LABOR: Latency and Buffer Occupancy Optimized Multimodal Mission Offloading Strategy in Resource-Constrained UAV Swarm Networks

The rapid development of UnmannedAerial Vehicle (UAV) swarm technologies has significantly expanded their applications in fields like road rescue and agricultural monitoring, where multi-modal data streams from sensors such as infrared, radar, and visible light are critical. However, processing such data in real-time is computationally intensive, particularly with UAVs' limited onboard resources. Missions involving multi-modal data are typically divided into sub-tasks, which can be processed either locally or offloaded to edge servers. While offloading sub-tasks helps alleviate onboard computational load, it introduces transmission delays and potential server overloads. Moreover, the inter-dependency of sub-tasks creates a synchronization bottleneck, where the slowest sub-task determines the mission completion time. Early-completed sub-tasks also occupy limited UAV onboard buffer resources. To address these challenges, we propose LABOR, a Proximal Policy Optimization (PPO)-based mission offloading strategy. LABOR dynamically determines whether each sub-task should be processed locally or offloaded, optimizing the balance between latency, resource availability, and computational requirements. Through extensive simulations in various scenarios, LABOR is shown to significantly outperform other offloading strategies, achieving up to 64.0% and 80.1% reductions in latency and buffer occupancy, respectively. This work provides a promising solution for efficient mission offloading in resource-constrained UAV swarm networks, enhancing both real-time performance and resource utilization.