Timeliness-Aware Computation Offloading Strategies for IIoT Networks

IF 7.9 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-19 DOI:10.1109/TNSE.2025.3570379

Tan Zheng Hui Ernest;A S Madhukumar

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引用次数: 0

Abstract

This paper investigates the peak age of information (PAoI) violation probability and mean PAoI of computation offloading strategies in multi-access edge computing-enabled (MEC-enabled) industrial Internet-of-Things (IIoT) networks. In particular, a comprehensive PAoI analysis framework for computation offloading strategies is proposed in this work. Through closed-form cumulative distribution function (CDF) expressions derived for received signal-to-interference-plus-noise ratios (SINRs) and PAoI arising from tandem M/M/1 queues, new closed-form expressions for PAoI violation probability and mean PAoI are obtained for the uplink timeliness-aware (UTA), joint uplink-and-computing timeliness-aware (JUCTA), and cloud-only (CL) computation offloading strategies. Extensive analysis demonstrate that the proposed UTA and JUCTA strategies outperform the CL strategy in MEC-enabled IIoT networks and are thus viable to support mission-critical IIoT applications. Crucially, it is also shown that the PAoI violation probability and mean PAoI of the considered computation offloading strategies hinges greatly on computation delay, communications radius, and task generation rates.

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工业物联网网络的时效性感知计算卸载策略

研究了基于多接入边缘计算的工业物联网（IIoT）网络中计算卸载策略的峰值信息年龄（PAoI）违规概率和平均PAoI。特别地，本文提出了一个用于计算卸载策略的综合pai分析框架。通过推导串联M/M/1队列中接收到的信噪比（SINRs）和PAoI的闭式累积分布函数（CDF）表达式，得到上行时序感知（UTA）、上行与计算联合时序感知（JUCTA）和纯云（CL）计算卸载策略的PAoI违反概率和平均PAoI的闭式表达式。广泛的分析表明，提出的UTA和juta策略在支持mec的工业物联网网络中优于CL策略，因此可以支持关键任务的工业物联网应用。重要的是，研究还表明，所考虑的计算卸载策略的pai违反概率和平均pai在很大程度上取决于计算延迟、通信半径和任务生成率。

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.