A wireless sensor network is a collection of small sensor nodes that have limited energy and are usually not rechargeable. Because of this, the lifetime of wireless sensor networks has always been a challenging area. One of the basic problems of the network has been the ability of the nodes to effectively schedule the sleep and wake-up time to overcome this problem. The motivation behind node sleep or wake-up time scheduling is to take care of nodes in sleep mode for as long as possible (without losing data packet transfer efficiency) and thus extend their useful life. This research going to propose scheduling of nodes sleeps and wake-up time through reinforcement learning. This research is not based on the nodes' duty cycle strategy (which creates a compromise between data packet delivery and nodes energy saving delay) like other existing researches. It is based on the research of reinforcement learning which gives independence to each node to choose its own activity from the transmission of packets, tuning or sleep node in each time band which works in a decentralized way. The simulation results show the qualified performance of the proposed algorithm under different conditions. KeywordsSleep or wake-up scheduling, Wireless sensor network, Sensor node energy.

中文翻译：

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基于增强学习的无线传感器网络节点睡眠或唤醒时间调度算法

无线传感器网络是能量有限且通常不可充电的小型传感器节点的集合。因此，无线传感器网络的寿命一直是一个充满挑战的领域。网络的基本问题之一是节点有效调度睡眠和唤醒时间以克服此问题的能力。节点睡眠或唤醒时间调度的动机是尽可能长时间地在睡眠模式下照顾节点（而不丢失数据包传输效率），从而延长其使用寿命。这项研究将提出通过强化学习来安排节点睡眠和唤醒时间的计划。这项研究不是基于节点的 占空比策略（在数据包传递和节点节能延迟之间做出折衷）与其他现有研究一样。它基于增强学习的研究，该学习赋予每个节点独立性，使其可以在每个时间段中以分散方式工作，从数据包的传输，调整或休眠节点中选择自己的活动。仿真结果表明了该算法在不同条件下的合格性能。睡眠或唤醒调度，无线传感器网络，传感器节点能量。仿真结果表明了该算法在不同条件下的合格性能。睡眠或唤醒调度，无线传感器网络，传感器节点能量。仿真结果表明了该算法在不同条件下的合格性能。睡眠或唤醒调度，无线传感器网络，传感器节点能量。