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Area Coverage Problem in Wireless Sensor Networks

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st The UTS-VNU Research School Advanced Technologies for IoT Applications Area Coverage Problem in Wireless Sensor Networks Author Names and Affiliations Dinh Thi Ha Ly - Huynh Thi Thanh Binh Modeling, Simulation and Optimization Lab School of Information Communication and Technology Hanoi University of Science and Technology Problem Statement Abstract We are interested in a new model of area coverage problem in wireless sensor networks that is to maximize covered area in a region of interest with a given number of sensors instead of finding the minimum number of sensors such that the region of interest can be completely supervised Contributions  Input  Propose a new function to evaluate quality of solution: o k: the number of sensor types Olap o n: the number of sensors o ni: the number of sensors for type i (i = k), such that  Propose algorithms to solve this problem: k o Genetic algorithm (IGA) ni  n  i 1 o Particle Swarm Optimaztion (PSO, DPSO) o ri: the sensing radius of sensor for type i (i = k) o Cuckoo Search (ICS) o W, H: the width and the length of the 2D domain A o Chaotic Flower Pollination (CFPA) respectively  Analyse convergence of proposed algorithms  Output  Publication: Huynh Thi Thanh Binh, Nguyen Thi Hanh, La o The position for each sensor node Van Quan, Nilanjan Dey, Improved Cuckoo Search and  Objective Chaotic Flower Pollination Algorithms for Maximizing o Maximize the area coverage of n sensors on A (coA) k n Area Coverage in Wireless Sensor Networks, Neural coA  area ( cr ( xi , yi )  A)  max Computing and Applications (SCI-E Index, IF: 1.492) i i 1 j 1 i j j Results (a) (b) (c) (d) Convergence timve of IGA, DPSO, ICS and CFPA in the same instance 30 ruuning timve of IGA, PSO, DPSO, ICS and CFPA in the instances The best solustions found by IGA (a), PSO (b), ICS (c), CFPA (d) after 30 runnings times on the largest instance Future work References  Apply to wireless sensor networks with obstacles  Apply to dynamic wireless sensor networks  Integrate with other objectives and constraints: o Connectivity assuarance o Energy optimization  B Wang, “Coverage problems in sensor networks: A survey,” ACM Comput Surveys, vol 43, no 4, pp 32:1–32:53, 2011  Yourim Yoon, Yong-Hyuk Kim, “An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks”, IEEE Transactions on 43.5, pp 1473-1483, 2013

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