Opportunistic RL-based WiFi Access for Aerial Sensor Nodes in Smart City Applications

Abstract

Unmanned air vehicles are becoming widespread, driven by improved wireless technologies. However, the WiFi technology used for communication has a highly crowded and unevenly distributed channel occupancy in its spectrum. To overcome this, WiFi resources need to be utilized efficiently. Therefore, this paper proposes the Opportunistic Reinforcement Learning-based WiFi Access scheme, which exploits intermittent channel occupancy to solve the NP-hard channel assignment problem. As a result, the proposed model has improved the accurate channel selection on the UAVs by 9%, performing 91% accuracy, compared to the trivial channel scoring-based selection algorithms.