Cross-Layer Optimization for Covert Offloading in Cooperative Computing UAV Ad Hoc Networks

Abstract

Cooperative computing in unmanned aerial vehicle (UAV) ad hoc networks can improve the overall computing service capability. Unfortunately, open air-to-air wireless links make task offloading vulnerable to eavesdropping, making covert and low-latency offloading a dual challenge. Task offloading performance is subject to multi-layer factors, and isolated singlelayer optimization is inherently limited. This paper investigates the cross-layer optimization of covert task offloading in UAV ad hoc networks. An algorithm for jointly optimizing the offloading decision, offloading size, route selection, bandwidth allocation, and beamforming vectors is proposed to minimize task completion delay. This algorithm is based on a double-chromosome genetic algorithm (DCGA) with an alternating optimization (AO) method during fitness evaluation. Simulation results demonstrate that the proposed algorithm effectively reduces task completion delay while guaranteeing communication covertness, achieving a maximum reduction of 19.87% compared to the baselines.