UAV-Assisted Resource Optimization for Priority-Aware and Trustworthy Services

Abstract

The rapid expansion of Internet of things (IoT) devices generates increasing service demands that require reliable computation in regions with limited fixed infrastructure. Uncrewed aerial vehicle (UAV)-based mobile edge computing (MEC) systems provide a flexible solution by relocating computational resources close to IoT devices. The task execution at UAVs faces uncertainty due to energy and processing constraints, and resource allocations are further complicated by differing IoT task priorities. In this context, we propose a priority-aware and trust-driven framework for UAV-assisted resource optimization. The UAV trustworthiness is modeled through weighted metrics including computational capacity, residual energy, and task-success history. We assign a confidence parameter to each trust metric to reflect the reliability of reported values, which guides the selection of UAVs for task offloading. UAVs are positioned over the coverage area using K-means clustering to ensure that devices with diverse trust thresholds and task priorities are effectively served. The resulting optimization problem is solved using a penalty-guided optimization (PGO) algorithm and evaluated against a benchmark algorithm based on a branch-and-bound approach and a greedy algorithm. Simulation results across varying trust thresholds and task priorities show that the proposed PGO algorithm achieves performance close to the optimal benchmark while efficiently handling diverse scenarios. These findings highlight the importance of trust-aware and priority-driven UAV-MEC frameworks for supporting reliable IoT services in emerging 6G networks.