粒子寻优和最小生成树聚类下的WSN能量优化

Approach to WSN energy optimization based on particle optimization and MST clustering

为了均衡分簇无线传感器网络节点能量负载,提高网络的能量利用效率,提出了一种粒子寻优和最小生成树聚类规则的能量优化算法(OMST)。该算法为了使得簇头的能量负载能够得到均衡,采用基于粒子寻优的方法来进行适应值求解,通过适应值对比来求得最佳簇头,以减少簇内节点的传输能耗。同时,提出一种最小生成树聚类规则的簇首数量选择方法,该方法基于剩余能量和距离因素来选择最优的簇首数量,在保证数据传输质量的同时最小化网络总能量的消耗量。仿真结果表明,相比一种新型差分进化的无线传感器网络聚类算法和多层节能及距离感知的无线传感器网络聚类算法,OMST算法的节点平均能量效率分别提高了16.7%和6.4%,网络节点存活数量分别提高了24.1%和13.7%。

To balance clustering wireless sensor network nodes energy load,and improve the energy efficiency of thenetwork,an energy optimization algorithm based on particle optimization and the minimum spanning tree clustering rulesis proposed.In order to achieve the balanced energy cluster head load,particle optimization-based approach is used in thisalgorithm to solve the adaptation value,then the best cluster head is obtained based on the compared adaptation value andthe transmission of energy is reduced by adapting fitness.Then,a minimum spanning tree clustering rule number clusterhead selection method is proposed,based on the residual energy and distance factors,it selects the optimal number ofclusters of the first,to ensure the quality of the data transmission network while minimizing the total energy consumption.Simulation results show that,the wireless sensor network clustering algorithm for wireless sensor networks clusteringalgorithm compared to a novel differential evolution and multi-saving and distance perception,the node average energyefficiency OMST algorithms are increasing by16.7%,and6.4%,the net number of surviving nodes are increasing by24.1%and13.7%.