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Efficient Routing Protocol for Wireless Sensor Network based on Reinforcement Learning

机译:基于强化学习的无线传感器网络高效路由协议

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Wireless sensor nodes are battery-powered devices which makes the design of energy-efficient Wireless Sensor Networks (WSNs) a very challenging issue. In this paper, we propose a new routing protocol for WSN based on distributed Reinforcement Learning (RL). The proposed approach optimises WSN lifetime and energy consumption. This routing protocol learns, over time, the optimal path to the sink node(s). With a dynamic path selection, our algorithm ensures higher energy efficiency, postpones nodes death and isolation. We consider while routing messages the distance between nodes, available energy and hop count to the sink node. The effectiveness of the proposed protocol is demonstrated through simulations and comparisons with some existing algorithms over different lifetime definitions.
机译:无线传感器节点是电池供电的设备,这使节能无线传感器网络(WSN)的设计成为一个非常具有挑战性的问题。在本文中,我们提出了一种基于分布式强化学习(RL)的WSN新路由协议。所提出的方法优化了WSN的寿命和能耗。随着时间的流逝,该路由协议会​​学习到宿节点的最佳路径。通过动态路径选择,我们的算法可确保更高的能源效率,推迟节点的死亡和隔离。我们在路由消息时考虑节点之间的距离,到接收节点的可用能量和跳数。通过仿真和与一些现有算法在不同生命周期定义上的比较和比较,证明了所提出协议的有效性。

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