侯睿 何柳婷.基于增强学习的非均匀分簇水声传感器网络能耗研究[J].中南民族大学学报自然科学版,2020,39(2):205-209
基于增强学习的非均匀分簇水声传感器网络能耗研究
Study on energy-consumption of unequal clustering in Underwater Acoustic Sensor Networks based on reinforcement learning
  
DOI:10.12130/znmdzk.20200216
中文关键词: 水声传感器网络  聚类算法  增强学习  路径优化
英文关键词: Underwater Acoustic Sensor Network  clustering algorithm  reinforcement learning  path optimization
基金项目:国家自然科学基金资助项目(61972424;60841001); 中央高校基本科研业务费专项资助项目(CZT19011); 中南民族大学研究生学术创新基金资助项目(2019sycxjj116)
作者单位
侯睿 何柳婷 中南民族大学 计算机科学学院武汉 430074 
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中文摘要:
      近年来,水声传感器网络越来越成为研究的热点,但由于水下环境复杂多变,导致网络中能量消耗不均问题.针对此问题提出一种基于增强学习的非均匀分簇的水声传感器网络路径优化算法. 该算法首先根据水声传感器网络中节点的深度和剩余能量把传感器节点分成大小不同的簇;其次根据节点的综合属性值选出最佳簇头;最后在数据传输阶段利用增强学习和ε-greedy策略对簇间的传输路径进行决策和学习,寻找最优路由. 实验结果表明:本文方法可以有效均衡能耗,并延长网路寿命.
英文摘要:
      In recent years, Underwater Acoustic Sensor Network has become a hot research topic, but due to the complex and changeable underwater environment, the energy consumption in the network is uneven. In order to solve this problem, a path optimization algorithm of Underwater Acoustic Sensor Network based on reinforcement learning is proposed. The algorithm firstly divides the sensor nodes into clusters of different sizes according to the depth and residual energy of the nodes in the network. Secondly, the best cluster head is selected according to the comprehensive attribute value of nodes. At last, the transmission paths between the cluster-head and the cluster-head are determined and learned by using reinforcement learning and ε-greedy strategy in the data transmission stage, and the global optimal path is selected. Experimental results show that the proposed method can effectively balance energy consumption and prolong network life.
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