부산대학교 도서관

This article considers the problem of an unmanned aerial vehicle (UAV)-enabled cloud network under partial computation offloading scenario, where multiple UAV-mounted aerial base stations are employed to serve a group of remote Internet-of-Things ground-based smart devices (ISDs). The main objective of this work is to maximize energy efficiency by minimizing the number of needed drones while minimizing the cost associated with serving the ISDs under some realistic quality of service constraints. To that end, we aim to jointly optimize the 3-D UAV placements, transmit power, and cloud resources. This represents a challenging, nonconvex, and NP-hard optimization problem. In this work, we decompose the optimization problem into three separate subproblems, namely, 2-D UAV positioning, UAV altitude optimization, and UAV-cloud resource association. These subproblems are solved using a modified global $K$ -means, successive convex approximation, and successive linear programming techniques. A comprehensive simulation study and comparative evaluation against the state-of-the-art (SOTA) algorithms are conducted to demonstrate the utility of the proposed approach and its benefits in applications of interest.