Improving the Performance of ALOHA with Internet of Things Using Reinforcement Learning

Intelligent medium access control (MAC) protocols have been a vital solution in enhancing the performance of a variety of wireless networks. ALOHA, as the first MAC approach, inspired the development of several MAC schemes in the network domain, with the primary advantage of simplicity. In this article, we present design, implementation, and performance evaluations of the ALOHA approach, through significant improvements in attaining high channel utilization as the most important performance metric. A critical emphasis is currently focused on removing the burden of packet collisions, while satisfying requirements of energy and delay criteria. We first implement the ALOHA protocol to practically explore its performance behaviors in comparison to analytical models. We then introduce the concept of dynamic payload instead of fixed-length packets, whereby a dynamic selection of the length of each transmitted packet is employed. Another specific contribution of this paper is the integration of the transmission policy of ALOHA with the potential of Internet of Things (IoT) opportunities. The proposed policy utilizes a state-less Q-learning strategy to achieve the maximum performance efficiency. Performance outputs prove that the proposed idea ensures a maximum throughput of approximately 58%, while ALOHA is limited to nearly 18% over a single-hop scenario.