Wireless sensor network (WSN) is effective for monitoring the target environment, which consists of a large number of sensor nodes of limited energy. An efficient medium access control (MAC) protocol is thus imperative to maximize the energy efficiency and performance of WSN. The most existing MAC protocols are based on the scheduling of sleep and active period of the nodes, and do not consider the relationship between the load condition and performance. In this paper a novel scheme is proposed to properly determine the duty cycle of the WSN nodes according to the load, which employs the Q-learning technique and function approximation with linear regression. This allows low-latency energy-efficient scheduling for a wide range of traffic conditions, and effectively overcomes the limitation of Q-learning with the problem of continuous state-action space. NS3 simulation reveals that the proposed scheme significantly improves the throughput, latency, and energy efficiency compared to the existing fully active scheme and S-MAC.

无线传感器网络（WSN）可有效监视目标环境，该目标环境由大量能量有限的传感器节点组成。因此，必须有一个有效的媒体访问控制（MAC）协议来最大化WSN的能量效率和性能。现有的大多数MAC协议都是基于节点的睡眠和活动时间的调度，并且没有考虑负载条件和性能之间的关系。本文提出了一种新的方案来根据负载适当地确定WSN节点的占空比，该方案采用Q学习技术和线性回归函数逼近。这样就可以针对各种交通状况进行低延迟的节能调度，通过连续的状态作用空间有效地克服了Q学习的局限性。NS3仿真显示，与现有的完全活动方案和S-MAC相比，该方案显着提高了吞吐量，延迟和能效。