粒子寻优和最小生成树聚类下的WSN能量优化

发布时间：2018-05-08 03:11

本文选题：无线传感器网络 + 能量优化　；参考：《计算机工程与应用》2017年15期

【摘要】：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%。
[Abstract]:In order to balance the energy load of cluster wireless sensor network nodes and improve the energy utilization efficiency of the network, an energy optimization algorithm named particle optimization and minimum spanning tree clustering rule is proposed. In order to balance the energy load of cluster heads, the algorithm uses particle optimization method to solve the fitness value, and obtains the best cluster head by comparing the fitness values, so as to reduce the transmission energy consumption of the nodes in the cluster. At the same time, a cluster head selection method based on the minimum spanning tree clustering rules is proposed. This method selects the optimal cluster head number based on residual energy and distance factors, and minimizes the total energy consumption of the network while ensuring the quality of data transmission. The simulation results show that, Compared with a new differential evolution clustering algorithm for wireless sensor networks and a multi-layer energy-saving and range-aware clustering algorithm for wireless sensor networks, the average energy efficiency of the nodes increased by 16.7% and 6.4%, respectively, and the network nodes survived. The number increased by 24.1% and 13.7%, respectively.
【作者单位】：常州工学院网络与教育技术中心;常州工学院计算机信息工程学院;
【分类号】：TN929.5;TP212.9

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