WSN中基于梯度和粒子群优化算法的分级簇算法

Gradient and Particle Swarm Optimization Based Hierarchical Cluster Algorithm in WSN

为均衡网络中节点的能量消耗,提出一种分级簇算法——GPHCA.该算法采用双簇头模式,利用粒子群优化算法搜寻能量大且到簇成员平均距离小的两个节点作为主簇头和副簇头,将簇头负担均衡到了两个节点上;在网关的选择上,同时考虑能量和转发路径的总距离,使最终选择的网关在能量和时延上得到均衡.仿真结果表明,GPHCA算法能有效延长网络的生命周期.

A hierarchical clustering algorithm was proposed to balance the nodes＇ energy consumption in the network. A mode of double cluster heads was adopted to select two cluster heads with sufficient remaining energy and small average distances to the cluster members using particle swarm optimization algorithm. The burden of one cluster head was shared by these two nodes. As for the gateways,the residual energy and the total distance of forwarding path was considered to make sure that the final chosen gateways get well balance between energy and delay. The simulation results show that the GPHCA algorithm can effectively prolong the network lifetime.