Delay-Aware IoT Task Scheduling in Space-Air-Ground Integrated Network

Due to the versatile networking capability, space- air-ground integrated network (SAGIN) becomes a prominent future architecture to support the ever- increasing Internet of Things (IoT) applications. In this paper, we investigate the IoT task offloading under an SAGIN scenario where multiple IoT devices generate computing tasks to be processed. We adopt an unmanned aerial vehicle (UAV) to fly along a given trajectory to collect the tasks of IoT devices within the coverage area, and then makes the online offloading decision, i.e., processing locally, or offloading to the nearby base station or the far-away satellite. However, due to the constrained energy resources committed by UAV and the uncertainty of the system dynamics, designing an efficient computation task offloading algorithm is challenging. This dynamic scheduling problem is formulated as a constrained Markov decision process (CMDP), considering the stochastic channel conditions, UAV coverage, energy consumption, and task queue backlogs. By exploiting the stationary stochastic feature of the CMDP, the problem can be solved by the linear programming to find a stochastic policy. Simulation results demonstrate that the proposed computation offloading scheme can significantly reduce IoT task processing delay as compared to other benchmarks.