Age of Information Outage Probability Analysis for Computation Offloading in IIoT Networks

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Age of Information Outage Probability Analysis for Computation Offloading in IIoT Networks
Title:
Age of Information Outage Probability Analysis for Computation Offloading in IIoT Networks
Journal Title:
IEEE Communications Letters
Keywords:
Publication Date:
13 September 2023
Citation:
Ernest, T. Z. H., & Madhukumar, A. S. (2023). Age of Information Outage Probability Analysis for Computation Offloading in IIoT Networks. IEEE Communications Letters, 27(9), 2471–2475. https://doi.org/10.1109/lcomm.2023.3294931
Abstract:
In this paper, the performance characterization of outage probability and age of information (AoI) for computation offloading in multi-access edge computing (MEC)-enabled industrial Internet-of-Things (IIoT) networks is investigated. Through newly obtained cumulative density functions of signalto-interference-plus-noise ratios in the MEC-enabled IIoT network, closed-form AoI outage probability expressions are derived for MEC-based computation offloading (MEC-CO) and cloudonly computation offloading (CLD-CO) protocols assuming Rician fading, base station locations modeled as binomial point processes, and Gamma distributed delays in the wired backhaul. Suitable communication radius and task sizes to enable reliable computation offloading in both rural and urban IIoT scenarios are identified for both MEC-CO and CLD-CO through extensive analysis, where it is shown that MEC-CO attains superior AoI performance over CLD-CO.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: A*STAR Advanced Remanufacturing and Technology Centre (ARTC)
Grant Reference no. : N/A
Description:
© 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1089-7798
1558-2558
2373-7891
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