Optimizing the Operation Cost for UAV-Aided Mobile Edge Computing - Zhang, Liang; Ansari, Nirwan (IEEE transactions on vehicular technology, 2021)
Mobile edge computing ( MEC ) is leveraged to reduce the latency for the computation-intensive and latency-critical tasks offloaded from wireless devices and Internet of Things Devices ( IoTDs ). Unmanned aerial vehicles ( UAV s) have attracted much attention from both academia and industry attributed to high mobility, high flexibility, and high maneuverability of UAVs. In this article, a novel UAV-assisted MEC architecture is proposed to provision services to IoTDs, where a UAV provides both communication and computing services or works as a relay node. We then formulate the joint c omputation offloading, spectrum resource a llocation, computation resource allocation, and UAV p lacement ( Joint-CAP ) problem in the UAV-MEC network to minimize the operation cost of provisioning IoTDs. Since the Joint-CAP problem is a mixed integer non-linear programming problem and NP-hard, we decompose it into two sub-problems and solve the sub-problems sequentially. Then, we propose a (1+ε) -approximation algorithm, named AA-CAP, to solve the Joint-CAP problem, and the performance of the AA-CAP algorithm is demonstrated to be superior to the baseline algorithms via simulations.