Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

作     者：Tang, Xiao Zhang, Hongrui Zhang, Ruonan Zhou, Deyun Zhang, Yan Han, Zhu 

作者机构：The School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China The Department of Informatics University of Oslo Oslo0316 Norway The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Internet of things 

摘      要：Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address the UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines. Copyright © 2023, The Authors. All rights reserved.