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MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY  NGUYEN THI MY BINH APPROXIMATE ALGORITHMS FOR SOLVING THE MINIMAL EXPOSURE PATH PROBLEMS IN WIRELESS SENSOR NETWORKS Major : Computer Science Code : 9480101 SUPERVISORS: Associate Professor Huynh Thi Thanh Binh Associate Professor Nguyen Duc Nghia Hanoi, 2020 DECLARATION OF AUTHORSHIP I assure that this dissertation ”Approximate algorithms for solving the minimal exposure path problems in wireless sensor networks” is my own work under the guidance of my supervisors, Associate Professor Huynh Thi Thanh Binh and Associate Professor Nguyen Duc Nghia All the research results are presented in the dissertation which are honest and have not published by any other author or work Hanoi, 12 December 2020 PhD Student Nguyen Thi My Binh SUPERVISOR Huynh Thi Thanh Binh i ACKNOWLEDGEMENT This dissertation was completed during my doctoral course at the School of Information Communication and Technology (SoICT), Hanoi University of Science and Technology (HUST) I am so grateful for all the people who always support and encourage me to complete this study First, I would like to express my sincere gratitude to my supervisors, Associate Professor Huynh Thi Thanh Binh and Associate Professor Nguyen Duc Nghia I am indebted to have had advisors who gave me all the freedom, resources, guidance and support during the period that led up to this dissertation Their broad knowledge in different areas inspired me and helped me overcome many difficulties in my research Furthermore, I would like to thank all the members of Modeling and Simulation Lab, Computer Science Department, SoICT, HUST, as well as all of my colleagues in the Faculty of Information Technology, Hanoi University of Industry They assisted me a lot in the research process and gave me helpful advice to overcome my own difficulties Furthermore, attending at scientific conferences has always been a great opportunity for me to receive many useful comments from the academic community Last but not least, I would like to express my utmost gratitude to my family, my parents, my husband and my children, for their unconditional love, support, understanding and encouragement I would not be able to achieve this accomplishment without their love and support Hanoi, 12 December 2020 Ph.D Student Nguyen Thi My Binh ii CONTENTS DECLARATION OF AUTHORSHIP i ACKNOWLEDGEMENT ii CONTENTS vi SYMBOLS vii LIST OF TABLES x LIST OF FIGURES xv INTRODUCTION 1 BACKGROUND 1.1 1.2 1.3 1.4 10 Wireless sensor networks 10 1.1.1 Sensors 10 1.1.2 Sensor nodes 11 1.1.3 Sensor coverage model 11 1.1.4 Sensing intensity models 12 1.1.5 Terminologies 12 1.1.6 Wireless sensor network scenarios 14 Optimization problems 15 Approximate algorithms 17 1.3.1 Single-solution-based metaheuristic 21 1.3.2 Population-based metaheuristics 22 1.3.2.1 Evolutionary algorithms 23 1.3.2.2 Particle swarm optimization algorithm 26 Conclusion 29 MINIMAL EXPOSURE PATH PROBLEMS IN OMNI-DIRECTIONAL SENSOR NETWORKS 2.1 30 Minimal exposure path problem in mobile wireless sensor networks 30 2.1.1 Motivations 30 2.1.2 Preliminaries and problem formulation 31 2.1.2.1 2.1.2.2 2.1.3 Preliminaries 31 Problem formulation 33 Proposed algorithms 34 2.1.3.1 The GAMEP for solving MMEP problem 34 iii 2.1.4 2.1.3.2 The HPSO-MMEP algorithm for solving the MMEP problem 38 2.1.3.3 Complexity analysis 42 Experimental results 43 2.1.4.1 2.1.4.2 2.2 Minimal exposure path problem in probabilistic coverage model 50 2.2.1 Motivations 50 2.2.2 Preliminaries and problem formulation 51 2.2.3 2.2.2.1 Preliminaries 51 2.2.2.2 Problem formulation 2.2.4 53 Proposed algorithms 55 2.2.3.1 2.2.3.2 2.3 Experimental settings 43 Computation results 44 Grid-based algorithm for solving the PM-based-MEP problem 55 Genetic algorithm for solving the PM-based-MEP problem 56 Experimental results 64 2.2.4.1 Experimental setting 64 2.2.4.2 Computation results 66 Conclusion 81 MINIMAL EXPOSURE PATH PROBLEM IN WIRELESS MULTIMEDIA SENSOR NETWORKS 82 3.1 Motivations 82 3.2 Preliminaries and problem formulation 83 3.2.1 3.2.2 3.3 3.2.1.1 The Boolean directional coverage model 83 3.2.1.2 The attenuated directional sensing model 83 3.2.1.3 Accumulative intensity function 84 3.2.1.4 3.2.1.5 Closest-sensing intensity function 85 Minimal exposure path 85 Problem formulation 86 Proposed algorithms 87 3.3.1 Individual representation 87 3.3.2 Individual initialization 88 3.3.3 3.4 Preliminaries 83 3.3.2.1 HEA individual initialization 88 3.3.2.2 GPSO individual initialization 89 3.3.2.3 Fitness function 90 Evolutionary operators 91 3.3.3.1 Evolutionary algorithm 91 3.3.3.2 Particle swarm optimization algorithm 94 3.3.4 Selection operator 97 3.3.5 Complexity analysis 98 Experimental results 98 iv 3.4.1 3.4.2 3.5 Experimental setting 98 3.4.1.1 Datasets 98 3.4.1.2 Parameters and system setting 99 Computational results 100 3.4.2.1 Algorithm parameters trials 100 3.4.2.2 Comparison under our datasets 104 3.4.2.3 Comparisons under the datasets of previous algorithms 109 Conclusion 111 OBSTACLES-EVASION MINIMAL EXPOSURE PATH PROBLEM IN WIRELESS SENSOR NETWORKS 4.1 4.2 Motivations 112 Preliminaries and problem formulation 112 4.2.1 4.3 Preliminaries 112 4.2.1.1 The truncated directional coverage model 112 4.2.1.2 The accumulative sensing intensity 113 4.2.1.3 Obstacle model 113 4.2.1.4 Minimal exposure path 114 4.2.2 Problem formulation 115 Proposed algorithm 116 4.3.1 4.3.2 4.3.3 4.4 112 A novel characteristic of FEA algorithm 117 4.3.1.1 Individual 117 4.3.1.2 Population 119 Algorithm progress 120 4.3.2.1 Initialization 120 4.3.2.2 Family pairing 120 4.3.2.3 4.3.2.4 Crossover 121 Mutation 122 4.3.2.5 Update 123 4.3.2.6 Selection 4.3.2.7 Family system based evolutionary algorithm 124 123 Complexity analysis 124 Experimental results 124 4.4.1 Dataset 124 4.4.2 4.4.3 Parameters 126 Computational results 126 4.4.3.1 The performance of FEA when using different A and D values 126 4.4.3.2 The performance of FEA when using different pmin and pmax values 127 4.4.3.3 Comparison between FEA and previous algorithm in OE-MEP problem 128 v 4.4.3.4 4.5 Comparison between FEA and GA-MEP 130 Conclusion 133 CONCLUSIONS AND FUTURE WORKS 134 PUBLICATIONS 137 BIBLIOGRAPHY 138 vi ABBREVIATIONS No Abbreviation Meaning WSNs Wireless Sensor Networks IoT Internet Of Thing ROI Region Of Interest BC Barrier Coverage MEP Minimal Exeposure Path PSO Particle Swarm Optimization NFE Numerically Function Extreme MWSN Mobile Wireless Sensor Networks GPSO Gravitation Partical Swarm Optimization 10 HGA Hybrid Genetic Algorithm 11 FEA Family Evolution Algorithm 12 HoWSNs Homogeneous Wireless Sensor Networks 13 HeWSNs Heterogeneous Wireless Sensor Networks 14 SWSN Static Wireless Sensor Networks 15 CO Combinatorial Optimization 16 TSP Travelling Salesman Problem 17 QAP Quadratic Assignment Problem 18 ACO Ant Colony Optimization 19 EC Evolution Computation 20 ILS Iterated Local Search 21 TS Tabu Search 22 GA Genetic Algorithm 23 LS Local Search 24 HeWMSN Heterogeneous Wireless Multimedia Sensor Networks 25 GAMEP Genetic Algorithm Minimal Exposure Path 26 HPSO-MMEP Hybrid Particle Swarm Mobile MEP 27 GA-MEP Genetic Algorithm for Minimal Exposure Path 28 GB-MEP Grid Based Algorithm for Minimal Exposure Path 29 HEA Hybrid Evolution Algorithm 30 MMEP Mobile Minimal Exposure Path vii LIST OF TABLES Table Comparative table of related works on MEP problem Table 1.1 Evolution process versus solving an optimization problem 25 Table 2.1 Experimental parameters for attenuated disk model and truncated attenuated disk model 44 Table 2.2 Parameters setting for GAMEP 45 Table 2.3 Experimental parameters for HPSO-MMEP algorithm 45 Table 2.4 Parameters setting for HPSO 45 Table 2.5 Different version of HPSO-MMEP using different genetic operators 46 Table 2.6 Computation results of HPSO-MMEP in comparison with GAMEP in uniform distribution of sensors (Mev: minimal exposure value, Sd: standard deviation) 48 Table 2.7 Computation results of HPSO-MMEP in comparison with GAMEP in Gauss distribution of sensors (Mev: minimal exposure value, Sd: standard deviation) 48 Table 2.8 Experimental parameters of probabilistic 65 Table 2.9 Experimental parameter of GA-MEP 65 Table 2.10 Experimental Parameter of HGA-NFE 66 Table 2.11 The comparison minimal exposure value, computation time and saw-tooth degree between GA-MEP and GB-MEP when using different subinterval ∆s, the topology used is u 50 (Mev : minimal exposure value; Time (s): computation time per unit second; Dst : saw-tooth degree) 67 Table 2.12 The minimal exposure value obtain from GB-MEP and the best solution of GA-MEP when threshold A varies from to on the topology used is u 50 (GBMev: the minimal exposure value obtains by GB-MEP; GA-Mev: the minimal exposure value obtains by GA-MEP) 69 Table 2.13 Computation time comparison of OGB and GB-MEP when subinterval ∆s varies from down-to 0.2 on instance u 50 viii 69 Table 2.14 The best minimal exposure value, running time and saw-tooth degree obtained from GA-MEP1, GA-MEP2 and GA-MEP on topology u 30 1, u 40 1, u 50 1, u 60 1, u 70 1, u 80 1, u 90 and u 100 (Mev : the minimal exposure value, Time: the computation time, Dst : the saw-tooth degree of each version GA-MEP algorithms) 71 Table 2.15 Result on Sign test for pairwise comparisons between Minimal Exposure values obtained by GA-MEP and HGA-NFE (Mev : the minimal exposure value) 76 Table 2.16 Comparison of experimental results between GB-MEP and GA-MEP (Num: number of sensors, Ord: the order of the topology, Mev: the minimal exposure value, Time: the computation time, Sd: standard deviation, Dst: the saw-tooth degree, BMev: best minimal exposure value, AMev: Average minimal exposure value) 79 Table 3.1 Experiment instance for homogeneous binary - Dataset Table 3.2 Experiment instance for heterogeneous binary - Dataset 100 Table 3.3 Experimental instances for homogeneous network using attenuated model - Dataset 99 100 Table 3.4 Parameters for HEA 101 Table 3.5 Parameters setting for GPSO 101 Table 3.6 Operators setting for four versions of HEA Table 3.7 Comparison between four HEA versions when running on the Dataset 101 (Heterogeneous, Binary) (Mev - Minimal exposure value, Time - Computation time (second)) 102 Table 3.8 Parameters setting for four versions of GPSO 103 Table 3.9 Comparison between four versions of GPSO when running on the Dataset (Heterogeneous, Binary) (Mev - Minimal exposure value, Time - Computation time (second)) 104 Table 3.10 Comparison between HEA, GPSO and previous algorithms when running on Dataset (Homogeneous - Binary) (Mev- Minimal exposure value, TimeComputational time (second)) 104 Table 3.11 Comparison between HEA, GPSO and previous algorithms when running on Dataset (Heterogeneous - Binary) (Mev- Minimal exposure value, TimeComputational time (second)) 105 ix