UGV Charging Stations for UAV-Assisted AoI-Aware Data Collection

Abstract

Meeting the stringent requirements of real-time Internet of Things (IoT) is a bit challenging than expected. Indeed, IoT devices generate massive amounts of data through their sensing features and face some constraints in timely transmitting sensed data to collectors. To overcome this problem, Unmanned Aerial Vehicles (UAVs) are deployed to act as data collectors for IoT devices as they significantly improve the freshness of collected data in real-time applications. Nevertheless, IoT devices and UAVs have limited energy capacity. In this paper, we tackle the energy concern of IoT devices by utilizing UAVs as data collectors and energy transmitters and by promptly charging IoT devices whenever necessary. As for the energy concern of UAVs, we deploy a set of Unmanned Ground Vehicles (UGVs) to energy supply UAVs, allowing them to complete their tasks successfully. Our objective is to employ a multi-agent reinforcement learning method for optimally controlling the trajectories of both UGVs and UAVs so that it jointly decreases their energy consumption, reduces the Age of Information (AoI) of IoT devices, and timely charges UAVs and avoids their failures. We conducted a series of tests using a simulation tool to validate the effectiveness of the approach.