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, 17th Decemberber 2018
SUPERVISORS

AUTHOR

Dr. Nguyen Kim Khanh

supporting me continuously and throughout writing this thesis.

Nguyen Thi Thanh Nga

3


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


2.3.1

Correlation of two variables................................................................. 33
2.3.1.1 Mutual information ............................................................. 33
2.3.1.2 Entropy correlation coefficient ........................................... 34

4


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

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

5


5 ENTROPY CORRELATION BASED DATA AGGREGATION
PROTOCOL (ECODA) ......................................................................................... 82

Conclusions ............................................................................................ 107

6

CONCLUSIONS AND FUTURE STUDY.................................................. 109
Summary of Contributions ..................................................................... 109
Limitations.............................................................................................. 110
Future work ............................................................................................ 111

PUBLICATION LIST.......................................................................................... 112
REFERENCES ..................................................................................................... 113
APPENDIX............................................................................................................ 125

6


LIST OF ABBREVIATIONS
Abbreviation

Meaning

BS

Base Station

CDR

Compression Driven Routing

CH


QoS

Quality of Service

RDC

Routing Driven Compression

RSSI

Received Signal Strength Indication

SPT

Shortest Path Tree

TDMA

Time Division Multiple Access

VLSI

Very Large-Scale Integration

WSN(s)

Wireless Sensor Network(s)

1-D

.......................................................................................................................... 59
Figure 4.2 Energy consumptions for the DSC, RDC and CDR schemes respectively
to entropy correlation coefficients. .................................................................. 60
Figure 4.3 Routing pattern of 1-D network .............................................................. 61
Figure 4.4 Total bit-hop cost Es that 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. ................................................................................... 63
Figure 4.5 Total bit-hop cost Es that corresponds to cluster size with different values
of entropy correlation coefficient in the case of 1-D with compression at the
cluster head only. ............................................................................................. 64
Figure 4.6 Routing pattern of the 2-D network [122] .............................................. 65
8


Figure 4.7 Total bit-hop cost Es that 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 Es that 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

with compression aggregation in the case of 16 correlation clusters ............ 102
Figure 5.15 Total energy comparison between distance-based protocol and ECODA
with compression aggregation in the case of 8 correlation clusters .............. 102
Figure 5.16 Total energy comparison between distance-based protocol and ECODA
with compression aggregation in the case of 8 correlation clusters .............. 103
Figure 5.17 Total energy comparison between distance-based protocol and ECODA
with compression aggregation in the case of 4 correlation clusters .............. 104
Figure 5.18 Total energy comparison distance-based protocol and ECODA with
compression aggregation in the case of 4 correlation clusters ...................... 105
Figure 5.19 Total energy comparison between distance-based protocol and ECODA
with representative aggregation in the case of 16 correlation clusters .......... 106
Figure 5.20 Number of alive nodes comparison between distance-based protocol and
ECODA with representative aggregation in the case of 16 correlation clusters
........................................................................................................................ 107

10


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

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 wellsuited 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

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 group
while 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
14


Luận án đầy đủ ở file: Luận án Full