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MINISTRY OF EDUCATION AND TRAINING

HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY

Nguyen Thi Thanh Nga

EFFICIENT DATA COMMUNICATION FOR WIRELESS SENSOR NETWORK

BASED ON DATA CORRELATION

Major: Computer Engineering Code No.: 9480106

COMPUTER ENGINEERING DISSERTATION

SUPERVISORS:

1 Dr Nguyen Kim Khanh

2 Assoc Prof Ngo Hong Son

Hanoi - 2018

COMMITMENT

I assure that this is my own research All the data and results in the thesis are completely true, were agreed to use in this thesis by co-authors This research hasn’t been published by other authors than me

Hanoi, 17thDecemberber 2018

Assoc Prof Ngo Hong Son

ACKNOWLEDGMENTS

This Ph.D thesis has been carried out at the Department of Computer Engineering, School of Information and Communication Technology, Hanoi University of Science and Technology The research has been completed under supervisions of Dr Nguyen Kim Khanh and Associate Prof Dr Ngo Hong Son

Firstly, I would like to express my sincere gratitude to my advisors Dr Nguyen Kim Khanh and Associate Prof Dr Ngo Hong Son for their continuous support of

my Ph.D study and related research, for their patience, motivation, and immense knowledge Their valuable guidance, unceasing encouragement and supports have helped me during all the time of research and writing out of this thesis

Besides my advisors, I would like to thank all my colleagues in the Department

of Computer Engineering for their insightful comments, encouragement and for the hard questions which incented me to widen my research from various perspectives I would like to express my appreciation to Prof Dr Trinh Van Loan for his time and patient helping me to correct the whole thesis as well as value comments during the process of pursuing my doctorate degree

I want to thank all my colleagues in the School of Information and Communication Technology, for their supports and helps in my work

I gratefully acknowledge the receipt of grants from 911 project of Ministry of Education and Training which enabled me to carry out this research

Finally, I would like to thank my family, my sisters, my father and mother, my husband and two children for their endless love, encouraging and unconditional supporting me continuously and throughout writing this thesis

Nguyen Thi Thanh Nga

TABLE OF CONTENT

COMMITMENT 2

ACKNOWLEDGMENTS 3

TABLE OF CONTENT 4

LIST OF ABBREVIATIONS 7

LIST OF FIGURES 8

LIST OF TABLES 11

PREFACE 13

1 INTRODUCTION 16

Overviews 16

Energy conservation in WSNs 19

1.2.1 Radio optimization 19

1.2.2 Sleep/wake-up schemes 20

1.2.3 Energy efficient routing 20

1.2.4 Data reduction 21

1.2.5 Charging solution 22

Data correlation and energy conservation in WSNs 23

Problem statements and contributions 24

2 CORRELATION IN WIRELESS SENSOR NETWORK 25

Correlation model survey 25

Information entropy theory 31

2.2.1 Overview 31

2.2.2 Entropy concept 32

2.2.3 Joint entropy 32

Correlation and entropy 33

2.3.1 Correlation of two variables 33

2.3.1.1 Mutual information 33

2.3.1.2 Entropy correlation coefficient 34

2.3.2 Correlation of more than two variables 36

Conclusions 38

3 ENTROPY-BASED CORRELATION CLUSTERING 39

Joint entropy estimation 39

3.1.1 Determining the upper bound of joint entropy 39

3.1.2 Determining the lower bound of joint entropy 42

3.1.3 Validating entropy estimation 44

Correlation region and correlation clustering algorithm 47

3.2.1 Estimated joint entropy and correlation 47

3.2.2 Correlation region definition 50

3.2.3 Correlation clustering algorithm 52

3.2.4 Validation 54

Conclusions 56

4 ENTROPY CORRELATION BASED DATA AGGREGATIONS 57

Compression aggregation 57

4.1.1 Comparison of compression schemes 57

4.1.2 Compression based routing scheme in a correlated region 60

4.1.2.1 1-D analysis 61

4.1.2.2 2-D analysis 65

4.1.2.3 General topology model analysis 69

4.1.3 Optimal routing scheme in correlation networks 71

Representative aggregation 72

4.2.1 Distortion function 72

4.2.2 Number of representative nodes 73

4.2.3 Representative node selection 76

4.2.4 Practical validation 77

Conclusions 80

PROTOCOL (ECODA) 82

Network model 82

Radio model 83

Outline of ECODA 84

5.3.1 Set-up phase 85

5.3.2 Steady-state phase 87

Performance evaluation 87

5.4.1 Simulation models 87

5.4.1.1 Simulation parameters 88

5.4.1.2 Simulation setups 89

5.4.1.3 Dissipated energy calculation 90

5.4.2 Simulation results and discussions 92

5.4.2.1 Compression aggregation-based routing protocol 92

5.4.2.2 Representative aggregation-based routing protocol 97

5.4.3 Evaluations and comparison 100

5.4.3.1 The case of ECODA with compression aggregation 101

5.4.3.2 The case of ECODA with representative aggregation 106

Conclusions 107

6 CONCLUSIONS AND FUTURE STUDY 109

Summary of Contributions 109

Limitations 110

Future work 111

PUBLICATION LIST 112

REFERENCES 113

APPENDIX 125

ECODA Entropy COrrelation clustering for Data Aggregation

LEACH-C Low Energy Adaptive Clustering Hierarchy- Centralized

LIST OF FIGURES

Figure 1.1 Wireless Sensor Network 16Figure 1.2 Wireless Sensor Network Applications 17Figure 2.1 The layout of sensor nodes in an environment with two different conditions area 30Figure 2.2 The relations between entropies, joint entropy, and mutual information 33Figure 2.3 Relation between correlation and joint entropy 37Figure 3.1 Joint entropy calculation principle 42Figure 3.2 Sensor layout in Intel Berkeley Research Lab 45Figure 3.3 Practical, upper bound and lower bound joint entropy (JE) of subsets of the dataset 1 46Figure 3.4 Estimated joint entropy with different values of entropy correlation coefficients using upper bound function (with Hmax = 2[bits]) 48Figure 3.5 Estimated joint entropy (by upper bound) and practical joint entropy of dataset 1 49Figure 3.6 Correlation-based clustering algorithm 52Figure 3.7 Temperature data measured at 11 nodes in the dataset 1 53Figure 3.8 Derivative of estimated joint entropy and calculated the joint entropy of the selected group 55Figure 4.1 Routing paths for three schemes: (a) DSC, (b) RDC, and (c) CDR [122] 59Figure 4.2 Energy consumptions for the DSC, RDC and CDR schemes respectively

to entropy correlation coefficients 60Figure 4.3 Routing pattern of 1-D network 61Figure 4.4 Total bit-hop cost Esthat corresponds to cluster size with different values

of entropy correlation coefficient in the case of 1-D with compression along SPT to the cluster head 63Figure 4.5 Total bit-hop cost Esthat corresponds to cluster size with different values

of entropy correlation coefficient in the case of 1-D with compression at the cluster head only 64Figure 4.6 Routing pattern of the 2-D network [122] 65

Figure 4.7 Total bit-hop cost Esthat corresponds to cluster size with different values

of entropy correlation coefficient in the case of 2-D with compression along

SPT to the cluster head 67

Figure 4.8 Total bit-hop cost Esthat corresponds to cluster size with different values of entropy correlation coefficient in the case of 2-D with compression at the cluster head only 68

Figure 4.9 Illustration of clustering for a general topology model 69

Figure 4.10 Total transmission cost that corresponds to cluster size with different values of entropy correlation coefficient with compression along SPT to the cluster head 70

Figure 4.11 Total transmission cost respectively to cluster size with different values of entropy correlation coefficient with compression at the cluster head only 71 Figure 4.12 The relation between distortion and the number of representative nodes with N = 10 74

Figure 4.13 The relation between distortion and the number of representative nodes with N = 15 74

Figure 4.14 The relation between distortion and the number of representative nodes with N = 20 75

Figure 4.15 Maximizing obtained information based representative node selection algorithm 77

Figure 5.1 Radio energy dissipation model 83

Figure 5.2 Time scheduling for one round 85

Figure 5.3 Sensor node distribution in the 200mx200m sensing area 88

Figure 5.4 Routing path of compression-based routing protocol 89

Figure 5.5 Total energy in each round in case of compression along SPT to the CH 93

Figure 5.6 Number of alive nodes in each round in case of compression along SPT to the CH 94

Figure 5.7 Total energy in each round in case of compression at the CH only 96

Figure 5.8 Number of alive nodes in each round in case of compression at the CH only 97

Figure 5.9 Total energy in each round in case of representative aggregation with compression with 16 correlation clusters 98Figure 5.10 Number of alive nodes in each round in case of representative aggregation with compression with 16 correlation clusters 98Figure 5.11 Total energy in each round in the case of representative aggregation without compression with 16 correlation clusters 99Figure 5.12 Number of alive nodes in each round in the case of representative aggregation without compression with 16 correlation clusters 100Figure 5.13 Total energy comparison between distance-based protocol and ECODA with compression aggregation in the case of 16 correlation clusters 101Figure 5.14 Total energy comparison between distance-based protocol and ECODA with compression aggregation in the case of 16 correlation clusters 102Figure 5.15 Total energy comparison between distance-based protocol and ECODA with compression aggregation in the case of 8 correlation clusters 102Figure 5.16 Total energy comparison between distance-based protocol and ECODA with compression aggregation in the case of 8 correlation clusters 103Figure 5.17 Total energy comparison between distance-based protocol and ECODA with compression aggregation in the case of 4 correlation clusters 104Figure 5.18 Total energy comparison distance-based protocol and ECODA with compression aggregation in the case of 4 correlation clusters 105Figure 5.19 Total energy comparison between distance-based protocol and ECODA with representative aggregation in the case of 16 correlation clusters 106Figure 5.20 Number of alive nodes comparison between distance-based protocol and ECODA with representative aggregation in the case of 16 correlation clusters 107

LIST OF TABLES

Table 3.1 Node’s entropy of the dataset 1 46

Table 3.2 Entropy correlation coefficient of each pair from the dataset 1 47

Table 3.3 Practical, upper bound and lower bound joint entropy (JE) of subsets of the dataset 1 49

Table 3.4 Clustering results of 48 nodes 53

Table 4.1 Number of representative nodes with distortion D = 0.05 76

Table 4.2 Number of representative nodes with distortion D = 0.1 76

Table 4.3 Number of representative nodes with distortion D = 0.15 76

Table 4.4 Selection of representative nodes and the actual distortion based on theoretical calculation (dataset 1 with N = 11 nodes) 78

Table 4.5 Selection of representative nodes and the actual distortion based on practical calculation (dataset 1 with N = 11 nodes) 78

Table 4.6 Entropy values of 10 nodes in the correlation region (dataset 2 with N = 10 nodes) 78

Table 4.7 Selection of representative nodes and the actual distortion based on theoretical calculation (dataset 2 with N = 10 nodes) 79

Table 4.8 Selection of representative nodes and the actual distortion based on practical calculation (dataset 2 with N = 10 nodes) 80

Table 5.1 Simulation parameters 88

Table 5.2 Simulation results in case of compression along SPT to the CH 94

Table 5.3 Simulation results in case of compression at the CH only 95

Table 5.4 Simulation results in the case of representative aggregation with compression at the CH 97

Table 5.5 Simulation results in the case of representative aggregation without compression at the CH 100

Table 5.6 Comparison between distance-based protocol and ECODA with compression aggregation in the case of 16 correlation clusters 103

Table 5.7 Comparison between distance-based in the case of 8 correlation clusters 104

Table 5.8 Comparison between distance-based protocol and ECODA with compression aggregation in the case of 4 correlation clusters 105Table 5.9 Comparison between distance-based protocol and ECODA with representative aggregation in the case of 16 correlation clusters 106

PREFACE

Wireless Sensor Network (WSN) is the collection of sensor nodes which cooperatively monitor surrounding phenomena over large physical areas The advances in the integration of micro-electro-mechanical systems and digital electronics with the development of wireless communications have enabled the wide deployment of WSNs Sensor nodes in WSNs have been equipped with various sensing capabilities in space and time and higher processing capacities can satisfy requests from various modern applications Because of low-cost, small-in-size and no-replace battery powered characteristics of sensor nodes, energy conservation is commonly recognized as the key challenge in designing and operating the networks

In typical WSNs applications, sensors are required for spatially dense deployment to achieve satisfactory coverage As a result, multiple sensors will record information about a single event in the sensing field, i.e sensed data are correlated with each other The existence of correlation characteristic can bring many significant potential advantages for the development of efficient communication protocols well-suited to the WSNs paradigm For example, due to the correlation degree, data in a correlated region can be compressed with a high ratio to reduce the amount of sent data for saving dissipated energy Even with high enough correlation, it may not be necessary for every sensor node in a correlation group to transmit its data to the base station Instead, a smaller number of sensor measurements (representation) might be adequate to communicate the event features to the base station within a certain reliability/fidelity level

From this point of view, various researches have focused on discovering and exploiting the correlation of sensed data in WSNs At the beginning of these researches, the traditional probability and statistic theory have been used to describe the correlation among data Nevertheless, these approaches limited the correlation as

a linear relation that may not appropriate for general, nonlinear cases in practice Therefore, the information entropy approach has been considered to obtain the generality However, most of the research approach, using traditional probability -statistic theory or information entropy theory, considered the correlation as a distance-dependence feature In general, the correlation of data may be independent

of external factors such as sensor location and environmental conditions and thus, so

it is better to concentrate on the information contained in the data itself rather than considering only attribute meta-data such as location and time

This thesis concentrates to discover and exploit the general correlation in WSNs using information entropy theory to look at the sensed data itself At first, a

novel distance-independence entropy-based correlation model for describing correlation characteristics in a wireless sensor network is proposed From this entropy correlation model, an energy efficient routing protocol with correlation-based data aggregation will be developed

To discover the correlation property, at first, an estimation of joint entropy for

a data group is established From this estimation, a definition of the correlation group

is proposed and then the correlation model that is used to calculate the joint entropy

of the correlation data group is developed To exploit the correlation characteristic,two main data aggregation schemes are analyzed and evaluated using the proposed correlation model At the end, these schemes are used to develop data aggregation routing protocols Using the proposed routing protocols, the transferred data in the network is reduced so that the dissipated energy is decreased

The thesis structure is as follows:

Chapter 1: Introduction

This chapter reviews the introduction of WSNs, energy conservation schemes, and data correlation problems The main contributions of the thesis are also presented shortly in this chapter

Chapter 2: Correlation in Wireless Sensor Network

This chapter presents the survey of correlation model in WSNs and the correlation through the point of view of information entropy Then, the idea to establish a new correlation model is described

Chapter 3: Entropy-based Correlation Clustering

Based on the analyzed factors in chapter 2, we propose the approximated estimation of joint entropy From this approximation method, we define the correlation region and propose the correlation clustering scheme We also verify the validation of the proposed estimation and correlation clustering scheme in this chapter

Chapter 4: Entropy-based Data Aggregations

In this chapter, we exploit the advantages of using data correlation by data aggregation using entropy correlation including entropy-based representative aggregation and entropy-based data compression

In entropy-based representative aggregation, the distortion of data in the groupwhile some nodes are put into sleep state is evaluated using the proposed correlation model From this evaluation, the number of representative nodes in a group is decided

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