基于GSO的无线传感监测网络部署优化研究

Research on optimization deployment of wireless sensor monitoring network based on group search optimizer

针对无线传感器网络应用于智能变电站设备状态监测存在节点能量有限和能耗不均衡的问题,根据分簇的设备监测网络模型特性和瑞利衰落环境下的节点能耗计算,引入簇头节点数据压缩比和传输周期,以节点簇总能耗、网络生存周期和距离因子作为综合目标函数,以多跳网络连通性作为约束,建立了网络部署的优化模型。采用群搜索算法GSO,以簇头节点感知半径、通信距离、数据压缩比和传输周期为优化目标,对提出的网络4种通信路径进行了优化比较。仿真和实验结果表明,合理的网络部署可以平衡多跳网络中簇头节点能耗、减弱关键簇头节点能耗对网络寿命的影响。同时,结果表明GSO算法比PSO算法在解决大规模无线传感器网络优化问题上,具有更好的计算速度和优化结果。

Aiming at the problems of limited energy and imbalanced energy consumption of sensor nodes of wireless sen- sor network applied to smart substation equipment condition monitoring, a network deployment optimization model is proposed using the penalty function method. In this model, according to the characteristics of the clustering network model for equipment monitoring and the energy consumption calculation of the nodes in Rayleigh fading environment, the data compression ratio and transmission period of cluster-head are introduced;the total energy consumption of the node clusters, network lifetime and distance factor are taken as the integrated objective functions;and the connectivity of multi-hop network is also taken into account as the constraint. Taking the sensing radius, communication distance, data compression ratio and transmission period of the cluster-head as the optimization objects, the group search optimi- zer （GSO） algorithm is used to optimize and compare the four communication paths of multi-hop network. Simulation and experiment results indicate that reasonable network deployment can balance the node cluster energy consumption and weaken the influence of the key node cluster energy consumption on network lifetime in multi-hop network. The simulation results also show that the GSO algorithm has better performances than the particle swarm optimization algo- rithm in terms of optimization result and computation speed in large scale wireless sensor network optimization.