Clustering strategy for energy balance of wireless sensor networks based on improved particle swarm optimization clustering algorithm
Yanlin Li · Jisuanji yingyong yanjiu · 2011
According to energy constraints of WSN,this paper presented a clustering strategy for energy balance based on the improved particle swarm optimization clustering algorithm,in order to balance the nodes' energy consumption and maximize the network's lifetime.First,divided the WSN into some hierarchical regions according to the distance from the sensor nodes to the sink node.Adopted different probabilities in different hierarchical regions to determine the clustering number and size.Then introduced particles with the same number of the nodes to the active hierarchical region.It formed a number of initial swarm particles by K-means clustering method.The inertia weight-based particle swarm optimization algorithm was amended as well as the flying rules of the particles to parallel intelligent searching and clustering.The advantage of summarizing and learning the particle swarms speeded up the convergence and overcame the issues that the initial clustering centers were sensitive to the clustering results.It also avoided the hot issues of the WSN,balanced the network energy consumption,and maximized the lifetime of the network.Theoretical analysis and the simulation results show it's effectiveness to the energy consumption balance.