文献类型：期刊文献

英文题名：Coverage Optimization and Simulation of Wireless Sensor Networks Based on Particle Swarm Optimization

作者：Zhang, Ye[1]

第一作者：张翊

通讯作者：Zhang, Y[1]

机构：[1]Xinxiang Univ, Sch Elect & Mech Engn, Xinxiang 453000, Henan, Peoples R China

第一机构：新乡学院

通讯机构：[1]corresponding author), Xinxiang Univ, Sch Elect & Mech Engn, Xinxiang 453000, Henan, Peoples R China.|[11071]新乡学院;

年份：2020

卷号：27

期号：2

起止页码：307-316

外文期刊名：INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS

收录：EI(收录号：20193207286099);Scopus(收录号：2-s2.0-85083756655);WOS:【ESCI(收录号:WOS:000527675800012)】；

语种：英文

外文关键词：Wireless sensor networks; Coverage optimization; External decentralization; Stochastic particle swarm optimization; Coverage optimization model

摘要：As a new type of measurement and control network, wireless sensor networks (WSNs) technology has the characteristics of diversity and wide practicability. Based on the characteristics of WSNs, wireless sensor networks are widely used in military and civil industries. However, due to the frequent changes in the basic topology of wireless sensor networks and the relatively small number of corresponding network nodes, the efficiency of the nodes is the reason for its further development. Therefore, the node distribution strategy and the corresponding network coverage optimization strategy of wireless sensor networks are of great significance to achieve energy saving and network life extension. In order to solve the above WSNs problems, this paper will analyze and simulate the coverage optimization of wireless sensor networks based on Improved Particle Swarm Optimization algorithm. Firstly, this paper will analyze the structure characteristics of wireless sensor networks, complete the WSNs network coverage model and get the corresponding functions. Then this paper innovatively proposes "external dispersion method" to solve the problem of local area overlap in WSNs network coverage. At the same time, it innovatively proposes "free-particle swarm optimization" to solve the local convergence of conventional particle swarm optimization. At the end of this paper, simulation experiments are carried out to compare the optimal particle swarm optimization algorithm with the traditional particle swarm optimization algorithm. The experiments show that the proposed algorithm has obvious advantages in convergence and coverage.