Towards a Mobility-cum-Battery Aware Dynamic UAV Deployment for Uninterrupted Connectivity

We consider use cases that necessitate the reliable transmission of critical communication to aid mobile users, such as providing emergency network services to survivors in disaster areas, supporting victims in a traffic accident, or monitoring public events occurring in remote places. In such cases, it has become a practice for network service providers to temporarily deploy a swarm of UAVs to complement the traditional unreliable mode of communication in such regions that may become intermittently unavailable. Given that the battery capacity limits the flight time of UAVs, it is important to design an optimal deployment plan such that ground devices in the area of interest (for example, users in disaster areas) are always connected to one or more UAVs, providing them with network connectivity. We consider that Battery Swapping Stations (BSS) are deployed in the area of interest (AoI) such that the UAVs can promptly replenish their battery to maintain uninterrupted services. Further, the relative position of the UAVs acting as network access points must be adjusted concerning the users’ current locations of the said network service to maintain uninterrupted connectivity to the User Equipments (UEs) in the given AoI. To that end, we propose a Deep Reinforcement Learning (DRL) algorithm to optimally adjust the positions of the UAVs concerning the UEs as well as the BSSs to maintain network connection despite dynamic changes in the position of UEs. Additionally, our algorithm also triggers the UAVs to swap their batteries whenever a certain optimal threshold for the available battery level has been reached. Since the UAVs are maneuvered from one optimal position to the next one based on the mobility of the UEs, there is an inherent dynamic sequential decision-making process involved in the given problem, making it a good fit for DRL. With extensive simulation experiments, we demonstrate that our approach outperforms the state-of-the-art in this field.