An efficient intelligent data fusion algorithm for wireless sensor network
Haitao Wang, Lihua Song, Jue Liu, Tingting Xiang
Abstract
Wireless sensor network (WSN) are usually restricted by assembled batteries which are difficult to recharge, therefore saving network energy is crucial for WSN. For increasing the survival time of the network, an efficient intelligent data fusion algorithm named GAPSOBP is put forward which integrating BP neural network, genetic algorithm and particle swarm optimization algorithm reasonably. In GAPSOBP, wireless sensors are analogy to neurons in the neural network. Data collected by sensors is extracted by BP neural network, and then combined with clustering routing to fuse extra data, thus reducing data volume sent to base station or sink node. Simulation results show that GAPSOBP is superior than LEACH and PSOBP algorithms in terms of energy consumption and network lifetime.