ABSTRACT
Optimal deployment of sensor node in other to ensure optimum network coverage is one of the challenging problems faced by Wireless Sensor Network (WSN) researchers due to the complexity and exhaustive nature of WSN. Target tracking problem is concerned with maximizing the lifetime of the network while continuously monitoring a set of targets.This dissertation presents an optimal deployment of WSN and target tracking scheme using the intelligent swarming behaviours of Artificial Fish Swarm Algorithm (AFSA). The preying, swarming and chasing behaviours of AFSA were initially replicated using MATLAB R2013b simulation environment. The position of network nodes were randomly deployed in a network coverage area of 60 square meters with a total of 60 sensor nodes of 4m radius and communication range of 15m using the replicated AFSA algorithm. Thereafter, the replicated AFSA was used to detect event based on target discovery probability model. A series of simulation were performed, and results showed that the proposed technique can attain maximum network coverage of 77.87% when the number of iteration was 25 after which it kept an almost constant value for the rest of the simulation process. The relationship between network coverage and number of mobile nodes also showed that network coverage increased with increase in mobile nodes. The approach indicated maximum network coverage of 80.07% when the mobile node was 50. Thereafter, it tended towards stability when the number of network nodes was above 50. Effects of various attenuation factors on the proposed model were evaluated and simulation results shows that the proposed method successfully attains maximum network coverage of 70.58%, 70.99%, 72.69% and 77.15% when the attenuation factors are 0.75, 0.8, 0.85 and 0.90 respectively. Target tracking simulation scenarios were presented and results showed that the computation energy required to successfully track 30, 45 and 60 targets were 21.63%, 28.003% and 36.99% less than the energy (time taking) required to track the 15 targets respectively.
TABLE OF CONTENTS
TITLE PAGE i
DECLARATION ii
CERTIFICATION iii
DEDICATION iv
ACKNOWLEDGEMENT v
ABSTRACT vii
LIST OF FIGURES xi
LIST OF TABLES xii
LIST OF ABBREVIATIONS xiii
CHAPTER ONE:INTRODUCTION 1
1.1 Background 1
1.2 Aim and Objectives 5
1.3 Statement of Problem 5
1.4 Scope and Limitations 6
1.6 Dissertation Organization 7
CHAPTER TWO:LITERATURE REVIEW 8
2.1 Introduction 8
2.2 Review of Fundamental Concepts 8
2.2.1 Target tracking techniques 8
2.2.1.1 Network architecture 9
2.2.1.2 Tracking algorithm 12
2.2.1.3 Type of sensors 14
2.2.1.4 Number of targets used 15
2.2.1.5 Technology used for implementation of tracking in WSN 16
2.2.1.6 Energy efficiency in WSN 16
2.2.2. Artificial Fish Swarm Algorithm 18
2.3 Review of Similar Works 23
CHAPTER THREE:MATERIALS AND METHODS 33
3.1 Introduction 33
3.2 Wireless Sensor Network Model 33
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3.3 Network Coverage Area 34
3.3.1 Network Area Problem Formulation 35
3.4 Optimization Using Artificial Fish Swarm Algorithm 36
3.4.1 Random Behaviour in WSN 37
3.4.2 Preying in WSN 37
3.4.3 Swarming in WSN 37
3.4.4 Chasing in WSN 38
3.5 Target Formation Using Mobile Nodes 43
3.6 Energy Efficient Management 44
3.7 Model Parameter Setting 45
3.8 System Specification 45
CHAPTER FOUR:RESULTS AND DISCUSSIONS 47
4.1 Introduction 47
4.2 Performance Evaluation of the proposed WSN model Based on Variation between Coverage Area and Sensor Nodes 47
4.3 Performance Evaluation of the proposed WSN model Based on Variation between Coverage Area and Number of Iteration 48
4.4 Performance Evaluation of the proposed WSN model Based on Variation between Network Coverage and Number of Mobile Nodes at Different Attenuation Factor 49
4.5 Target Tracking Simulation Scenarios 51
4.6 Time of Target Discovery and Number of Targets 54
4.7 Validation 55
CHAPTER FIVE:CONCLUSION AND RECOMMENDATION 57
5.1 Summary 57
5.2 Conclusion 58
5.3 Significant Contributions 58
5.4 Recommendation for Further Work 59
REFERENCES 60
Appendix A 65
Appendix B 70
Appendix C 73
Appendix D 74
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Appendix E 75
Appendix F 76
Appendix G
CHAPTER ONE
INTRODUCTION
1.1 Background
Wireless Sensor Networks (WSNs) are increasingly being used for collecting data, such as physical and environmental properties, from a geographical region of interest due to advancements in electronics and wireless communication technology. WSNs are composed of a large number of tiny, low-power, low-cost sensor nodes which have the ability to sense physical phenomena, process data, and communicate with one another (Alikhani, 2010). A sensor node is a tiny device that includes four basic components: a sensing subsystem for data acquisition from the physical surrounding environment, a processing subsystem for local data processing and storage, a wireless communication subsystem for data transmission and a power supply subsystem, which are batteries. A large number of these wireless sensor nodes are deployed across a geographical region to form a WSN. These WSNs create smart environments by providing access to information regarding the environment through collecting, processing, analyzing, and disseminating data whenever required (Alikhani, 2010). In order to use WSNs in inaccessible terrains or disaster relief operations, random deployment of the sensor nodes is required. As a result, the position of these nodes will not be predetermined and thus the nodes must have the ability to collaborate with each other to form self-organized networks in order to perform tasks including but not limited to determining their location (Akyildizet al., 2002).
WSNs have numerous applications, which include environmental monitoring, specifically for planetary exploration, geophysical monitoring, habitat monitoring, oceanography, wildlife tracking and target tracking. Target tracking is useful in keeping check on movements of individuals (pedestrians or mobile), in congested areas; such as business district of cities,
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government buildings, nuclear facilities, borders, seaports, airports and national monuments. Another promising application of WSNs is in the field of physiological monitoring and medical sensing (Alikhani, 2010). WSNs can also be used in smart homes and offices where they can efficiently regulate light, temperature, and humidity based on predefined individual preferences. Other uses of WSNs are in inventory tracking, precision agriculture, disaster detection, search and rescue operations, and commercial and residential security. In a military context, WSNs can be used for surveillance and battle-space monitoring. WSNs can also be used in transportation such that vehicles equipped with wireless sensors form local communication networks. These networks allow the vehicles to share information on weather and road conditions, plan routes, avoid traffic, and identify their position in areas where Global Positioning System (GPS) signals are unavailable (Alikhani, 2010). A sensor network deployment can be categorized as either a dense deployment or a sparse deployment. A dense deployment has a high number of sensor nodes in the given field of interest while a sparse deployment has fewer nodes. The dense deployment model is used in situations where it is very important for every event to be detected or when it is important to have multiple sensors cover an area. Sparse deployments may be used when the cost of the sensors makes a dense deployment prohibitive or when there is the need to achieve maximum coverage using the barest minimum number of sensors (Mulligan, 2010). Malik (2005) stated that sensors can be deployed in any facility or area which has to be sensed in three main ways: 1) Triangular sensor deployment: Sensors are placed in a triangular fashion. 2) Square sensor deployment: Sensors are arranged in square position. 3) Irregular sensor deployment: Sensors arrangement is uneven. These deployments are depicted in Figure 1.1 (Malik, 2005).
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Figure1.1.WSN Deployments: ((a) Triangular (b) Square and (c) Irregular) (Malik, 2005). The following are factors to consider in Wireless Sensor Network Deployment: a) Scalability: A sensor network typically comprises of a large number of nodes spread randomly throughout the area. Scalability includes reliability of command dissemination and data transfer, management of large volume of data and scalable algorithms for analysing the data. Therefore, managing all these becomes a very difficult task. However, the number of nodes depends on the application. Distributed and localized (such as: Trilateration and Swarm) algorithmsare essential in these cases. In WSNs, increasing the number of sensors in an area does not lead to better tracking results; since beyond a critical threshold (0.9) increasing the number of sensors does not improve the location precision in tracking. Hence, the placement of the sensors in an area should be so as to maintain a balance between number of sensors and coverage required (Tseng et al., 2003).
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b) Stability: Since sensors are likely to be installed in outdoor or even hostile environments, their failure is an issue of concern always. The system must be able to operate well without supervision. This unattended mode of operation is common nowadays (Malik, 2005). c) Power: Sensors are deployed in different terrains and since no source of power supply is available, sensor devices are operated by battery. Hence, energy conservation is a prime concern at all times. Operations such as on-board signal processing and communication with neighbouring nodes consume a lot of energy. Thus, energy awareness has to be incorporated in every aspect of design and operation which means it is an integral part of groups of communicating sensor nodes and the entire network and not only in the individual nodes (Malik, 2005). Target tracking is the application of WSN whose goal is to trace the roaming path of an object which is considered as a target and to detect the position of target. As WSN continuously monitor the environment, it provides the space to enhance the energy efficiency. Target tracking scheme comprises of three interrelated subsystems which are shown in Figure 1.2.
Figure1.2: Target Tracking Scheme Classification (Chauhan&Ahlawat, 2014) .
The sensing subsystem is used to sense the target i.e. it comprises of the node that first detects the target and other nodes which gradually take part in detecting the target. The second
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subsystem is the prediction based algorithm which is used to trace the path of the desired target. The third one is the communication subsystem which is used to send the information from one node to another. All these three subsystem work collaboratively and maintain the relationship among themselves(Chauhan&Ahlawat, 2014). This research, consist of two parts, the first part focuses on optimal deployment of sensor node using artificial fish swarm algorithm while the second part focused on target tracking taking into account energy management at node level.
1.2 Aim and Objectives
The aim of the research is the development of an Artificial Fish Swarm Algorithm (AFSA) based energy efficient target tracking scheme in Wireless Sensor Networks (WSN). The objectives of the study are as follows:
1) Replication of the Preying, Swarming and Chasing behaviours of Artificial Fish Swarm Algorithm for optimal deployment of WSN.
2) Deployment of WSN nodes using the Replicated AFSA in (1) and the development of target tracking scheme using node tracking probability model.
3) Simulation and performance evaluation of the developed model in 2) using MATLAB R2013b Communication/Control toolboxes and validation by comparison with results obtained in Yiyue et al., (2012)
1.3 Statement of Problem
Sensor node deployment is crucial to Wireless Sensor Network (WSN) energy efficiency. Inapt deployment cause inappropriate node concentration (high density). This creates message collisions and retransmissions, signal intrusions and cramming, huge energy consumptions, communication slit among others on the WSN. These in turn creates challenges on the
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scalability, stability, distributed architecture, energy consumption and autonomous operations of the WSN. The managing of the random spread of nodes in a coverage area is tasking due to dependence on applications (distributed and localized). The challenge is improving precision location-tracking problem, with emphasis on tracing the roaming path of node in a coverage in which sensors are deployed using AFSA.
1.4 Scope and Limitations
The limitations of this research work are highlighted as follows:
i. Time delay and synchronization.
ii. Optimization of power and memory space.
iii. Computational limitations.
1.5 Methodology
The step by step procedures adopted in this research, which include the development of the Artificial Fish Swarm Algorithm (AFSA) and their applications in the wireless sensor network are highlighted as follows:
i) Initialization of population of AFSA (The number of wireless sensor nodes) which is N numbers of Artificial Fish (AF) in D problem dimension space and all other parameters (Visual distance, Step size and Crowdedness).
ii) Replication of AFSA using the
a. Preying
b. Swarming
c. Chasing
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iii) Initialize the WSN network model and search (deploy) the nodes in its sensor area using the replicated AFSA behaviours in (ii)
iv) Determination of the node detected by all other nodes in the network based on node probability.
v) Determination of target tracking time in order to evaluate the energy efficiency of the proposed technique
vi) Evaluation of the performance of the proposed model based on network coverage.
vii) Validation using the work of Yiyue et al., (2012).
1.6 Dissertation Organization
The general introduction has been presented in Chapter One. Detail review of related literatures and relevant fundamental concept about the Target Tracking Techniques and the Tracking Algorithm (Artificial Fish Swarm Algorithm) is carried out in Chapter Two. The methodology and relevant mathematical models describing the Wireless Sensor Network and Optimization using Artificial Fish Swarm Algorithm were presented in Chapter Three. The analysis, performance and discussion of the result were presented in Chapter Four. Conclusions and recommendations makes up Chapter Five.
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