基于AIPSO的传感器网络动态节点部署策略  

AIPSO-Based Dynamic Node Deployment Strategy of Sensor Network

作　　者：俞垚魏 李云龙[2,3] 岳川 袁伟 李艳峰[5] YU Yaowei;LI Yunlong;YUE Chuan;YUAN Wei;LI Yanfeng(School of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing Jiangsu 211106,China;PLA 31539 Army,Beijing 100144,China;School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China;PLA 66736 Army,Beijing 100095,China;Automobile NCO Academy,Army Military Transportation University,Bengbu Anhui 233001,China)

机构地区：[1]南京航空航天大学民航学院,江苏南京211106 [2]中国人民解放军31539部队,北京100144 [3]北京理工大学信息与电子学院,北京100081 [4]中国人民解放军66736部队,北京100095 [5]陆军军事交通学院汽车士官学校,安徽蚌埠233001

出　　处：《传感技术学报》2025年第2期322-331,共10页Chinese Journal of Sensors and Actuators

基　　金：国家社科基金军事学(2020-SKJJ-C-011);山东省自然科学基金(ZR2022QF091)。

摘　　要：利用传感器网络对任务区域进行监测是保障区域安全稳定的重要手段。多传感器组建的覆盖网络可为区域提供高效的感知和通信服务。理想的传感器部署策略是实现网络覆盖最大化的必要条件。当部分固定传感器功能失效导致监测区域出现覆盖空洞,可以通过调整周围可移动传感器实施快速修复。首先建立了传感器网络节点部署模型。其次,针对传感器网络节点部署特征,提出了基于人工免疫机制的粒子群优化算法(Artificial Immune-based Particle Swarm Optimization,AIPSO),提高了种群的多样性,解决了传统优化算法中容易出现的早熟收敛和局部最优值问题,提升了节点部署效率。仿真结果表明,与传统粒子群算法(Particle Swarm Optimization,PSO)、基于量子行为的粒子群优化算法(Quantum Particle Swarm Optimization,QPSO)以及改进的免疫粒子群算法(Improved Immune Particle Swarm Optimization,IIPSO)相比,AIPSO算法从整体上减少了动态传感器的移动距离,同时能够最大程度地保持传感器网络的覆盖率和节点覆盖效率。Using sensor network to monitor mission area is an important means to ensure regional security and stability.The multi-sensor overlay network can provide efficient sensing and communication services for the region.An ideal sensor deployment strategy is the nec-essary condition to maximize network coverage.When some fixed sensors fail and cover holes appear in the monitoring area,quick repair can be implemented by adjusting the surrounding movable sensors.Firstly,the deployment model of sensor network node is established.Secondly,targeting at the deployment characteristics of sensor network nodes,artificial immune-based particle swarm optimization(AIP-SO)is proposed to improve the diversity of the population.The problems of early convergence and local optimal value appeared in the traditional optimization algorithm are solved and the node deployment efficiency is improved.The simulation results show that,compared with the traditional particle swarm optimization(PSO),quantum particle swarm optimization(QPSO)and improved immune particle swarm optimization(IIPSO),AIPSO algorithm reduces the moving distance of dynamic sensors on the whole.At the same time,it can maximize the coverage rate and node coverage efficiency of the sensor network.

关 键 词：传感器网络 动态节点 部署策略 人工免疫 粒子群优化 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]