Selection of Metaheuristic Algorithm to Design Wireless Sensor Network  

作　　者：Rakhshan Zulfiqar Tariq Javed Zain Anwar Ali Eman H.Alkhammash Myriam Hadjouni 

机构地区：[1]Hamdard University,Karachi,75270,Pakistan [2]Electronic Engineering Department,Sir Syed University of Engineering&Technology,Karachi,Pakistan [3]Department of Computer Science,College of Computers and Information Technology,Taif University,P.O.Box 11099,Taif,21944,Saudi Arabia [4]Department of Computer Sciences,College of Computer and Information Science,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia

出　　处：《Intelligent Automation & Soft Computing》2023年第7期985-1000,共16页智能自动化与软计算（英文）

基　　金：Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R193),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：The deployment of sensor nodes is an important aspect in mobile wireless sensor networks for increasing network performance.The longevity of the networks is mostly determined by the proportion of energy consumed and the sensor nodes’access network.The optimal or ideal positioning of sensors improves the portable sensor networks effectiveness.Coverage and energy usage are mostly determined by successful sensor placement strategies.Nature-inspired algorithms are the most effective solution for short sensor lifetime.The primary objective of work is to conduct a comparative analysis of nature-inspired optimization for wireless sensor networks(WSNs’)maximum network coverage.Moreover,it identifies quantity of installed sensor nodes for the given area.Superiority of algorithm has been identified based on value of optimized energy.The first half of the paper’s literature on nature-inspired algorithms is discussed.Later six metaheuristics algorithms(Grey wolf,Ant lion,Dragonfly,Whale,Moth flame,Sine cosine optimizer)are compared for optimal coverage of WSNs.The simulation outcomes confirm that whale opti-mization algorithm(WOA)gives optimized Energy with improved network coverage with the least number of nodes.This comparison will be helpful for researchers who will use WSNs in their applications.

关 键 词：BIO-INSPIRED computing EVOLUTIONARY COMPUTATION greedy algorithms wireless sensor network computational intelligence 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]