Deep Reinforcement Learning-Based Multi-Hop State-Aware Routing Strategy for Wireless Sensor Networks
Deep Reinforcement Learning-Based Multi-Hop State-Aware Routing Strategy for Wireless Sensor Networks
复制标题
基于深度强化学习的无线传感器网络多跳状态感知路由策略
DOI：
10.3390/app11104436
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发表时间：
2021-05
影响因子：
2.7
通讯作者：
Wang Cheng
中科院分区：
文献类型：
--
作者：
Zhang Aiqi;Sun Meiyi;Wang Jiaqi;Li Zhiyi;Cheng Yanbo;Wang Cheng
随着无线传感器网络技术的发展，路由策略在物联网中具有重要意义。高效的路由策略是保证无线传感器网络正确、快速传输的基础技术之一。本文研究如何将联合收割机深度学习技术与路由技术相结合，提出一种高效的路由策略，以科普网络拓扑变化。首先，我们使用递归神经网络结合深度确定性策略梯度方法来预测网络流量分布。第二，多跳节点状态被认为是双深Q网络的输入。因此，节点可以根据网络的当前状态做出路由决策。提出了一种基于业务流预测的多跳状态感知路由策略。仿真结果表明，MHSA-TFF可以改善传输延迟，平均路由长度和能量效率。
With the development of wireless sensor network technology, the routing strategy has important significance in the Internet of Things. An efficient routing strategy is one of the fundamental technologies to ensure the correct and fast transmission of wireless sensor networks. In this paper, we study how to combine deep learning technology with routing technology to propose an efficient routing strategy to cope with network topology changes. First, we use the recurrent neural network combined with the deep deterministic policy gradient method to predict the network traffic distribution. Second, the multi-hop node state is considered as the input of a double deep Q network. Therefore, the nodes can make routing decisions according to the current state of the network. Multi-hop state-aware routing strategy based on traffic flow forecasting (MHSA-TFF) is proposed. Simulation results show that the MHSA-TFF can improve transmission delay, average routing length, and energy efficiency.
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DOI：
--
发表时间：
2014-06
期刊：
--
影响因子：
--
作者：
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通讯作者：
David Silver;Guy Lever;N. Heess;T. Degris;Daan Wierstra;Martin A. Riedmiller
影响因子：
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DOI：
10.1109/cita.2011.5999535
发表时间：
2011-07
期刊：
2011 7th International Conference on Information Technology in Asia
影响因子：
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作者：
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通讯作者：
Zeyad Ghaleb Al-Mekhlafi;R. Hassan
影响因子：
11.2
作者：
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通讯作者：
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影响因子：
3.7
作者：
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通讯作者：
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