Intelligent UAV Surveillance Networks with Edge-Assisted Execution of Computer Vision Tasks

Developing an edge-assisted UAV surveillance system that ensures low-latency, high-accuracy execution of computer vision tasks while optimizing energy consumption and network resources remains a complex challenge. In this paper, we address the limitations of existing research by leveraging image semantics, image ensemble processing, and mmWave UAV-edge channel statistics. We do so by focusing on joint optimization of UAV speed, camera images per second (IPS) rate, offloading policy, and transmission rates with the aim to minimize the UAV's energy consumption. Given the NP-hardness of the problem, we propose an algorithmic solution, named Intelligent UAV Network (IntUNe), which is based on an innovative constrained reinforcement learning strategy that dynamically and effectively adjusts to real-time conditions. Our results demonstrate that, in delay-constrained UAV surveillance networks, IntUNe closely matches the optimum in small-scale scenarios, and it increases inference accuracy by significantly reducing violations of the accuracy constraint by up to 96.19% compared to state-of-the-art alternatives. Also, it reduces UAV propulsion energy consumption by 34.67% and total UAV energy consumption by up to 37.3%.