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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 Hanoi, 2020 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 cosupervisors, Associate Professor Huynh Thi Thanh Binh and Associate Professor Nguyen Duc Nghia All the research results are presented in the dissertation which have never been published by others Hanoi, October 16, 2020 Ph.D Student Nguyen Thi My Binh SUPERVISOR Asso Prof 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 co-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, October 16, 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 Wireless sensor networks 1.1.1 Sensors 1.1.2 Sensor nodes 1.1.3 Sensor coverage model 1.1.4 Sensing intensity models 1.1.5 Terminologies 1.1.6 Wireless sensor network scenarios 1.2 Optimization problems 1.3 Approximate algorithms 1.3.1 Single-solution-based metaheuristic 1.3.2 Population-based metaheuristics 1.3.2.1 Evolutionary algorithms 1.3.2.2 Particle swarm optimization 1.4 Conclusion 10 10 10 11 11 12 12 14 15 17 21 22 23 26 29 MINIMAL EXPOSURE PATH PROBLEMS IN OMNI-DIRECTIONAL SENSOR NETWORKS 2.1 Minimal exposure path problem in mobile wireless sensor networks 2.1.1 Motivations 2.1.2 Preliminaries and problem formulation 2.1.2.1 Preliminaries 2.1.2.2 Problem formulation 2.1.3 Proposed algorithms 2.1.3.1 The GAMEP for solving the MMEP problem 30 30 30 31 31 33 34 34 iii algorithm 2.2 2.3 2.1.3.2 The HPSO-MMEP algorithm for solving the MMEP problem 2.1.3.3 Complexity analysis 2.1.4 Experimental results 2.1.4.1 Experimental settings 2.1.4.2 Computation results Minimal exposure path problem in probabilistic coverage model 2.2.1 Motivations 2.2.2 Preliminaries and problem formulation 2.2.2.1 Preliminaries 2.2.2.2 Problem formulation 2.2.3 Proposed algorithms 2.2.3.1 Grid-based algorithm for solving the PM-based-MEP problem 2.2.3.2 Genetic algorithm for solving the PM-based-MEP problem 2.2.4 Experimental results 2.2.4.1 Experimental setting 2.2.4.2 Computation results Conclusion MINIMAL EXPOSURE PATH PROBLEM IN WIRELESS DIA SENSOR NETWORKS 3.1 Motivations 3.2 Preliminaries and problem formulation 3.2.1 Preliminaries 3.2.1.1 The Boolean directional coverage model 3.2.1.2 The attenuated directional sensing model 3.2.1.3 Accumulative intensity function 3.2.1.4 Closest-sensing intensity function 3.2.1.5 Minimal exposure path 3.2.2 Problem formulation 3.3 Proposed algorithms 3.3.1 Individual representation 3.3.2 Individual initialization 3.3.2.1 HEA individual initialization 3.3.2.2 GPSO individual initialization 3.3.2.3 Fitness function 3.3.3 Evolutionary operators 3.3.3.1 Evolutionary algorithm 3.3.3.2 Particle swarm optimization algorithm 3.3.4 Selection operator 3.3.5 Complexity analysis 3.4 Experimental results iv 38 42 43 43 44 50 50 51 51 53 55 55 56 64 64 66 81 82 82 83 83 83 83 84 85 85 86 87 87 88 88 89 90 91 91 94 97 98 98 MULTIME 3.4.1 3.5 Experimental setting 3.4.1.1 Datasets 3.4.1.2 Parameters and system setting 3.4.2 Computational results 3.4.2.1 Algorithm parameters trials 3.4.2.2 Comparison under our datasets 3.4.2.3 Comparisons under the datasets of previous algorithms Conclusion 98 98 99 100 101 104 109 111 OBSTACLES-EVASION MINIMAL EXPOSURE PATH PROBLEM IN WIRELESS SENSOR NETWORKS 112 4.1 Motivations 112 4.2 Preliminaries and problem formulation 112 4.2.1 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 4.3 Proposed algorithm 116 4.3.1 A novel characteristic of FEA algorithm 117 4.3.1.1 Individual 117 4.3.1.2 Population 119 4.3.2 Algorithm progress 120 4.3.2.1 Initialization 120 4.3.2.2 Family pairing 120 4.3.2.3 Crossover 121 4.3.2.4 Mutation 122 4.3.2.5 Update 123 4.3.2.6 Selection 123 4.3.2.7 Family system based evolutionary algorithm 124 4.3.3 Complexity analysis 124 4.4 Experimental results 124 4.4.1 Dataset 124 4.4.2 Parameters 126 4.4.3 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 4.5 4.4.3.4 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 Partical 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 GLS Guided Local Search 24 VNS Variable Neighborhood Search 25 LS Local Search 26 HeWMSN Heterogeneous Wireless Multimedia Sensor Networks 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 69 viii Figure 4.12 The computational time (sec) comparison between FEA and GA-MEP on some noble topologies not have to remove invalid individuals, but also search for more paths that move along the boundaries of obstacles As a common sense, the paths along the boundaries of the obstacles often have fairly low exposure value since the sensing wave is being absorbed by obstacles This setting also allows FEA to find path through narrow alleys between obstacles which are usually critical weak point in security The minimal exposure paths are obtained by FEA in Figure 4.13 for example, have many paths go along obstacle boundaries or through narrow passages that emit very low exposure value ❼ Secondly, the crossover operator in FEA is much more effective since it can discover individuals with backward path which significantly enlarge the search space Since the obstacles in the region is non-cross-able, the intruder in many case will require a backward way to reach the best penetration path In many topologies, the solution gives by FEA contains backward paths that allow the exposure value to get even lower than the one gives by GA-MEP In the minimal exposure path example showed in Figure 4.13, the MEP found by FEA in data 0.5 30 has backward paths while the one found by GA-MEP only contains forward paths In this particular case and also very usually, the backward paths contains valuable genes with very low exposure, thus, the Mev of FEA is better than the Mev of GA-MEP ❼ Thirdly, the family system and the dynamic population size of FEA help improve the diversity of the population and reduce the chance of local optima Therefore, FEA can be more stable than GAMEP does, especially when performing on a highly complex search space such as OE-MEP problem For the computational time, GA-MEP is more or less faster than FEA which is fair and rea131 GA-MEP Grid-based Mev: 0.1497 Mev: 1.9341 Mev: 111.7348 Mev: 3.6252 Mev: 9.9426 Mev: 136.8566 Mev: 1.4978 Mev: 5.7483 Mev: 45.9453 Data 0.5 30 Data 0.3 60 Data 0.3 30 FEA Sensor Obstacle MEP Figure 4.13 The Minimal Exposure Path is achieved by FEA, GA-MEP and Grid-based method on some noble topologies 132 sonable due to FEA has more complex operator than GA-MEP does However, the gain on computational time is acceptable since the difference is not high In summaries, performance of FEA is better than GA-MEP when performing on the OE-MEP problem 4.5 Conclusion This Chapter proposes to investigate the OE-MEP problem in WSN where obstacles are presented in their arbitrary shapes, and can be used as a tool to determine the weaknesses in coverage level of a given sensor network The goal of the OE-MEP problem is to find out a path that penetrates through the field with the minimal exposure value and does not cross any obstacles.This problem is substantially beneficial for network designers who can apply established formulas to evaluate the quality of coverage of the provided WSN without costly deployment and test The OE-MEP is formulated and presented as a generic mathematical model which then is converted into an optimization problem with constraints We create a family system based evolutionary algorithm (FEA) in an attempt to solve this OE-MEP problem efficiently We consider obstacles with arbitrary shapes (to match realistic scenarios) and we model these obstacles as convex polygons and create random data sets to effectively measure the performance of the OE-MEP approach We then conduct numerous systematic simulations to test the performance of our proposed FEA algorithm with a variety of network scenarios and obstacles The results show evidence that FEA is strongly suitable for solving the OE-MEP problem and more efficient than prior approaches regarding solution quality and computational time 133 CONCLUSIONS AND FUTURE WORKS Contributions This dissertation surveys the approximation algorithms for dealing with the barrier coverage problem in wireless sensor networks Especially, it has studied and proposed some new models of the MEP problem in Dimensions which is roof of barrier coverage problem, devised the efficient heuristic/metaheuristic algorithms to solve the proposed MEP problems The dissertation has addressed the MEP problem in WSNs completely, and achieved the highlight research results as follows: The main results of the dissertation include: For omni-directional mobile wireless sensor networks, the MEP problem in mobile wireless sensor networks with several different sensor coverage models (MMEP problem) is studied and has been obtained some emphasized results as follows: Regarding omni-directional static wireless sensor networks, we have investigated the MEP problem based on probabilistic coverage model with noise (PM-based-MEP), and accomplished some results as follows: ❼ Formulating a minimal exposure path problem under the probabilistic coverage model with noise in a WSN, called PM-based-MEP A new definition of exposure measure for this model is also introduced ❼ Converting the PM-based-MEP into an optimization problem with a objective function and constraints which permit the use of mathematical optimization methods to solve ❼ Proposing the GB-MEP algorithm to obtain the solution based on the traditional grid- based method incorporated with several improvements To enhance the search space and more efficiently solve the problem, we design a new individual representation, an efficient crossover and a suitable mutation operator to form a genetic algorithm called GA-MEP ❼ Conducting experiments in various scenarios to examine the proposed algorithms Quality of solutions and computation time are compared with existing methods and analyzed to give insights into the use of each algorithm in the PM-based-MEP In term of heterogeneous directional wireless sensor networks, the MEP problem in HWDSNs (HM-MEP) is focused, and has been obtained the main results: ❼ Establish mathematical models to represent the HM-MEP problem ❼ Propose two efficient meta-heuristic algorithms: HEA - a hybrid evolutionary algorithm in combination with local search and GPSO - a novel particle swarm optimization based on the gravity force theory ❼ Analysis, evaluate and compare the experimental results and show that our proposed 134 algorithms outperform the previous methods for most cases regarding quality solution and computation time With respect to the MEP problem in real-world WSN scenarios, the MEP problem in WSNs with arbitrary-shape obstacles is addressed successfully, and has been achieved the results as follows: ❼ Formulate a generic mathematical model to represent the arbitrary shape obstacles-evade MEP problem in WSNs, called OE-MEP and convert OEMEP into an optimization problem ❼ Model the obstacles as convex polygons to match realistic scenarios and devise a method to randomly generate obstacles in different forms The newly created data set can serve as an effective measurement for the performance of OE-MEP approaches ❼ Propose a new algorithm called Family System based Evolutionary Algorithm (FEA) for solving the OE-MEP problem efficiently ❼ Conduct a number of systematic simulations to study the performance of FEA under a variety of network scenarios as well as obstacles ❼ Analyze the experimental results to prove that FEA adapts to the OEMEP problem and outperforms prior approaches regarding solution quality and computational time Limitations Although the MEP problems in WSNs have been studied under several sensing coverage models efficiently, the dissertation has still some limitations as follows: ❼ The MEP problem has not addressed in WSNs under modern sensing coverage models such as full-view sensing coverage, k-angle sensing coverage yet ❼ The MEP problem has not solved in 3-Dimensions WSNs yet ❼ The MEP problem has not taken into account rotating capability of sensor nodes Future works Although barrier coverage has been actively studied by academic community, there is still a long way to go before it can be applied into large-scale practical systems In our future work, we would like to focus on applying barrier coverage into real systems Three-dimension barrier coverage Most prior researches studied on the barrier coverage problem in two-dimensional spaces The gap between theory and practice will cause that laboratory researches cannot be applied in large-scale practical systems However, in many practical scenarios, sensors deployed in the 135 atmosphere, in underwater or in the sea, and the outer space may need to guarantee the barrier covered in three-dimensional space, or referred to shell coverage It is not straightforward to extend the approaches proposed for two-dimensional barrier coverage to adapt to threedimensional barrier coverage, and new algorithms need to be designed Practical sensing coverage model Most of existing barrier coverage solutions, the directional sensing model models are simple and ideal such as binary sensing model, attenuated sensing model, etc Recently, practical sensing coverage model, full-view coverage model, and some application-oriented barrier coverage, bring new reflection and enlightenment to the design of barrier coverage solutions Sensor with rotating capabilities In directional sensing coverage models, a sensor can only sense in the direction of its orientation A rotating directional sensor can change the orientation of its sensor at a certain rotational speed to provide good coverage When these sensors are randomly deployed in the ROI, one interesting problem is how to select appropriate rotating directional sensors and their working orientations to guarantee barrier coverage 136 PUBLICATIONS [1] Binh, N.T.M., Thang, C.M., Nghia, N.D and Binh, H.T.T., 2017, Genetic algorithm for solving minimal exposure path in mobile sensor networks In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp.1-8) [2] Binh, H.T.T., Binh, N.T.M., Ngoc, N.H., Ly, D.T.H and Nghia, N.D., 2019 Efficient approximation approaches to minimal exposure path problem in probabilistic coverage model for wireless sensor networks Applied Soft Computing, 76, pp 726-743 (SCIE, Q1, IF 4.873) [3] Binh, N.T.M., Binh, H.T.T., Le Loi, V., Nghia, V.T., San, D.L and Thang, C.M., 2019, An efficient approximate algorithm for achieving (k − ω) barrier coverage in camera wireless sensor networks In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications (Vol 11006, p 1100613) International Society for Optics and Photonics [4] Binh, N.T.M., Binh, H.T.T., Van Linh, N and Yu, S., 2020, Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in Internet of Things Applied Intelligence, 50, pp 1889–1907 (SCIE, Q2, IF 3.325) [5] Binh, N.T.M., Abdelhamid Mellouk, Binh, H.T.T., Loi, L.V, San, D.L, Anh, T.H, 2020, An elite hybrid particle swarm optimization for solving minimal exposure path problem in mobile wireless sensor networks Sensors, 9, pp 2586-2611 (SCIE, Q1, IF 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Symposium (ANSS’06), pages 11–pp IEEE, 2006 145 ... sensing field beginning at the point B and ending at the point E is a path for any point on the path ℘, the distance from ℘ to the closest sensor is maximum They then designed an algorithm using... application, the requirements in solving the BC problem are different Finding penetration path, especially the minimal exposure path problem is the superior version of the barrier coverage [29] The minimal. .. higher if the intruder stays longer in the sensing field The goal of the MMEP problem is to find out a penetration path ℘ from the beginning point B to the ending point E such that the exposure
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