Performance Evaluation of Machine Learning Based Channel Selection Algorithm Implemented on IoT Sensor Devices in Coexisting IoT Networks
The number of IoT devices may dramatically increase in the near future. Numerous IoT devices may generate enormous traffic, which causes network congestions and packet losses. To manage network congestions, Ma et al. have proposed a channel selection algorithm based machine learning for IoT devices. They modeled channel selection as Multi-Armed Bandit problem and have designed a algorithm based on Tug-of-War dynamics to solve this problem. Furthermore, they confirmed dynamic channel selection in a local area where devices are crowded. In this paper, we conduct evaluation experimentation in real environment where devices are coexisting with other IoT systems, Sigfox and LoRaWAN. Our experimental results using our implemented systems show that each IoT node selects appropriate channel by the proposed algorithm based on reinforcement learning and the packet delivery rate (frame success rates) and fairness among the sensor nodes can be improved by the proposed scheme.
Reinforcement Learning, Multi Armed Bandit, IoT, Distributed Channel Selection