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基于改进GWO和贪婪算法的覆盖优化方法

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  • 国家科技期刊平台
  • NETL
  • NSTL
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针对能量异构无线传感器网络中节点随机部署时,节点冗余造成覆盖率低的问题,提出一种基于改进灰狼优化和贪婪算法的两阶段覆盖优化方法IGWO-GA.首先,将静态节点和移动节点随机部署在目标区域内;其次,根据网络的覆盖率、节点的能量和虚拟移动距离建立多因素协同适应度函数,将灰狼包围策略划分为内层包围和外层包围,并提出猎物权重因子动态分配策略,确定移动节点的初选位置序列;最后,在终选位置优化阶段,提出贪婪算法确定节点与初选位置的最优匹配,重新进行节点部署,从而完成覆盖优化.仿真结果表明,相较于DPSO、IPSO-IRCD、GWO、GRDSA,IGWO-GA能够有效提高网络覆盖率,降低节点能耗,延长网络生命周期.
Coverage Optimization Based on Improved GWO and Greedy Algorithms
To address the problem of low coverage due to node redundancy when nodes are deployed randomly in energy heterogeneous wireless sensor networks,a two-stage coverage optimization method based on improved gray wolf optimization and greedy algorithm(IGWO-GA)is proposed.Firstly,static and mobile nodes are randomly deployed in the target area.Secondly,a multi-factor cooperative fitness function is established based on the coverage of the network,the energy of the nodes and the virtual moving distance,and the gray wolf encirclement strategy is divided into inner encirclement and outer encirclement,and a strategy for dynamically allocating weight fac-tors to the prey is proposed to determine the primary sequence of mobile node locations.Finally,in the final location optimization stage,a greedy algorithm is proposed to determine the optimal matching of nodes with the initial selected location and redeploy the nodes,com-pleting the coverage optimization.The simulation results show that compared with DPSO,IPSO-IRCD,GRDSA and GWO,the IGWO-GA algorithm can effectively improve the network coverage,reduce the node energy consumption,and extend the network life cycle.

heterogeneous wireless sensor networkscoverage optimizationgray wolf optimization algorithmmulti-factor synergyencir-clement strategydynamic weightsgreedy algorithm

苟平章、郭保永、郭苗

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西北师范大学计算机科学与工程学院,甘肃 兰州 730070

异构无线传感器网络 覆盖优化 灰狼优化算法 多因素协同 包围策略 动态权重 贪婪算法

国家自然科学基金项目国家自然科学基金项目

7196102862261048

2024

10.3969/j.issn.1004-1699.2024.09.018

传感技术学报
东南大学 中国微米纳米技术学会

传感技术学报

CSTPCD北大核心
影响因子：1.276
ISSN：1004-1699
年,卷(期)：2024.37(9)