System Cost Optimization-Based Task Offloading for UAV-Assisted LEO Satellite Networks

The integration of unmanned aerial vehicles (UAVs) and low Earth orbit (LEO) satellites is expected to provides flexible, scalable and efficient computing support for Internet of things (IoT) applications. In this paper, we study the task execution problem in a UAV-assisted satellite offloading network, where UAVs fly over the network coverage region to gather tasks from IoT devices. The collected tasks can be executed at the UAVs or offloaded to satellites for processing. Jointly considering the energy consumption and task dropping cost, we define system cost function as the weighted sum of the two metrics, and formulate a constrained system cost minimization problem, where the power allocation strategy, task offloading and scheduling, and UAV flight trajectory planning is jointly optimized. As the formulated problem is a mixed integer nonconvex optimization problem, which cannot be solved conveniently, we decompose it into subproblems, and propose an alternate iteration-based algorithm to solve the subproblems. In particular, to tackle the IoT device task transmission subproblem, we relax binary variables into continuous ones and transform the original problem into a standard linear problem which can be addressed using existing optimization tools. To resolve UAV trajectory design subproblem, we employ successive convex optimization and first order Taylor expansion method to convert the problem into a convex problem which can then be solved conveniently. For the power allocation subproblem, an iterative method and Lagrange dual approach are applied to obtain the optimal result. To obtain task offloading and computing scheduling strategy, a heuristic approach is proposed. The simulation results reveal that our proposed method achieves superior performance compared to the existing algorithms.