Vehicle Density-Aware Adaptive Offloading for UAV-Based Road Traffic Monitoring

Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology for real-time road traffic monitoring, offering enhanced efficiency and responsiveness to modern traffic management systems. However, the resource limitations of UAVs and the dynamic nature of traffic densities present significant challenges for continuous operation. To address these constraints, this study proposes a vehicle-density-aware adaptive offloading mechanism that dynamically alternates between local processing and task offloading to fog nodes, based on real-time traffic conditions. The mechanism operates in three distinct modes: Low-CPU Mode for low vehicle density, Full Offloading Mode for moderate density, and Local Processing Mode for high-density scenarios. Preliminary results reveal that the proposed VD-aware adaptive offloading mechanism effectively balances performance, resource efficiency, and communication costs. It maintains competitive accuracy, optimizes throughput, and dynamically manages CPU utilization and communication overhead. These findings highlight the adaptability and efficiency of the proposed mechanism, making it an ideal solution for UAV-based road traffic monitoring in dynamic and resource-constrained environments.