Joint Task Offloading and Energy Harvesting in Space-Air-Ground-Integrated MEC Networks

The rapid development of space-air-ground integrated technology has laid a foundation for achieving wide area wireless communication coverage, but its applicability is limited due to the limited computational capacity and energy storage of the terminal devices. This article thus proposes a space-air-ground integrated mobile-edge computing (MEC) task offloading and computing resource allocation (SIMOC) algorithm. First, a space-air-ground integrated MEC system is designed in which the terminal devices in the system can offload tasks to air- and space-based servers while performing energy harvesting. Second, an optimization objective of maximizing the task execution benefit minus the sum cost of task offloading and execution is established, for which the problem is transformed into a time-slot-based minimization problem of queue-stability minus revenue using Lyapunov optimization. Finally, the energy harvesting and task offloading subproblems of the queue-stability-minus-revenue problem are solved, respectively, to maximize the total revenue while ensuring device stability. Simulation results show that the SIMOC algorithm can reduce the all-task completion time by up to 98.16% compared with only local task execution algorithm in the absence of newly added tasks and shows good performance in handling newly added tasks. Meanwhile, the SIMOC algorithm has better performance compared to the particle swarm optimization algorithm.