Joint Optimization of UAV Trajectories and Computational Offloading for Space-Air-GroundIntegrated Networks

As an emerging network architecture,space-air-ground integrated network has attracted significant attention from researchers in recent years,and it can greatly improve the overall quality of service.Addressing the challenges of insufficient network coverage and the lack of basic infrastructure in remote areas,a space-air-ground integrated network framework is proposed in which unmanned aerial vehicles(UAVs) and satellites collaboratively collect tasks.In this framework,UAVs and satellites provide edge computing services for ground sensors,while cloud servers deliver cloud services.Given that UAV coverage,task completion rate,and task latency are critical factors influencing system performance,this study jointly optimizes UAV trajectory and computation offloading to maximize UAV coverage and task completion rate while minimizing latency.The proposed joint optimization problem is formulated as a mixed-integer nonlinear programming problem,therefore,a dual-layer optimization algorithm based on the Beluga Whale Optimization and Sand Cat Swarm Optimization is developed,with the two layers separately optimizing UAV trajectory and computation offloading.Experimental results show that the proposed algorithm significantly improves the coverage rate of multiple UAVs,effectively enhances the task completion rate,and reduces average task latency in computation offloading.