Optimal wireless sensor network using cloud adaptive particle-swarm-optimization algorithm
云粒子群优化算法在无线传感器网络中的应用

The poor computation ability and limited storage of power of the nodes of wireless sensor network(WSN) have seriously restricted the development of WSN. Based on the cloud adaptive particle swarm optimization(CAPSO) algorithm, an optimal approach for WSN is proposed, which includes the network clustering, network modeling, and the iterative optimization with CAPSO algorithm, etc. The convergence for CAPSO algorithm can be accelerated by using cloud model to optimally select the inertia weight. The test results of typical function show that the CPSO algorithm is superior to the conventional PSO and Genetic Algorithms(GA). In addition, the power consumption of the whole network can be reduced and the lifespan of nodes can be prolonged by using the binary power control algorithm in network modeling. The simulation experiment and comparison analysis show that the proposed approach possesses advantages of high speed in optimization, strong survival ability of nodes, and effective reduction of power consumption in control.