摘要
为了解决无线传感器网络覆盖率控制收敛性较差、自适应能力不强的问题,提出了WSN网络覆盖问题假设,并根据网络有效覆盖率及任务节点数量,构建WSN网络覆盖控制问题模型.以粒子群算法为基础,采用自适应粒子群优化算法获取最佳覆盖区域,在惯性权重系数中加入演变因子和聚合因子,改善粒子群算法的适应性,通过增加碰撞回弹策略,优化粒子群的多元性,实现WSN网络覆盖的最优控制.结果表明,所提方法自适应能力强,具有较高的覆盖率,可降低控制网络节点移动产生的耗能.
In order to solve the problem that the convergence of wireless sensor network coverage control is poorer and the adaptive ability is not strong,WSN network coverage question assumptions were proposed.According to the effective network coverage and the task node quantity,a WSN network coverage control model was built.In terms of particle swarm optimization(PSO)algorithm,an adaptive particle swarm optimization algorithm was proposed to obtain the best coverage,and the evolution and aggregation factors were added to the inertia weight coefficient for the improvement of PSO adaptability.Through the strategy of increasing collision rebound,the diversity of PSO was optimized,and the optimal coverage control of WSN network was realized.The results show that the as-proposed method has strong adaptive ability and good convergence of coverage control,and can reduce the energy consumption generated by the control of network node movement.
作者
聂文梅
宋晓霞
NIE Wen-mei;SONG Xiao-xia(School of Computer and Network Engineering,Shanxi Datong University,Datong 037009,China)
出处
《沈阳工业大学学报》
CAS
北大核心
2023年第4期459-464,共6页
Journal of Shenyang University of Technology
基金
山西省自然科学基金面上项目(201901D111311)
山西大同市重点研发项目(2020023)
山西大同大学校级项目(2019k5).
关键词
无线传感器网络
粒子群算法
演变因子
聚合因子
感知半径
惯性权重系数
wireless sensor network
particle swarm optimization algorithm
evolution factor
aggregation factor
perceived radius
inertia weight coefficient
