Enhancing IEEE 802.15.4 Access Mechanism with Machine Learning
Arslan Musaddiq, Tariq Rahim, Dong‐Seong Kim · 2021
The Internet of Things (IoT) network consists of resource-constrained tiny devices. An efficient channel access mechanism for densely deployed IoT devices operating in a lossy environment is one of the major challenges for future IoT networks. The IoT nodes using IEEE 802.15.4 MAC protocol increase the backoff exponent (BE) during the channel sensing period. This blind increase of BE and contention window (CW) before frame transmission affects the network performance. Therefore, in this paper, we propose to use machine learning such as a reinforcement learning (RL) mechanism to handle channel access mechanisms efficiently. The proposed mechanism is evaluated using Contiki 3.0 Cooja simulations. The simulation results indicate that the proposed RL-based mechanism enhances the network performance.