Task Co-Offloading for D2D-assisted Mobile Edge Computing in Industrial Internet of Things

Xingxia Dai, Zhu Xiao, Hongbo Jiang, Mamoun Alazab, John Lui, Schaharam Dustar, Jiangchuan Liu

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this work, we investigate task cooffloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.

    Original languageEnglish
    Pages (from-to)1-12
    Number of pages12
    JournalIEEE Transactions on Industrial Informatics
    DOIs
    Publication statusE-pub ahead of print - 15 Mar 2022

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