Fairness-Based 3D Multi-UAV Trajectory Optimization in Multi-UAV-Assisted MEC System

Document Type

Article

Publication Title

IEEE Internet of Things Journal

Abstract

Unmanned aerial vehicles (UAVs) -assisted mobile edge computing (MEC) communication system has recently gained increasing attention. In this paper, we investigate a 3D multi-UAV trajectory optimization based on ground devices (GDs) selecting the target UAV for task computing. Specifically, we first design a 3D dynamic multi-UAV-assisted MEC system in which GDs have real-time mobility and task update. Next, we formulate the system communication, computation, and flight energy consumption as objective functions based on fairness among UAVs. Then, to pursue fairness among UAVs, we theoretically deduce and mathematically prove the optimal GDs’ selectivity and offloading strategy, that is, how GDs select the optimal UAV for task offloading and how much to offload. While ensuring the optimal offloading strategy and GDs’ selectivity between UAVs and GDs at each step, we model UAV trajectories as a sequence of location updates of all UAVs and apply a multi-agent deep deterministic policy gradient (MADDPG) algorithm to find the optimal solution. Simulation results demonstrate that we achieve the minimum energy consumption under the premise of fairness and the efficiency of model processing tasks.

First Page

1

Last Page

1

DOI

10.1109/JIOT.2023.3241087

Publication Date

1-31-2023

Keywords

computing offloading, Delays, Energy consumption, fairness, Internet of Things, mobile edge computing (MEC), multi-agent deep deterministic policy gradient (MADDPG), Resource management, selectivity, Task analysis, Three-dimensional displays, Trajectory, trajectory optimization, Unmanned aerial vehicles (UAVs)

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