面向软件定义无线传感器网络部署的细菌觅食优化算法  

A Bacteria Foraging Optimization Algorithm for Software-Defined Wireless Sensor Network Deployment

作　　者：毛明 胡涛[2] 张光辉[1] 李建龙 孙艳红 MAO Ming;HU Tao;ZHANG Guanghui;LI Jianlong;SUN Yanhong(Unit 68002,Lanzhou 730000,China;Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]68002部队,甘肃兰州730000 [2]信息工程大学,河南郑州450001

出　　处：《信息工程大学学报》2025年第2期245-252,共8页Journal of Information Engineering University

摘　　要：软件定义无线传感器网络(SDWSN)能够更好地解决路由数据的聚合和网络拓扑控制问题。然而,如何将这种集中控制模式与WSN的分布式结构部署相结合仍然面临着资源限制、节点异构性等诸多挑战。针对以上问题,提出一种基于细菌觅食优化算法的SDWSN部署方案,用于实现控制节点的部署和传感器节点网络动态自适应聚类。该自适应函数包含能量、距离和负载均衡3个因素,通过控制节点间的协作和自适应调整来提升网络弹性和运行效率。仿真结果表明,所提方案在能量消耗、网络效率和网络寿命3个方面均优于基于基因突变的鲸鱼优化算法(GM-WOA)和非线性加权粒子群优化算法(NWPSO)等对比方案。Software-defined wireless sensor networks(SDWSN)are considered to better address the issue of routing data aggregation and network topology control.However,it still faces many challenges such as resource limitation and node heterogeneity how to integrate this centralized control mode with the distributed deployment of WSN.To solve the above problems,an SDWSN deployment scheme based on bacterial foraging optimization algorithm is proposed,which is utilized to realize the deployment of control nodes and dynamic adaptive clustering of sensor node network.Three factors,namely energy,distance and load balancing,are incorporated into the adaptive function.The collaboration among nodes and the adaptive adjustment are controlled by using the function to enhance the network resilience and operational efficiency.Simulation results indicate that compared with genetic mutationbased whale optimization algorithm(GM-WOA)and non-linear weight particle swarm optimization algorithm(NWPSO),superior performance is achieved by using the proposed scheme in energy consumption,network efficiency and network lifetime.

关 键 词：软件定义网络 无线传感器网络 细菌觅食优化算法 控制节点 网络寿命 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]