Các thuật toán xấp xỉ tìm đường xâm nhập có khả năng bị phát hiện nhỏ nhất trong mạng cảm biến dây (approximate algorithms for solving the minimal exposure path problems in wireless sensor networks)

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 di erent areas inspired me and helped me overcome many di culties 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 di culties Furthermore, attending at scienti c 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 DECLARATION OF AUTHORSHIP ACKNOWLEDGEMENT CONTENTS SYMBOLS LIST OF TABLES LIST OF FIGURES INTRODUCTION BACKGROUND 1.1 Wireless sensor networks 1.1.1 1.1.2 1.1.3 1.1.4 1.1.5 1.1.6 Optimization problems Approximate algorithms 1.3.1 1.3.2 1.2 1.3 1.4 Conclusion MINIMAL EXPOSURE PATH PROBLEMS IN OMNI-DIRECTIONAL SENSOR NETWORKS 2.1 Minimal exposure path problem in mobile wireless s 2.1.1 2.1.2 2.1.3 iii 2.1.3 2.1.3 2.1.4 Expe 2.1.4 2.1.4 2.2 Minimal exposure path problem in probabilistic cove 2.2.1 Moti 2.2.2 Prel 2.2.2 2.2.2 2.2.3 Prop 2.2.3 2.2.3 2.2.4 Expe 2.2.4 2.2.4 2.3 Conclusion MINIMAL EXPOSURE PATH PROBLEM IN WIRELESS MULTIMEDIA SENSOR NETWORKS 3.1 Motivations 3.2 Preliminaries and problem formulation 3.2.1 Prel 3.2.1 3.2.1 3.2.1 3.2.1 3.2.1 3.2.2 Prob 3.3 Proposed algorithms 3.3.1 Indiv 3.3.2 Indiv 3.3.2 3.3.2 3.3.2 3.3.3 Evol 3.3.3 3.3.3 3.3.4 Sele 3.3.5 Com 3.4 Experimental results iv 3.4.1 Expe 3.4.2 3.4.1 3.4.1 Com 3.4.2 3.4.2 3.4.2 3.5 Conclusion OBSTACLES-EVASION MINIMAL EXPOSURE PATH PROBLEM IN WIRELESS SENSOR NETWORKS 4.1 Motivations 4.2 Preliminaries and problem formulation 4.2.1 Prel 4.2.1 4.2.1 4.2.1 4.2.1 4.2.2 Prob 4.3 Proposed algorithm 4.3.1 A no 4.3.1 4.3.1 4.3.2 Algo 4.3.2 4.3.2 4.3.2 4.3.2 4.3.2 4.3.2 4.3.2 4.3.3 Com 4.4 Experimental results 4.4.1 Data 4.4.2 Para 4.4.3 Com 4.4.3 4.4.3 4.4.3 v 4.4.3.4 Comparison between FEA and GA-MEP 130 4.5 Conclusion 133 CONCLUSIONS AND FUTURE WORKS PUBLICATIONS BIBLIOGRAPHY vi No Abbreviation WSNs IoT ROI BC MEP PSO NFE MWSN GPSO 10 HGA 11 FEA 12 HoWSNs 13 HeWSNs 14 SWSN 15 CO 16 TSP 17 QAP 18 ACO 19 EC 20 ILS 21 TS 22 GA 23 GLS 24 VNS 25 LS 26 HeWMSN vii Table Comparative table of re Table 1.1 Evolution process versu Table 2.1 uated disk model Experimental paramete Table 2.2 Parameters setting for G Table 2.3 Experimental paramete Table 2.4 Parameters setting for H Table 2.5 Di erent version of HPS Table 2.6 Computation results of HPSO-MMEP in comparison with GAMEP in uniform distribution of sensors (Mev: minimal exposure value, Sd: standard deviation) Table 2.7 Computation results of HPSO-MMEP in comparison with GAMEP in Gauss distribution of sensors (Mev: minimal exposure value, Sd: standard deviation) Table 2.8 Experimental paramete Table 2.9 Experimental paramete Table 2.10 Experimental Paramete Table 2.11 The comparison minimal exposure value, computation time and saw-tooth degree between GA-MEP and GB-MEP when using di erent subinterval s, the topology used is u time per unit second; Dst: saw-tooth degree) 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 Mev: the minimal exposure value obtains by GB-MEP; GA-Mev: the minimal exposure value obtains by GA-MEP) Table 2.13 Computation time comparison of OGB and GB-MEP when subinterval s varies from down-to 0.2 on instance u 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 nd 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 ˆ Secondly, the crossover operator in FEA is much more e ective since it can discover individuals with backward path which signi cantly 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 ˆ 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 Data 0:3 30 FEA Data 0:3 60 Mev: 0.1497 Data 0:5 30 Mev: 3.6252 Mev: 1.4978 Sensor 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 di erence 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 nd out a path that penetrates through the eld with the minimal exposure value and does not cross any obstacles.This problem is substantially bene cial 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 e ciently We consider obstacles with arbitrary shapes (to match realistic scenarios) and we model these obstacles as convex polygons and create random data sets to e ectively 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 e cient 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 e cient heuristic/metaheuristic algorithms to solve the proposed MEP problems The dis-sertation 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 sen-sor networks with several di erent 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: ˆ Formulating a minimal exposure path problem under the probabilistic coverage model with noise in a WSN, called PM-based-MEP A new de nition of exposure measure for this model is also introduced ˆ 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 ˆ 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 e ciently solve the problem, we design a new individual representation, an e cient crossover and a suitable mutation operator to form a genetic algorithm called GA-MEP ˆ 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: ˆ Establish mathematical models to represent the HM-MEP problem ˆ Propose two e cient 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 ˆ 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: ˆ 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 prob-lem ˆ Model the obstacles as convex polygons to match realistic scenarios and devise a method to randomly generate obstacles in di erent forms The newly created data set can serve as an e ective measurement for the performance of OE-MEP approaches ˆ Propose a new algorithm called Family System based Evolutionary Algorithm (FEA) for solving the OE-MEP problem e ciently ˆ Conduct a number of systematic simulations to study the performance of FEA under a variety of network scenarios as well as obstacles ˆ 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 e ciently, the dissertation has still some limitations as follows: ˆ The MEP problem has not addressed in WSNs under modern sensing coverage models such as full-view sensing coverage, k-angle sensing coverage yet ˆ The MEP problem has not solved in 3-Dimensions WSNs yet ˆ 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 three-dimensional 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 re ection 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 E cient 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 e cient approximate algorithm for achieving (k !) barrier coverage in camera wireless sensor networks In Arti cial 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, E cient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks 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Proposing the practical and useful models of the minimal exposure path problem in wireless sensor networks ˆ Pro ering e cient metaheuristic algorithms to solve the proposed minimal exposure path problems