–1034.

[5] Yannis, M., Magdalene, M., Michael, D., & Constantin, Z. (2009). Ant colony and particle swarm optimization for financial classification problems. Expert Systems with Applications, 36, 10604–10611.

[6] Zeng, Y., Zhu, C. A., Shen, L. G., & Qi, J. Y. (2007). Discrete optimization problem of machine layout based on swarm intelligence algorithm. Computer Integrated Manufacturing Systems, 13, 541–552.

[7] Kumar Y, Gupta S. and Sahoo G, A Clustering Approach Based on Charged Particles, International Journal of Software Engineering and ItsApplications, Vol. 10, No. 3 (2016), pp. 9-28.

[8] Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput-Aided Des 43(3):303–315

[9] Sahoo AJ, Kumar Y (2014) Advances in signal processing and intelligent recognition systems, Modified teacher learning based optimization method for data clustering Springer, Berlin, pp. 429-437.

[10] A. Sadollah, A. Bahreininejad, H. Eskandar, M. Hamdi, Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems, Appl. Soft Comput. J. 13 (2013), 2592–2612. doi:10.1016/j.asoc.2012.11.026.

[11] Coelho L, Mariani VC. Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert SystAppl 2008;34:1905–13.

[12] Talatahari S, Farahmand Azar B, Sheikholeslami R, Gandomi A (2012) Imperialist competitive algorithm combined with chaos for global optimization. Commun Nonlinear Sci Numer Simul 17:1312–1319.

[13] Z.L. Yang, K. Li, Q. Niu, et al., A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads, J. Mod. Power Syst. Clean Energy 2 (4) (2014) 298–307.

[14] C.H. Chen, Group leader dominated teaching–learning based optimization, in:International Conference on Parallel and Distributed Computing, Applications and Technologies, 2013, pp. 304–308.

[15] Kuppusamy K (2005) Hybrid algorithm based on EP and LP for security constrained economic dispatch problem, Electric Power SystemsResearch, 76(1–3), pp. 77–85

[16] M. Sonmez, Discrete optimum design of truss structures using artificialbee colony algorithm, Structural and Multidisciplinary Optimization 43 (1) (2011) 85–97Rao, R.V. and Patel, V., 2013. An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), pp.710-720.

[17] Satapathy, S.C. and Naik, A., 2014. Modified Teaching–Learning-Based Optimization algorithm for global numerical optimization—A comparative study. Swarm and Evolutionary Computation, 16, pp.28-37.

[18] Huang, J., Gao, L. and Li, X., 2015. An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Applied Soft Computing, 36, pp.349-356.

[19] Zou, F., Wang, L., Hei, X. and Chen, D., 2015. Teaching–learning-based optimization with learning experience of other learners and its application. Applied Soft Computing, 37, pp.725-736.

[20] Ouyang, H.B., Gao, L.Q., Kong, X.Y., Zou, D.X. and Li, S., 2015. Teaching-learning based optimization with global crossover for global optimization problems. Applied Mathematics and Computation, 265, pp.533-556.

[21] Zou, F., Wang, L., Hei, X., Chen, D. and Yang, D., 2014. Teaching–learning-based optimization with dynamic group strategy for globaloptimization. Information Sciences, 273, pp.112-131.

[22] Lim, W.H. and Isa, N.A.M., 2014. An adaptive two-layer particle swarmoptimization with elitist learning strategy. Information Sciences, 273, pp.49-72.

[23] Sahoo, G., 2015. A two-step artificial bee colony algorithm for clustering. Neural Computing and Applications, pp.1-15


97

Documents

CICT 2017, International Conference @ BMIET, SONEPAT, NCR