Joint Split Offloading and Trajectory Scheduling for UAV-Enabled Mobile Edge Computing in IoT Network

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-08 DOI:10.1109/TNSE.2024.3476168

Yunkai Wei;Zikang Wan;Yinan Xiao;Supeng Leng;Kezhi Wang;Kun Yang

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引用次数: 0

Abstract

Unmanned Aerial Vehicles (UAV) can provide mobile edge computing (MEC) service for resource-limited devices in Internet of Things (IoT). In such scenario, partial offloading can be used to balance the computing task between the UAV and the IoT devices for higher efficiency. However, traditional partial offloading is not suitable for training deep neural network (DNN), since DNN models cannot be portioned with a continuous ratio. In this paper, we introduce a split offloading scheme, which can flexibly split the DNN training task into two parts based on the DNN layers, and allocate them to the IoT device and UAV respectively. We present a scheme to synchronize the training and communicating period of DNN layers in the UAV and IoT device, and thus reduce the model training time. Based on this scheme, an optimization model is proposed to minimize the UAV energy consumption, which jointly optimizes the UAV trajectory, the DNN split position and the service time scheduling. We divide the problem into two subproblems and solve it with an iterative solution. Simulation results show the proposed scheme can reduce the model training time and the UAV energy consumption by up to 25% and 14.4% compared with benchmark schemes, respectively.

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物联网网络中无人机移动边缘计算的联合分割卸载和轨迹调度

无人飞行器（UAV）可以为物联网（IoT）中资源有限的设备提供移动边缘计算（MEC）服务。在这种情况下，部分卸载可用于平衡无人机和物联网设备之间的计算任务，以提高效率。然而，传统的部分卸载并不适用于深度神经网络（DNN）的训练，因为 DNN 模型无法以连续的比例进行分配。本文介绍了一种拆分卸载方案，可根据 DNN 层数将 DNN 训练任务灵活拆分成两部分，分别分配给物联网设备和无人机。我们提出了一种方案，使无人机和物联网设备中 DNN 层的训练和通信时间同步，从而缩短了模型训练时间。在此方案的基础上，我们提出了一个优化模型来最小化无人机能耗，该模型联合优化了无人机轨迹、DNN 分割位置和服务时间调度。我们将问题分为两个子问题，并采用迭代法求解。仿真结果表明，与基准方案相比，所提出的方案可将模型训练时间和无人机能耗分别减少 25% 和 14.4%。

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

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.