基于改进蚁群的BP神经网络WSN数据融合算法

为了保证无线传感器网络（WSN）在深井中能有效地工作，提出了一种改进蚁群的反向传播（BP）神经网络WSN数据融合算法（IFA-IACOBP）.通过规划蚂蚁运动方向和引入节点剩余能量对蚁群算法启发因子进行改进，优化蚂蚁下一跳节点选择概率，利用改进后的蚁群算法对BP神经网络进行优化，引入井下WSN数据融合，数据经两级融合处理后，能去除大部分冗余信息.仿真实验结果表明，IFA-IACOBP算法能有效减少网络数据通信量，提高数据实时性，降低网络能耗，延长网络寿命.

Information Fusion Algorithm Based on Improved Ant Colony Optimization BP Neural Network in WSN

In order to ensure the effective working of wireless sensor network (WSN) in deep mine, an information fusion algorithm based on improved ant colony optimization back-propagation (BP) neural network in WSN (IFA-IACOBP) is proposed. The heuristic factor of ant colony optimization (ACO) is improved by planning the direction of ants' motion and introducing the residual energy of nodes to improve the selection probability of the ant next-hop node. The improved ant colony algorithm is used to optimize the BP neural network, which is applied to WSN information fusion in mine. These data are processed by two-level fusion, which can remove most redundant information. Simulation results show that the IFA-IACOBP algorithm can effectively decrease the network data communication, improve data real-time performance, reduce network energy consumption and prolong network lifetime.