Energy-Efficient Trajectory Design and Computing Offloading in UAV-Aided IoT Networks

In this paper, we propose to minimize the total energy expenditure in unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) internet of things (loT) networks by employing the non-orthogonal multiple access (NOMA). Considering the obstacles of an urban city environment, a composite optimization model incorporating both the flight trajectory and computational offloading is initially formulated. To address this non-convex problem, the original problem is converted into two independent subproblems, and then an energy-efficient trajectory design and computing offloading algorithm is developed by employing the successive convex approximation (SCA) and quadratic approximation approaches. Moreover, comprehensive simulation results verify that the proposed algorithm achieves a decrease in weighted energy expenditure by 14.8% in comparison to the algorithms employing a fixed trajectory, fixed transmit power, and the orthogonal multiple access (OMA).