Mobile beacon path planning of UWSNs based on Hopfield neural network

XUE Jianbin; CHANG Xinliang

doi:10.13873/J.1000-9787(2020)04-0035-04

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Mobile beacon path planning of UWSNs based on Hopfield neural network

更新时间：2025-09-11

- Mobile beacon path planning of UWSNs based on Hopfield neural network
- Issue 4, Pages: 35-38+42(2020)
- 作者机构：
  
  1. 兰州理工大学计算机与通信学院
  2. 东南大学移动通信国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)04-0035-04
  CLC： TN929.3;TP183;TP212.9
- Published：2020
- 稿件说明：
移动端阅览
XUE Jianbin, CHANG Xinliang. Mobile beacon path planning of UWSNs based on Hopfield neural network[J]. 2020, (4): 35-38+42.
DOI：

XUE Jianbin, CHANG Xinliang. Mobile beacon path planning of UWSNs based on Hopfield neural network[J]. 2020, (4): 35-38+42. DOI： 10.13873/J.1000-9787(2020)04-0035-04.

摘要

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。

Abstract

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.

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