基于元胞蝙蝠算法的无线传感器网络节点定位研究
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摘要
为了提高节点定位精度,解决定位误差较大的问题,提出了基于元胞蝙蝠算法的无线传感器网络节点定位算法,以此来获得更高的定位精度。首先将元胞自动机的思想融入蝙蝠算法,采用了改进的元胞限制竞争选择小生境技术和灾变机制,使得该算法在寻优过程中能够跳出局部极值,避免早熟现象,更快地收敛到全局最优解。通过标准测试函数的验证,表明了该改进算法在收敛深度和广度上的优势。之后将元胞蝙蝠算法应用到无线传感器网络节点定位上来提高定位精度。实测实验中,该算法在测试环境下平均定位误差在0.4 m以内,相比于改进PSO算法,获得更好的定位效果。
To further enhance the location precision of unknown nodes and solve the node location error in wireless sensor network, a location method based on cellular automata bat algorithm was presented. Mixed the idea of cellular automata and the bat algorithm and drawed into the cellular RCS niche technique and disaster mechanism, the algorithm could jump out of local optimum and increase the convergence speed. In order to verify the feasibility and efficiency, the proposed algorithm was verified through simulation of several benchmark functions. Then the algorithm implemented the CA-BA to node location prediction to increase the precision of the unknown node location. In the measured experiment, the results show that the proposed algorithm has higher accuracy compared to the improved PSO algorithm, which average localization error is less than 0.5 m.
To further enhance the location precision of unknown nodes and solve the node location error in wireless sensor network, a location method based on cellular automata bat algorithm was presented. Mixed the idea of cellular automata and the bat algorithm and drawed into the cellular RCS niche technique and disaster mechanism, the algorithm could jump out of local optimum and increase the convergence speed. In order to verify the feasibility and efficiency, the proposed algorithm was verified through simulation of several benchmark functions. Then the algorithm implemented the CA-BA to node location prediction to increase the precision of the unknown node location. In the measured experiment, the results show that the proposed algorithm has higher accuracy compared to the improved PSO algorithm, which average localization error is less than 0.5 m.
引文
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[2]方震,赵湛,郭鹏,等.基于RSSI测距分析[J].传感技术学报,2007(11):2526-2530.FANG Z,ZHAO Z,GUO P,et al.Analysis of distance measurement based on RSSI[J].Chinese Journal of Sensors and Actuators,2007,20(11):2526-2530.
[3]焦磊,邢建平,张军,等.一种非视距环境下具有鲁棒特性TOA无线传感网络定位算法[J].传感技术学报,2007,20(7):1625-1629.JIAO L,XING J P,ZHANG J,et al.A new NLOS TOA-based wireless sensor network localization algorithm with robust character[J].Chinese Journal of sensors actuators,2007,20(7):1625-1629.
[4]刘长平,叶春明.具有混沌搜索策略的蝙蝠优化算法及性能仿真[J].系统仿真学报,2013,25(6):1183-1188,1195.LIU C P,YE C M.Bat algorithm with chaotic search strategy and analysis of its property[J].Journal of System Simulation,2013,25(6):1183-1188,1195.
[5]赖锦辉.基于蝙蝠优化算法的无线传感器网络节点定位研究[J].计算机测量与控制,2014,22(8):2709-2712.LAI J H.Research on nodes localization method for wireless sensor networks based on bat optimization algorithm[J].Computer Measurement&Control,2014,22(8):2709-2712.
[6]尚俊娜,刘春菊,岳克强,等.多智能体蝙蝠算法在无线传感器中的应用[J].传感技术学报,2015,28(9):1418-1424.SHANG J N,LIU C J,YUE K Q,et al.The multi-agent bat algorithm applied to wireless sensor network[J].Chinese Journal of Sensors and Actuators,2015,25(9):1418-1424.
[7]朱大林,詹腾,张屹,等.多策略差分进化的元胞多目标粒子群算法[J].电子学报,2014,42(9):1831-1838.ZHU D L,ZHAN T,ZHANG Y,et al.Cellular multi-objective particle swarm algorithm based on multi-strategy differential evolution[J].Acta Electronica Sinica,2014,42(9):1831-1838.
[8]张屹,万兴余,郑小东,等.基于正交设计的元胞多目标遗传算法[J].电子学报,2016,44(1):87-94.ZHANG Y,WAN X Y,ZHENG X D,et al.Cellular genetic algorithm for multiobjective optimization based on orthogonal design[J].Acta Electronica Sinica,2016,44(1):87-94.
[9]YANG X S.A new metaheuristic bat-inspired algorithm[J].Nature Inspired Cooperative Strategies for Optimization,2010(284):65-74.
[10]石杨.元胞粒子群优化算法及其在柔性作业车间调度中的应用[D].武汉:华中科技大学,2010:8-10.SHI Y.A thesis submitted in partial fulfillment of the requirements for the degree of master of engineering[D].Wuhan:Huazhong University of Science and Technology,2010:8-10.
[11]张俞.元胞遗传算法的研究[D].江西:南昌航空大学,2009:37-41.ZHANG Y.Research for cellular genetic algorithm[D].Jiangxi:Nanchang Hangkong University,2009:37-41.
[12]李新鹏,张超勇,高亮,等.基于元胞粒子群算法的数控切削参数优化[J].计算机工程与应用,2014,50(2):252-257.LI X P,ZHANG C Y,GAO L,et al.NC cutting parameter optimization based on cellular particle swarm optimization algorithm[J].Computer Engineering and Applications,2014,50(2):252-257.
[13]鲁宇明,陈殊,黎明,等.自适应调整选择压力的灾变元胞遗传算法[J].系统仿真学报,2013,25(3):436-444.LU Y M,CHEN S,LI M,et al.Self-adaptive cellular genetic algorithms with disaster based on selection pressure[J].Journal of System Simulation,2013,25(3):436-444.
[14]詹杰,刘宏立,刘述钢,等.基于RSSI的动态权重定位算法研究[J].电子学报,2011,39(1):82-88.ZHAN J,LIU H L,LIU S G,et al.The study of dynamic degree weighted centroid localization algorithm based on RSSI[J].Acta Electronic Sinica,2011,39(1):82-88.
[15]尚俊娜,盛林,程涛,等.基于LQI权重和改进粒子群算法的室内定位方法[J].传感技术学报,2017,30(2):284-290.SHANG J N,SHENG L,CHENG T,et al.The indoor localization based on LQI weight and improved particle swarm optimization algorithm[J].Chinese Journal of Sensors and Actuators,2017,30(2):284-290.