Distributed reinforcement learning scheme for environmentally adaptive IoT network selection

Proliferation of smart internet of things (IOTs) devices has boosted the improvement of multiple networking functions which have a different capability in terms of capacity and access delay. Herein, the networking function of IoT devices should be properly selected to fully utilise the capabilities of the different types of networking technologies. In this Letter, a reinforcement learning-based self-organising scheme is proposed for the IOTs. A node selects an adequate IoT network function and adapts its topology by learning channel circumstance. To verify the performance of the proposed learning-based scheme, simulations reflect a multiple number of heterogeneous IoT networks and show that the average latency of IoT devices can be efficiently reduced compared to the conventional benchmark networks (Wi-Fi and narrow band IoT).