Reliability-Critical Computation Offloading in UAV Swarms

Abstract

The rapid advancement of autonomous and heterogeneous unmanned aerial vehicle (UAV) swarms necessitates efficient computation offloading (CO) strategies to optimize their performance in industries, e.g., disaster management, surveillance, and environmental monitoring. UAVs face constraints such as limited energy, latency requirements, and failure risks, making robust CO approaches essential. Current CO methods often fall short due to high energy consumption, increased latency, and reliability issues in challenging conditions. This work introduces a novel collaborative CO strategy to address these deficiencies. Our approach utilizes a Bayesian network for failure mode effect analysis, considering communication bit error probabilities among multiantenna UAVs. We further employ rating-based federated deep learning to optimize decision-making, determining the best CO destination for each UAV based on factors like positions and resource capacities. Our strategy significantly outperforms existing benchmarks and state-of-the-art methods. It decreases the average probability of critical task failure by 43% and reduces energy consumption by 15% on average ensuring UAV swarms can meet strict constraints in harsh environments. These improvements demonstrate the utility of our approach in enhancing the operational reliability and efficiency of UAV swarms across diverse applications.