基于Hopfield神经网络的UWSNs移动信标路径规划

Mobile beacon path planning of UWSNs based on Hopfield neural network

结合水声信道传播损耗模型,将所有节点中位置重要、邻居节点多的节点选为虚拟信标节点,使信标节点到各个虚拟信标节点位置向周围广播,完全覆盖整个网络。提出筛选策略,最小化虚拟信标节点的数量。把所有虚拟信标节点的路径规划看作旅行商问题(TSP),通过Hopfield神经网络将虚拟节点连接起来,使路径总长度最小。为解决Hopfield神经网络的随机性,使其适用于数量较大的TSP,在结束条件部分引入交叉算子Position-based Crossover的思想,提出交叉策略,减少规划的路径总长度。仿真实验证明:该策略能解决水下无线传感器网络(UWSNs)移动信标节点的路径规划问题,且能有效减少路径总长度。

Combined with the underwater acoustic channel propagation loss model to select the nodes which have important locations and many neighbor nodes in all nodes as virtual beacon nodes,so that the beacon nodes can broadcast to the surrounding locations of each virtual beacon node,which can completely cover the entire network.In the process,a screening strategy is proposed to minimize the number of virtual beacon nodes. Consider the path planning of all virtual beacon nodes as the traveling salesman problem( TSP),and connect these virtual nodes through the Hopfield neural network to minimize the total path length. In order to solve the randomness of Hopfield neural network,and make it be applied to a large number of TSPs. In the end condition part,the idea of crossover operator Position-based Crossover is introduced,and a crossover strategy is proposed to reduce the total path length of the planning. Simulation experiments verify that this strategy can solve the path planning problem of UWSNs mobile beacon nodes,and can effectively reduce the total path length.