Dynamic Adaptive Task Offloading for UAV-based Road Traffic Monitoring

Unmanned Aerial Vehicles (UAVs) are increasingly used for road traffic monitoring due to their mobility and wide-area coverage. However, their limited onboard resources make real-time video analysis challenging under dynamic traffic conditions. To overcome this, computational task offloading to nearby fog nodes is often employed. The main challenge lies in deciding when to process locally or offload, as both traffic and computational load vary continuously. Existing heuristic-based approaches are lightweight but rely on fixed thresholds, leading to unstable switching and degraded performance under fluctuating conditions. Meanwhile, Deep Reinforcement Learning (DRL)–based methods can adaptively optimize offloading but require extensive training and high computational costs, limiting their practicality on UAVs. To address this challenge, we propose Dynamic Vehicle Density-aware Offloading (DVDOffload), an adaptive task offloading technique designed to maximize performance and resource efficiency by adapting the offloading decision to road traffic conditions. The proposed method uses vehicle density as the primary workload indicator and dynamically adjusts offloading thresholds using an Exponential Moving Average (EMA) to ensure adaptive and stable decisions. Experimental results in multiple realistic traffic scenarios show that DVDOffload achieves higher accuracy, faster processing, and lower resource consumption compared to several baseline heuristic and DRL-based approaches in the evaluated UAV–fog traffic monitoring system.