LAUSR

In Internet of Things (IoT) applications, sometimes the quality of service (QoS) of throughput for transmitting video or the QoS of bounded delay for control of a sensor node is required. A traditional contention-based medium access control (MAC) protocol cannot meet the adaptive traffic demands of these networks and confers delay-related constraints. Q-learning (QL) is one of the reinforcement learning (RL) mechanisms and can potentially be the future machine learning scheme for spectrum MAC protocols in IoT networks. In this study, a QL-based MAC protocol is proposed to facilitate adaptive adjustment of the length of the contention period in response to the ongoing traffic rate in IoT networks. The novelty of QL-based MAC lies in its use of RL to dynamically adjust the length of the contention period according to the traffic rate. The QL-based MAC will solve the models without additional input information to adapt to environmental variations during training. We confirm that the proposed QL-based MAC protocol with node contention is robust. In addition, we showed that our proposed QL-based MAC protocol has higher system throughput, lower end-to-end delay, and lower energy consumption in MAC contention than those of contention-based MAC protocols.