PREUS: Proactive and Robust Edge-UAV Systems for Autonomous Monitoring in Dynamic Environments

Edge computing and AI can potentially empower Unmanned Aerial Vehicle (UAV) systems with automated decision-making and resource support for monitoring in future science tasks such as emergency response, search and rescue, inspections, and wildfires. However, it is challenging to achieve autonomous and robust monitoring in such systems, given the dynamic environmental situations, the limited capabilities, and the unbalanced load of the UAVs. For instance, the monitoring activity levels at different locations might vary, which leads to an unbalanced monitoring load for the corresponding UAVs. Moreover, the UAVs require regular recharging/maintenance and can have malfunctions that will disrupt the monitoring task. In this article, we develop a novel proactive and robust Edge-UAV framework named PREUS to enable autonomous and efficient monitoring of dynamic environments when faced with dynamic environment situations and various UAV workload stresses that can jeopardize the monitoring performance. PREUS features a unique design to handle the varying UAV workload stress of the monitored area. It incorporates novel spatial, temporal, and proactive exploration vs. exploitation planning to balance the UAVs’ workloads in various locations with fluctuating activities. In addition, PREUS includes novel Deep Reinforcement Learning (DRL) design specialized to maximize coverage in the complex environments and provides faster and stabler decision-making capabilities than the existing methods. The positive impact brought by PREUS is demonstrated in terms of the achieved monitoring performance, including coverage and balanced UAV load.