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GRID BASED ROUTING PROTOCOL IN WIRELESS SENSOR NETWORK Thesis Submitted In Partial Fulfillment of the Requirements for the Degree of M.TECH. (I.T.) in Software Engineering of JIS College of Engineering BY Jahiruddin Ahamed Univ. Roll Number: 12311410013 Registration Number: 101230410031 of 2010-2011 Under The Guidance of Soumyabrata Saha Department of Information Technology
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GRID BASED ROUTING PROTOCOL IN WIRELESS SENSOR NETWORK

Thesis Submitted In Partial Fulfillment of the Requirements for the Degree of M.TECH. (I.T.) in Software Engineering of

JIS College of Engineering

BY

Jahiruddin Ahamed Univ. Roll Number: 12311410013

Registration Number: 101230410031 of 2010-2011

Under The Guidance ofSoumyabrata Saha

Department of Information Technology

JIS College of EngineeringBlock-a, Phase-III, Kalyani, Nadia, Pin-741235

West Bengal, India

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CERTIFICATE

This is to recognize and appreciate that

Jahiruddin Ahamed , Roll Number: 12311410013 ,

Student of the Department of Information

Technology, has successfully completed his

dissertation “Grid Based Routing Protocol” which

is worth of acceptance for the partial fulfillment

of his degree of Master of Technology in

Information Technology (Software Engineering) in

the year 2012. The project, that has the span

throughout his final year of study, has got the

expected involvement from him and the kind of

devotion, he has shown, will add value to his

merit.

Project Supervisor Head of the Department

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Principal Director

ACKNOWLEDGEMENT

I hereby declare that this thesis contains literature survey and original research work by the undersigned, as part of requirements of the Degree of M.Tech. (IT) in Software Engineering. All information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all materials and results that are not original to this work.

The analysis of the project work wishes to express my gratitude to Mr. Soumyabrata Saha for allowing the degree attitude and providing effective guidance in development of this project work. His conscription of the topic and all the helpful hints, he provided, contributed greatly to successful development of this work, without being pedagogic and overbearing influence.

I also express my sincere gratitude to Mr. Somsubhra Gupta, Head of the Department of Information Technology of JIS College of Engineering and all the respected faculty members of Department of IT for giving the scope of successfully carrying out the project work.

Finally, I take this opportunity to thank to Dr. U. Banerjee, Principal of JIS College of Engineering and Dr.A.Guha, Director of JIS College of Engineering for giving us the scope of carrying out the project work.

Date: …………………………………….………………………………….

Jahiruddin AhamedM.TECH (IT) in Software Engineering

2nd YEAR/4th SEMESTERUniv Roll--12311410013

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Table of ContentsTable of Contents

PREFACE…………………………………………………….

ABSTRACT................................................................................

CHAPTER 1 WIRELESS SENSOR NETWORK

1.1 INTRODUCTION…………………………………………….

CHAPTER 2 BACKGROUND DETAILS1.1 WIRELESS SENSOR NETWORK……………………………….

1.1.1 EVOLUTION OF SENSOR NETWORK………1.1.2 WIRELESS SENSOR NETWORK MODEL….1.1.3 SENSOR NODE………………………………1.1.4 WSN COMMUNICATION ARCHITECTURE…1.1.5 CHARCTERISTICS OF WSN

1.2 ROUTING 1.2.1 ROUTING CHALENGES AND DESIGN ISSUE….1.2.2 ROUTING OBJECTIVES…………………………..1.2.3 ROUTING TECNIQUES……………………………

CHAPTER 3 RELATED WORK1.1 LEACH…………………………………………….

1.1.1 LEACH-SUB-CH 1.1.2 ASN

1.2 PEGASIS………………………………………….1.3 TEEN & APTEEN…………………………………1.4 MuMHR ………………………………………………..1.5 GBR……………………………………………….

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1.6 GAF…………………………………………1.7 GEAR…………………………………………….1.8 HGMR…………………………………………...1.9 TTDD…………………………………………

CHAPTER 4 SYSTEM MODEL AND DESIGN1.1 PROPOSED NEW PROTCOL DESIGN………………………………1.2 GRID PARTITION…………………………………………………..1.3 CLUSTER HEAD SELECTION AND ALGORITHM………………..1.4 EIGHT DIRECTION WAY FLOODING AND ALGORITHM………1.5 LOCATION AWARE FLOODING……………………………………

CHAPTER 5 RESULT AND ANALYSIS 1.1 EVALUATION ENVIRONMENT

1.1.1 OVERVIEW OF THE MATLAB ENVIRONMENT1.1.2 THE MATLAB SYSTEM1.1.3 MATLAB DOCUMENTATION

1.2 SIMULATION WITH MATLAB………………………………………1.2.1 SIMULATION

PARAMETER…………………………………..1.2.2 OUTPUT………………………………………….

CHAPTER 6 CONCLUSION

REFERENCES

APPENDIX- ASOURCE CODE……………………………….. …………

APPENDIX- BLIST OF FIGURE AND LIST OF TABLE………….. ….

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Face  Recognition: An Introduction

Face

Face Recognition 

Face is the  most common biometric used by humans

Applications range from static, mug-shot verification to a dynamic, uncontrolled face identification in a cluttered background

Challenges: o automatically locate the face o recognize the face from a general

view point under different illumination conditions, facial expressions, and aging effects

Authentication vs Identification 

Face Authentication/Verification  (1:1 matching)

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Face Identification/Recognition  (1:N matching)

www.viisage.com 

 Access Control 

Applications 

www.visionics.com

 Video Surveillance (On-line or off-line) 

Applications 

Face Scan at Airports 

www.facesnap.de

Why is Face Recognition Hard?  

Many faces of Madonna

Face Recognition Difficulties 

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Identify similar  faces (inter-class similarity)

Accommodate intra-class variability due to:

o head poseo illumination conditionso expressionso facial accessorieso aging effects

 

Cartoon faces 

Inter-class Similarity 

Different persons  may have very similar appearance

 

Twins  

Father and son  

www.marykateandashley.com 

news.bbc.co.uk/hi/english/in_depth/americas/2000/us_elections

Intra-class Variability 

Faces with intra-subject  variations in pose, illumination, expression, 

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accessories, color, occlusions, and brightness 

Sketch of a Pattern Recognition Architecture 

Feature

Extraction 

Classification 

Image

(window) 

Object

Identity 

Feature 

Vector

Example: Face Detection 

Scan window over image 

Classify window as either:o Faceo Non-face

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Classifier  

Window 

Face 

Non-face

Detection Test Sets

Profile views 

Schneiderman’s

Test set

Face Detection: Experimental Results  

 Test sets: two CMU benchmark data sets

Test set 1: 125 images with 483 faces

Test set 2: 20 images with 136 faces 

[See also work by Viola & Jones, Rehg, more recent

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by Schneiderman]

Example: Finding skin Non-parametric Representation of CCD 

Skin has a very small range of (intensity independent) colors, and little texture

o Compute an intensity-independent color measure, check if color is in this range, check if there is little texture (median filter)

o See this as a classifier - we can set up the tests by hand, or learn them.

o get class conditional densities (histograms), priors from data (counting)

 

Classifier is

Figure from “Statistical color models with application to skin detection,” M.J. Jones and J. Rehg, Proc. Computer Vision and Pattern Recognition, 1999 copyright 1999, IEEE

Face Detection

Face Detection Algorithm 

Face  Localization 

Lighting  Compensation 

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Skin  Color Detection 

Color  Space Transformation 

Variance-based  Segmentation  

Connected  Component &

Grouping 

Face  Boundary Detection 

Verifying/  Weighting

Eyes-Mouth  Triangles 

Eye/  Mouth Detection 

Facial  Feature Detection 

Input  Image 

Output  Image

Canon Powershot

Face Recognition: 2-D and 3-D  

  

2-D 

      

Face 

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Database 

Time

(video) 

  

2-D 

       

Recognition

Data 

3-D 

3-D 

Recognition

Comparison 

Prior  knowledge

of  face class

Pose-dependent 

Algorithms 

Pose-invariant 

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Pose-dependency 

Matching  features 

Appearance-based  (Holistic) 

-- Gordon et al., 1995 

Feature-based (Analytic) 

Hybrid 

Viewer-centered Images 

-- Lengagne et al., 1996 

-- Atick et al., 1996 

Object-centered Models 

-- Yan et al., 1996 

-- Zhao et al., 2000 

Face representation 

-- Zhang et al., 2000 

PCA, LDA 

LFA 

EGBM 

Taxonomy of Face Recognition

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Image as a Feature Vector 

Consider an n-pixel image to be a point in an n-dimensional space, x    Rn.

Each pixel value is a coordinate of x. 

3

Nearest Neighbor Classifier 

 

 { Rj } are set of training images.  

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R1 

R2 

I

Comments 

Sometimes called “Template Matching”

Variations on distance function (e.g. L1, robust distances)

Multiple templates per class- perhaps many training images per class.

Expensive to compute k distances, especially when each image is big (N dimensional).

May not generalize well to unseen examples of class.

Some solutions:

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o Bayesian classificationo Dimensionality reduction

Eigenfaces (Turk, Pentland, 91) -1 

Use Principle Component Analysis (PCA) to reduce the dimsionality

How do you construct Eigenspace? 

[      ]        [    ] 

[ x1 x2 x3 x4 x5 ] 

Construct data matrix by stacking vectorized images and then apply  Singular Value Decomposition (SVD)

Eigenfaces 

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Modelingo Given a collection of n labeled

training images,o Compute mean image and

covariance matrix.o Compute k Eigenvectors (note that

these are images) of covariance matrix corresponding to k largest Eigenvalues.

o Project the training images to the k-dimensional Eigenspace.

Recognitiono Given a test image, project to

Eigenspace.o Perform classification to the

projected training images.

Eigenfaces: Training Images 

[ Turk, Pentland 01

Eigenfaces 

Mean Image 

Basis Images

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Difficulties with PCA 

Projection may suppress important detail

o smallest variance directions may not be unimportant

Method does not take discriminative task into account

o typically, we wish to compute features that allow good discrimination

o not the same as largest variance

Fisherfaces: Class specific linear projection 

 An n-pixel image xRn can be projected to a low-dimensional feature space yRm by

 

y = Wx 

where W is an n by m matrix. 

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 Recognition is performed using nearest neighbor in Rm.

 

 How do we choose a good W?

PCA & Fisher’s Linear Discriminant  

Between-class scatter 

Within-class scatter 

Total scatter 

Whereo c is the number of classeso i is the mean of class i

o | i | is number of samples of i.. 

 

2

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PCA & Fisher’s Linear Discriminant  

PCA (Eigenfaces)   

    Maximizes projected total scatter 

Fisher’s Linear Discriminant    

    Maximizes ratio of projected between-class to projected within-class scatter 

PCA 

FLD

Four Fisherfaces From ORL Database

Eigenfaces and Fisherfaces

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PREFACE

The project on Grid based routing protocol for wireless sensor network. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy consumption is an essential design issues. Since wireless sensor network protocols are application specific, so the focus has been given to the routing protocols that might differ depending on the application and network architecture investigate deterministic node deployment for large scale WSNs under the following performance metrics: Coverage, energy consumption and packet transfer delay .Using a basic geometry I proposed square grid calculating optimal path, an Eight-Direction way that acts as a location service server. Eight direction way prevents intensive energy consumption at the border sensor nodes and thus provides energy balancing to all the sensor nodes and efficiently forwards (or relays) data from a source node to a sink with minimal delay path.

This report file is divided into various sections which are collectively organized into 6 chapters:

Chapter 1: Introduction gives the brief idea of wireless sensor network, Application area.

Chapter 2: Background Details of wireless sensor network, Evolution of sensor network, sensor node, architecture, protocol stack, routing design constraint, routing techniques description.

Chapter 3: Related Work: Hierarchical, flat, location, Qos based routing protocol.

Chapter 4: System Model and Design introduces the motivation, problem statement, design the protocol, algorithm.

Chapter 5: Analysis and simulation result introduces simulation parameter and cluster head selection and performance of the protocol.

Chapter 6 : Conclusion

References

APENDIX A

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APENDIX B

Abstract

Wireless Sensor Networks (WSNs) consist of thousands of tiny nodes having the capability of sensing, computation, and wireless communications. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy consumption is an essential design issues. Since wireless sensor network protocols are application specific, so the focus has been given to the routing protocols that might differ depending on the application and network architecture. In this paper, I present a survey of the state-of-the-art routing techniques in WSNs. Outline the design challenges for routing protocols in WSNs followed by a comprehensive survey of different routing techniques. Overall, the routing techniques are classified into three categories based on the underlying network structure: flat, hierarchical, and location-based routing. A proper node deployment scheme can reduce the complexity of problems in wsn as routing, data fusion communication overhead, packet delay, etc. In this thesis, I investigate deterministic node deployment for large scale WSNs under the following performance metrics: Coverage, energy consumption and packet transfer delay .Using a basic geometry I proposed square grid calculating optimal path, an eight direction way system that acts as a location service server. Eight directions way prevents intensive energy consumption at the border sensor nodes and thus provides energy balancing to all the sensor nodes. Then I propose a Location-aware flooding that efficiently forwards (or relays) data from a source node to a sink with minimal delay path. Results will be demonstrate that new proposed protocol not only provides an efficient and scalable location service, but also reduces the average data communication overhead in scenarios with multiple sinks and sources.

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Chapter 1

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1. Introduction

1.1 Introduction

In recent years, there have been major advances in the development of wireless sensors and IC process technology. Due to these advances, wireless sensor networks (WSNs) have been replacing traditional network technologies . These WSNs have a number of advantages over wired networks, such as ease of deployment, extended transmission range, and self-organization.

There are, however, a few inherent limitations to WSNs. These include low communication bandwidth, small storage capacity, limited computation resources, and limited device energy. In terms of energy, many researchers assume that all nodes in a sensor network are battery-driven. Because of this, energy is a very scarce resource in sensor networks. Therefore, energy efficiency is an important design issue in WSNs[7].

Currently, WSNs are used in various applications. Figure 1 shows a schematic of applications for WSNs. Among their many applications, they can be used in the scientific, commercial, medical, and military battlefield, industry control, traffic control, and ambient conditions detection areas and in smart homes.

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Figure 1 : Application for WSN

Data gathering is a typical operation in many WSN applications, and data aggregation in a hierarchical manner is widely used for prolonging network lifetime. Data aggregation can eliminate data redundancy and reduce the communication load. Hierarchical mechanisms (especially clustering algorithms) are helpful to reduce data latency and increase network scalability, and they have been extensively exploited in previous works.

In this thesis, I propose a Grid based routing protocol, called GBRP for data gathering in wireless sensor networks. In GBRP, a node with a high ratio of residual energy to the average residual energy of all the neighbor nodes in its cluster range will have a large probability to become the cluster head. This can better handle heterogeneous energy circumstances than existing clustering algorithms which elect the cluster head only based on a node’s own residual energy. After the cluster formation phase, GBRP data will be flooded packet into Eight direction way (EDW) over the set of cluster heads. Only the root node of this region can communicate with the sink node by single-hop communication. Because the energy consumed for all communications in in-network can be computed by the free space model, the energy will be extremely saved and thus leading to sensor network longevity. GBRP also utilizes a simple but efficient approach to solve the area coverage problem. With the increase in node density, this approach can guarantee that the network lifetime will be linear with the number of deployed nodes, which significantly outperforms the previous works designed for data gathering application.

The remainder of this thesis is organized as follows: Section 2 describe background details, Section 3 describes reviews related works. Section 4 presents the detailed design of GBRP. Section 5 reports the result of GBRP effectiveness and performance via simulations and a comparison made with other energy efficient protocol. Section 6 concludes the thesis.


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