WSN中一种平衡节点能耗的分簇算法研究

Research on the Clustering Algorithm of a Balanced Energy Consumption of Nodes for WSN

针对无线传感器网络(WSN)中能量消耗和节点死亡过高的问题,在分析LEACH-C集中式分簇算法的基础上,提出了一种基于量子行为粒子群优化的WSN分簇算法.考虑到模拟退火算法在执行算法过程中的复杂性,利用具有全局搜索能力和收敛速度快等特点的量子行为粒子群优化算法,代替模拟退火算法对LEACH-C分簇算法中簇头的选取进行优化.通过MATLAB仿真分析,改进后的算法有效延长了传感器节点的生命,平衡了各节点的能量,提高了WSN的整体性能.

Aiming at the problem that the energy consumption and node death are too high for wireless sensor networks（ WSN）,on the basis of analyzing the LEACH-C centralized clustering algorithm,the paper proposed a WSN clustering algorithm based on quantum-behaved particle swarm optimization. The complexity of simulated annealing algorithm in the implementation of the algorithm was taken into account. A quantum behaved particle swarm optimization algorithm with global searching ability and fast convergence rate,and replaced the simulated annealing algorithm was used to optimize the selection of cluster head in LEACH-C clustering algorithm. Through MATLAB simulation analysis,the improved algorithm can effectively prolong the life of sensor nodes,balance the energy of each node,and improve the overall performance of WSN.