Research Article
Pareto Optimal Solution for Multi-objective Optimization in Wireless Sensor Networks
@INPROCEEDINGS{10.1007/978-3-030-43690-2_33, author={Haimanot Alemayehu and Mekuanint Bitew and Birhanu Shiret}, title={Pareto Optimal Solution for Multi-objective Optimization in Wireless Sensor Networks}, proceedings={Advances of Science and Technology. 7th EAI International Conference, ICAST 2019, Bahir Dar, Ethiopia, August 2--4, 2019, Proceedings}, proceedings_a={ICAST}, year={2020}, month={6}, keywords={Pareto optimal Multi-objective Energy consumption Coverage Wireless sensor networks}, doi={10.1007/978-3-030-43690-2_33} }- Haimanot Alemayehu
Mekuanint Bitew
Birhanu Shiret
Year: 2020
Pareto Optimal Solution for Multi-objective Optimization in Wireless Sensor Networks
ICAST
Springer
DOI: 10.1007/978-3-030-43690-2_33
Abstract
A wireless sensor network (WSN) consists of small sensors with limited sensing range, processing capability, and short communication range. The performance of WSNs is determined by multi-objective optimization. However, these objectives are contradictory and impossible to solve optimization problems with a single optimal decision. This paper presents multi-objective optimization approach to optimize the coverage area of sensor nodes, minimize the energy consumption, and maximize the network lifetime and maintaining connectivity between the current deployed sensor nodes. Pareto optimal based approach is used to address conflicting objectives and trade-offs with respect to non-dominance using non-dominating sorting genetic algorithm 2 (NSGA-2). The tools we have used for simulation are: NS2 simulator, tool command language script (TCL) and C language and Aho Weinberger keninghan script (AWK) are used. We have checked the coverage area, packet deliver ratio, and energy consumption of sensor nodes to evaluate the performance of proposed scheme. According to the simulation results, the packet delivery ration is 0.93 and the coverage ratio of sensor to region of interest is 0.65.