Unmanned aerial vehicle (UAV) enabled mobile-edge computing (MEC) has been recognized as a promising approach for providing enhanced coverage and computation capability to Internet of Things (IoT), especially in the scenario with limited or without infrastructure. In this paper, we consider the UAV assisted partial computation offloading mode MEC system, where ground sensor users are served by a moving UAV equipped with computing server. Computation bits (CB) and computation efficiency (CE) are two vital metrics describe the computation performance of system. To reveal the CB-CE tradeoff, an optimization problem is formulated to maximize the weighted sum of the above two metrics, by optimizing the UAV trajectory jointly with communication resource, as well as the computation resource. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory optimization and resource allocation optimization. We propose an iterative algorithm to solve the two sub-problems by Dinkelbach’s method, Lagrange duality and successive convex approximation technique. Extensive simulation results demonstrate that our proposed resource allocation optimization scheme can achieve better computational performance than the other schemes. Moreover, the proposed alternative algorithm can converge with a few iterations.
Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting