Integrated trajectory optimization for UAV-enabled wireless powered MEC system with joint energy consumption and AoI minimization

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks Pub Date : 2024-10-03 DOI:10.1016/j.comnet.2024.110842

Yuchen Li , Hongwei Ding , Zhijun Yang , Bo Li , Zhuguan Liang

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引用次数: 0

Abstract

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless powered mobile edge computing (MEC) system, where a UAV, equipped with RF Chains and MEC servers, can sustainably provide wireless energy for charging Internet of Things (IoT) devices and executing computing tasks from these devices while hovering at designated hover points. Our goal is to minimize the weighted sum of energy consumption and Age of information (AoI) in this system, which depended on the UAV’s hovering time at designated points and its flying time. To achieve this, we jointly optimize the deployment of hover points and the visiting order of these points by the UAV. It is NP hard and mixed-integer non-convex which is difficult to solve by traditional methods. To tackle this problem, we present a trajectory optimization algorithm for joint energy consumption and AoI (TOJEA), which consists of two phases. In the first phase, an Equilibrium Optimizer (EO) algorithm with a variable individual size via its coding and updating strategies, in which each particle (individual) with its concentration (position) represents a target solution i.e. the whole deployment of hover points, is proposed to optimize the number and locations of hover points. Based on the deployment of hover points, a low-complexity greedy algorithm is adopted in the second stage to generate the optimal visiting order for the UAV. Experimental results demonstrate that TOJEA outperforms other algorithms on ten instances with up to 400 IoT devices.

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针对无人机无线供电 MEC 系统的综合轨迹优化，实现能耗和 AoI 的联合最小化

本文研究了一种支持无线供电移动边缘计算（MEC）系统的无人飞行器（UAV），在该系统中，配备射频链和 MEC 服务器的无人飞行器可为物联网（IoT）设备充电提供可持续的无线能源，并在指定悬停点悬停时执行这些设备的计算任务。我们的目标是最大限度地降低该系统的能耗和信息年龄（AoI）的加权和，这取决于无人机在指定点的悬停时间和飞行时间。为此，我们共同优化了悬停点的部署和无人机访问这些点的顺序。这是一个 NP 难、混合整数非凸问题，传统方法难以解决。为了解决这个问题，我们提出了一种联合能耗和 AoI 的轨迹优化算法（TOJEA），它包括两个阶段。在第一阶段，我们提出了一种平衡优化算法（EO），该算法通过编码和更新策略实现可变的个体大小，其中每个粒子（个体）的浓度（位置）代表一个目标解，即悬停点的整体部署，从而优化悬停点的数量和位置。在悬停点部署的基础上，第二阶段采用低复杂度贪婪算法为无人机生成最优访问顺序。实验结果表明，在多达 400 个物联网设备的 10 个实例中，TOJEA 的性能优于其他算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Networks 工程技术-电信学

CiteScore

10.80

自引率

3.60%

发文量

434

审稿时长

8.6 months

期刊介绍： Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.