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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, 17th Decemberber 2018
SUPERVISORS

AUTHOR

Dr. Nguyen Kim Khanh

Nguyen Thi Thanh Nga

Assoc. Prof. Ngo Hong Son

2


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

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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

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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

5


5 ENTROPY CORRELATION BASED DATA AGGREGATION
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

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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

Cluster Head

CSMA

Carrier Sense Multiple Access

DSC

Distributed Source Coding

ECODA

Entropy COrrelation clustering for Data Aggregation

JE

Joint Entropy

LEACH-C

Low Energy Adaptive Clustering Hierarchy- Centralized

MAC

Media Access Control

MEMS

Micro Electro Mechanical Systems

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

One-Dimensional

2-D

Two-Dimensional

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

.......................................................................................................................... 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
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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

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

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Figure 5.9 Total energy in each round in case of representative aggregation with
compression with 16 correlation clusters ........................................................ 98
Figure 5.10 Number of alive nodes in each round in case of representative aggregation
with compression with 16 correlation clusters ................................................ 98
Figure 5.11 Total energy in each round in the case of representative aggregation
without compression with 16 correlation clusters ........................................... 99
Figure 5.12 Number of alive nodes in each round in the case of representative
aggregation without compression with 16 correlation clusters. .................... 100
Figure 5.13 Total energy comparison between distance-based protocol and ECODA
with compression aggregation in the case of 16 correlation clusters ............ 101
Figure 5.14 Total energy comparison between distance-based protocol and ECODA

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

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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

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

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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

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
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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 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


based on the desired distortion and then a representative aggregation routing protocol
is developed.
In entropy-based data compression, several data compression schemes are
evaluated using the proposed correlation model. Then a compression-based routing
protocol is developed.
Chapter 5: Entropy Correlation based Data Aggregation Protocol (ECODA)
In this chapter, we outline an Entropy COrrelation-based Data Aggregation
protocol (ECODA) using the proposed clustering scheme in chapter 3 and data
aggregation schemes in chapter 4. The simulations have also been done to validate
the effectiveness of the proposed clustering and aggregating schemes.
Chapter 6: Conclusions and Further study
This chapter concludes the results of the thesis with careful evaluations and
points out the remained problems that are the future works.

15

CHAPTER 1

1

INTRODUCTION
Overviews

People always want to know more about the physical world around, so that
they can have a better understanding of the surrounding environment. Therefore, they
try to collect the environment’s information as much details as possible. Sensor nodes
are used to link the physical to the digital world by capturing and revealing real-world
phenomena and converting these into a form that can be processed, stored, and acted
upon. By integrating sensors into numerous devices, machines, and environments, a
tremendous societal benefit can be provided such as avoiding catastrophic
infrastructure failures, conserving precious natural resources, increasing productivity,
enhancing security, and enabling new applications such as context-aware systems and
smart technologies. The advances in technologies such as very large-scale integration
(VLSI), microelectromechanical systems (MEMS), and wireless communications,
that make sensors become tinier, low-power, inexpensive, further contribute to the
widespread use of distributed sensor systems such as wireless sensor networks.

Figure 1.1 Wireless Sensor Network1

1

/>
16

Wireless Sensor Network (WSN), is the collection of sensor nodes which
cooperatively monitor surrounding phenomena over large physical areas [1]–[4].
These sensor nodes can sense, observe or measure, gather information from the
environment and transmit the sensed data to the user based on some local decision
process. A typical sensor node is composed of a sensing unit which is equipped with
one or more sensors, a processing unit, a power unit, and a transceiver unit. The
sensing unit could have various sensors such as thermal, biological, chemical, optical,
and magnetic to measure properties of the environment. A sensor node acquires data
through the sensing unit, processes sensed data by the processing unit and finally
transmits processed data using the transceiver unit. Because of the limitations of
memory capabilities, sensor nodes should be implemented by wireless
communication to transfer the data to a base station, allowing them to disseminate
their sensor data to remote processing, visualization, analysis, and storage systems.

Figure 1.2 Wireless Sensor Network Applications 2

2

/>Wireless_Sensor_Networks/figures?lo=1

17


There are five types of WSNs: terrestrial WSN, underground WSN,
underwater WSN, multi-media WSN, and mobile WSN [3]. In terrestrial WSNs [1],
there are hundreds to thousands of inexpensive wireless sensor nodes deployed in a
given area, either in an ad hoc or in a pre-planned manner. Reliable communication
in a dense environment is very important in this WSN type. Battery power is limited
and may not be rechargeable in terrestrial sensor nodes, however, they can be
equipped with a secondary power source such as solar cells. In a terrestrial WSN,

energy can be conserved with multi-hop optimal routing, short transmission range,
in-network data aggregation, eliminating data redundancy, minimizing delays, and
using low duty-cycle operations.
In underground WSNs [5], sensor nodes are buried underground or in a cave
or mine used to monitor underground conditions. An underground WSN is more
expensive than a terrestrial WSN in terms of equipment, deployment, and
maintenance. In addition, the operation of wireless communication is more difficult
in the underground environment due to signal losses and high levels of attenuation.
Opposite to a dense deployment of sensor nodes in a terrestrial WSN,
underwater WSNs [6] consist of sensor nodes and vehicles deployed underwater.
Because of their special working environment, underwater sensor nodes are more
expensive and fewer sensor nodes are deployed, in comparison with terrestrial WSNs.
Autonomous underwater vehicles are used for exploration or gathering data from
sensor nodes. Underwater wireless communications are typically established through
transmission of acoustic waves with limited bandwidth, long propagation delay, and
signal fading issue. In addition, underwater sensor nodes must be able to selfconfigure and adapt to the harsh ocean environment.
Multi-media WSNs [7] have been developed to enable the monitoring and
tracking of events using multimedia such as video, audio, and imaging. Multi-media
WSNs consist of various low-cost sensor nodes equipped with cameras and
microphones. They are usually deployed in a pre-planned manner into the
environment to guarantee coverage. Multi-media sensor nodes interconnect with each
other over a wireless connection for data retrieval, process, correlation, and
compression. Because of high data transmission, challenges in multi-media WSN
include high bandwidth demand, high energy consumption, quality of service (QoS)
provisioning, data processing and compressing techniques, and cross-layer design.
Mobile WSNs [8] [9] consist of a collection of sensor nodes that can move on
their own and interact with the physical environment. Same as in static WSNs, nodes
in mobile WSNs can sense, compute, and communicate. But unlike from static nodes,
18

mobile nodes can reposition and organize itself in the network. This mobility
characteristic requires dynamic routing in a mobile WSN. Challenges in mobile WSN
include deployment, localization, self-organization, navigation and control, coverage,
energy, maintenance, and data process.
The above described features of WSNs ensure great potential for many
applications [10]–[14]. The development of WSNs was motivated by military
applications [15]–[19] and then were widely used in various fields such as industrial
monitoring [20]–[25], environment monitoring [26]–[33], agriculture [34]–[37],
forest fire detection [38]–[40], animal tracking [41] [42], healthcare [43]–[50],
security [51]–[53], home automation [54] [55], power utility’s distribution [56],
logistics [57], intelligent traffic systems [58], etc.
In Vietnam, studies on WSNs have been considered in the last two decades.
The most attracted topics are energy saving and load balancing in WSNs, in
consideration of base station position [59], delay constrained [60], 3D WSN [61],
WSNs with holes [62], k-means clustering [63]. The applications of WSNs are also
widely considered such as landside monitoring [64], smart grid [65], target tracking
[66], logistics [57], and healthcare monitoring [67].
Energy conservation in WSNs
In most cases, energy for activities in WSNs comes from a limited battery
supply. However, in many applications, it is very hard or impossible to recharge the
batteries due to the deployment of the nodes because of the difficulties and hostile
terrain or due to a large number of nodes deployed in the environment [68] [69]. For
those reasons, energy conservation is commonly recognized as the key challenge to
designing and operating the network in WSNs, because individual sensor nodes are
expected to be low-cost, small-in-size, and powered by a non-replaceable battery.
In recent years, numerous energy-saving approaches have been proposed in
[70] [71]. They can mainly be classified into five categories including radio
optimization, data reduction, sleep/wakeup schemes, energy-efficient routing and
charging solutions. The next section will present these five categories of energysaving approaches.

1.2.1 Radio optimization
In radio optimization, to save energy, radio parameters such as coding and
modulation schemes, power transmission and antenna direction are optimized. Radio
optimization approaches can further be divided into 4 schemes including modulation

19


optimization, cooperative communication, transmission power control, and a
directional antenna.
Modulation optimization tries to optimize the modulation parameters that
results in minimum radio energy consumption. The good trade-off between the
constellation size, the information rate, the transmission time, the distance between
nodes and the noise are considered [72] [73].
Cooperative communication schemes try to improve the quality of the
received signal by collaborating several single antenna devices to create a virtual
multi-antenna transmitter [74] [75].
Transmission power control schemes enhance energy efficiency at the
physical layer by adjusting radio transmission power. The idea is that a lower
communication range between nodes requires less power from radio [76] [77].
Another idea is that a node with higher remaining energy may increase its
transmission power, which enables other nodes to decrease their transmission power
[78].
Directional antenna schemes allow the signal to be sent and received in one
direction at a time that allows the improvement of transmission range and throughput
[79] [80]. To take advantage of directional antennas, new MAC protocols have been
proposed in [81] [82]. In addition, some specific problems also have to be considered
in [83].
1.2.2 Sleep/wake-up schemes
Sleep/wake-up schemes try to adapt node activity to save energy by putting

the radio in sleep mode. The main idea of this approach is the duty cycling scheme.
Duty cycling scheme schedules the node radio state according to network activity to
minimize idle listening and favor the sleep mode. They are the most energy-efficient
but suffer from sleep latency. In some cases, it is not possible to broadcast information
to all its neighbors because of unsimultaneously active. In addition, some fixing
parameters such as listening/sleeping period, preamble length, and slot time are
strictly issues because of system performance. The detailed survey of duty cycling
can be found in [84].
1.2.3 Energy efficient routing
Routing is also a burden that makes seriously drain energy reserves. In general,
there are various routing paradigms. In this research area, some main paradigms are
considered such as cluster architecture, energy as a routing metric, multipath routing,

20


relay node placement. An extensive review of energy-aware routing protocols can be
found in [85] [86].
Cluster architecture is the organizing of the network into clusters and each
cluster is managed by a cluster head. This technique has been proposed to enhance
energy efficiency because it can limit energy consumption in different means such as
reducing the communication range inside the cluster that requires less transmission
power; limiting the number of transmits by fusion done by cluster head; reducing
energy-intensive computation to cluster head; enabling power-off some nodes inside
the cluster while cluster head takes forwarding responsibilities; and balancing energy
consumption by cluster head rotation [87].
Energy can also be considered as a metric in the setup path phase to extend the
lifetime of sensor networks. In this case, routing algorithms not only focus on the
shortest paths but also can select the next hop based on its residual energy [88].
Multipath routing, in general, is more complex than single-path routing. But

single-path routing can rapidly drain the energy of nodes on the selected path.
Multipath routing can balance the energy among nodes by alternating forwarding
nodes [89] [90]. More surveys on multipath routing protocols can be found in [91].
The premature depletion of nodes in each region can create energy holes or
partition the network. This situation can be avoided by optimizing node placements
or adding some relay nodes with enhanced capabilities. This helps to improve energy
balance, avoid sensor hot-spots, ensuring coverage [92]–[94].
1.2.4 Data reduction
Energy consumption depends on data transmission. Thus, reducing the amount
of data to be delivered can save energy. Data reduction approaches can be divided
into three types: data aggregation, adaptive sampling, and network coding.
Data aggregation techniques involve different ways of routing data packets to
combine them by exploiting the extracted features and statistics of datasets coming
from different sensor nodes. There are several aggregation techniques with different
aggregation functions and for different specific application requirements. The first
type of aggregation function is to extract the maximum, minimum or averaged value
of aggregated data [95] [96]. In this way, it can reduce the amount of communicating
data in the networks which affect the power consumption. However, this technique
can lose much of the original structure in the extracted data.
The second type of aggregation technique is data compression. Data
compression techniques are further divided into distributed data compression [97]
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[98] and local data compression [99] [100]. The distributed data compression
techniques are the most optimal compression. However, it is much more complicated
than local data compression that is with smaller compression rate. The detailed survey
of data compression in WSN can be found in [101]. It is important to note that the
data compression techniques are only effective with correlation data. Therefore, the
correlation is usually required when using these techniques.

The third type of aggregation technique is representative type [102] in which
some nodes are chosen to be the representative of a group of nodes. The other nodes
in the group can be put to sleep to save energy. The number of sleep nodes that affects
the power consumption is decided by specified distortion. Same as data compression,
these techniques required data in correlation.
Adaptive sampling techniques adjust the sampling rate at each sensor while
ensuring that application needs are met in terms of coverage of information precision
by exploiting spatial-temporal correlations between data. By reducing the number of
samples, the amount of transmitted data is reduced thus save the node energy. The
temporal analysis of sensed data is used in [103] and spatial correlation is used in
[104]. More details about adaptive sampling can be found in [105].
Network coding is used to reduce the traffic in broadcast scenarios by sending
a linear combination of several data instead of a copy of each data. At the destination
nodes, data can be decoded by solving the linear equations [84] [106]. Network
coding exploits the trade-off between computation and communication since
communications are slow compared to computations and more energy consumption.
1.2.5 Charging solution
Several recent types of research address energy harvesting and wireless
charging techniques for WSNs as promising solutions because of recharge capability
without human intervention.
Energy harvesting techniques have been developed to enable the sensors to
harvest energy from their surrounding environment such as solar, wind or kinetic
energy [107]. Energy harvesting schemes often require energy prediction to manage
the available power efficiently. It is important to note that because of the limitation
of remain energy between two harvesting opportunities, the energy saving
mechanisms are still necessary to implement.
The breakthrough in wireless power transfer is expected to enable the wireless
charging capability for WSNs. Wireless charging can be done in two ways:
electromagnetic radiation and magnetic resonant coupling. It is shown that
22

omnidirectional electromagnetic radiation technology is only applicable to ultra-low
power requirement and low sensing activities [108]. The reason is electromagnetic
waves suffer from the rapid drop in power efficiency over distance, and active
radiation technology may pose safety concerns to humans. In contrast, magnetic
resonance coupling appears to be the most promising technique with higher efficiency
and safer. However, the charging range is still a big concern [108].
Data correlation and energy conservation in WSNs
In typical WSNs applications, sensors are required for spatially dense
deployment to achieve satisfactory coverage [1]. Consequently, multiple sensors will
record information about a single event in the sensing field, i.e. these sensed data
strongly depends on each other. For example, temperature sensors in the same room
record the same temperature information, or several cameras that monitor the same
area record many frames with similar information. In another word, they 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, thus the
amount of sent data is reduced [109]. 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 might be adequate to
communicate the event features to the base station within a certain reliability/fidelity
level [110].
In addition, in WSNs, the power breakdown heavily depends on the specific
node. However, the following remarks generally hold [109] [111].
• The radio energy consumption is of the same order of magnitude in the reception,
transmission, and idle states, while the power consumption drops of at least one
order of magnitude in the sleep state. Therefore, the radio should be put to sleep
(or turned off) whenever possible.

• The communication activity has an energy consumption much higher than the
computation activity. It has been shown that transmitting one bit may consume as
much as executing a few thousand instructions [112]. Therefore, communication
should be traded for computation.
Data correlation can allow us to reduce the data transferring, or even to put
some sensor nodes to sleep. Thus, it can make WSNs conserve energy significantly.

23


Problem statements and contributions
The main problems in this research are “How to recognize the correlation
among dataset by looking at data itself and how to exploit the correlation
characteristic for energy conservation in WSNs”. In this research, we focus on
WSNs working in high correlation environment. A high correlation environment can
be divided into groups called correlation regions where measured data strongly
depends on each other. By clustering sensor nodes into correlation regions, data
aggregation can be done to conserve the energy in WSNs. In this paper, we focus on
two data aggregation schemes including data compression and representative
aggregation. The main contributions of the thesis are:
Developing an entropy correlation clustering algorithm and entropy
correlation model to describe the correlation characteristics of a correlation cluster.
This algorithm can divide a correlation environment into several correlation
regions using the entropy values of measured data and the entropy correlation
coefficients of measured data pairs in the environment. At the same time, this
algorithm uses only the data itself and does not depend on the distance information.
The correlation model describes the relationship between the joint entropy of a dataset
and the number of data series in the dataset, in consideration of data’s entropy
correlation coefficient.
Analyzing and evaluating the impact of the correlation characteristic to data

aggregation schemes.
With the proposed correlation clustering and model, it is necessary to evaluate
their impact on data aggregation schemes. With data compression aggregation,
several compression schemes and network structures are considered to find the most
appropriate compression routing for WSNs. With representative aggregation, a
distortion function that measures the required ratio of data loss is used. The number
of representative nodes is then evaluated, and the representative node selection
algorithm is proposed.
Developing an entropy correlation-based data aggregation protocol for WSNs
to exploit the correlation characteristic of the sensed environment.
The developed protocol includes two phases, one phase is for data collection
to identify correlation characteristic, the other phase is for data aggregation
implementation. For this protocol, the proposed clustering algorithm and data
aggregation schemes are used. In addition, the design of the protocol is proposed. The
feasibility of the developed protocol is then demonstrated using simulation.
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CHAPTER 2
2

CORRELATION IN WIRELESS SENSOR NETWORK

As mentioned in chapter 1, correlation characteristic has many significant potential
advantages for the development of energy-efficient communication protocols for
WSNs. To evaluate and exploit the correlation characteristic, it is necessary to build
a correlation model. This chapter concentrates on the survey of the existing
correlation models. From the advantages and limitations of the previous correlation
model, the approaching methodology of developing a new correlation model will be
pointed out.

Correlation model survey
Correlation is represented for the relationships between quantitative
variables or categorical variables. In other words, it’s a measure of how things are
related. Data correlation is a measure of how data is related to each other.
To exploit the correlation in WSNs, it is necessary to recognize the correlation
among data in the network by establishing correlation models. There have been many
research efforts to study the correlation model in WSNs. In [111], correlated nodes
are supposed to observe the same source 𝑆, and the observed data 𝑋𝑖 (𝑡 ) at the ith node
is the sum of a correlated version of the source 𝑆𝑖 (𝑡 ) and observed noise 𝑁𝑖 (𝑡 ).
𝑋𝑖 (𝑡 ) = 𝑆𝑖 (𝑡 ) + 𝑁𝑖 (𝑡 ).

(2.1)

The correlation model is the covariance function 𝐾𝜗 (correlation coefficient
𝜌) that is chosen to be distance dependence and can be classified into four groups
including:
Spherical:
3𝑑 1 𝑑 3
( )
𝐾𝜗 = {1 − 2 𝜃1 + 2 𝜃2 if 0 ≤ 𝑑 ≤ 𝜃1 ,
0
if 𝑑 > 𝜃1 > 0.

(2.2)

Power exponential:
𝐾𝜗 = 𝑒

(−

𝑑 𝜃2
)
𝜃1
.

Rational quadratic:

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(2.3)