Rating-aware graded security offloading and cost optimization for UAV-assisted MEC networks

UAV-assisted mobile edge computing (MEC) provides a flexible way to process computation-intensive tasks for ground users, but the open air-to-ground transmission links expose offloaded data to eavesdropping risks. Existing secure offloading schemes often apply fixed protection policies to all tasks, which may introduce unnecessary overhead for low-sensitivity data while providing insufficient adaptation for highly sensitive data. To address this issue, this paper proposes a rating-aware graded security offloading framework for UAV-assisted MEC networks. Each task is assigned a normalized sensitivity rating, and the rating is mapped to a security-strength coefficient that affects encryption/decryption delay, security-related energy consumption, and offloading cost. Based on this model, the offloading decision among local computing, UAV-MEC computing, and cloud computing is jointly optimized with transmission power and computing resource allocation. The resulting mixed-integer non-convex problem is solved by a tailored alternating optimization algorithm. Simulation results under the same baseline parameter settings show that the proposed framework maintains a higher offloading ratio and lower system cost than the fixed-security and no-rating baselines in the considered hover-based UAV-MEC scenario.