Maximizing network lifetime in wireless sensor networks is one of the critical issues, particularly for transmitting multimedia data. The wireless sensor network's lifetime is directly linked to energy conservation at each sensor node in the network. Clustering is the most energy-efficient technique for saving energy in sensor networks. The appropriate method for selecting the cluster head is still lagging. The sink node divides the deployment region into the optimal number of sub-regions depending upon its placement in the sensing region. The initial cluster heads are chosen randomly in each region, and this is not an energy-efficient method. The sink node adopts particle swarm optimization technique to select the cluster head in each region efficiently. The chosen cluster head in each region advertises its role to member nodes. Then, the cluster head node is chosen forms the new cluster. PSO optimization technique with the optimization parameters of clustering coefficient, the sensor node's remaining energy, and the distance from the sink and the head of the cluster to the members is adopted to select the cluster head sensor node. The cluster head spends most of its energy aggregating and transferring the data to the sink node. For unloading the cluster head responsibilities, the assistant cluster head and super cluster head are selected for the aggregation and transfer of data, respectively. The proposed energy efficient cluster head selection algorithm has improved the network lifetime by an average of 65 percent better than the existing clustering algorithms.

最大化无线传感器网络的网络寿命是关键问题之一，尤其是对于传输多媒体数据而言。无线传感器网络的寿命直接与网络中每个传感器节点的节能相关。群集是在传感器网络中节省能源的最节能技术。选择簇头的适当方法仍然滞后。汇聚节点根据部署区域在传感区域中的位置将部署区域划分为最佳子区域数。在每个区域中随机选择初始簇头，这不是一种节能的方法。汇聚节点采用粒子群优化技术有效地选择每个区域的簇头。每个区域中所选的簇头将其角色通告给成员节点。然后，选择群集头节点形成新群集。采用具有聚类系数，传感器节点的剩余能量以及从汇聚点和簇头到成员的距离的优化参数的PSO优化技术来选择簇头传感器节点。群集头花费大部分精力来聚集数据并将数据传输到接收器节点。为了卸载集群头职责，分别选择辅助集群头和超级集群头进行数据的聚合和传输。所提出的高能效簇头选择算法比现有的簇算法平均提高了65％的网络寿命。采用聚类系数，传感器节点的剩余能量以及聚散点和簇头到成员的距离等优化参数的PSO优化技术来选择簇头传感器节点。群集头花费大部分精力来聚集数据并将数据传输到接收器节点。为了卸载集群头职责，分别选择辅助集群头和超级集群头进行数据的聚合和传输。所提出的高能效簇头选择算法比现有的簇算法平均提高了65％的网络寿命。采用聚类系数，传感器节点的剩余能量以及聚散点和簇头到成员的距离等优化参数的PSO优化技术来选择簇头传感器节点。群集头花费大部分精力来聚集数据并将数据传输到接收器节点。为了卸载集群头职责，分别选择辅助集群头和超级集群头进行数据的聚合和传输。提出的高能效簇头选择算法比现有的簇算法平均提高了65％的网络寿命。并采用从汇聚点到簇首到成员的距离来选择簇头传感器节点。群集头花费大部分精力来聚集数据并将数据传输到接收器节点。为了卸载集群头职责，分别选择辅助集群头和超级集群头进行数据的聚合和传输。提出的高能效簇头选择算法比现有的簇算法平均提高了65％的网络寿命。并采用从汇聚点到簇首到成员的距离来选择簇头传感器节点。群集头花费大部分精力来聚集数据并将数据传输到接收器节点。为了卸载集群头职责，分别选择辅助集群头和超级集群头进行数据的聚合和传输。提出的高能效簇头选择算法比现有的簇算法平均提高了65％的网络寿命。