Open Access

ARTICLE

Reliable Task Offloading for 6G-Based IoT Applications

Usman Mahmood Malik¹, Muhammad Awais Javed², Ahmad Naseem Alvi², Mohammed Alkhathami^3,*

1 Department of Computer Software Engineering, National University of Science and Technology (NUST), Islamabad, 44000, Pakistan
2 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, 45550, Pakistan
3 Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia

* Corresponding Author: Mohammed Alkhathami. Email:

(This article belongs to the Special Issue: Distributed Computing with Applications to IoT and BlockChain)

Computers, Materials & Continua 2025, 82(2), 2255-2274. https://doi.org/10.32604/cmc.2025.061254

Received 20 November 2024; Accepted 31 December 2024; Issue published 17 February 2025

Abstract

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing, and data storage services which are required for several 6G applications. Artificial Intelligence (AI) algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability. In this paper, the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers (POMH) in which larger tasks are divided into smaller subtasks and processed in parallel, hence expediting task completion. However, using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously, preventing resource wastage. Additionally, applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes, resulting in a difficult-to-solve, externalities problem. This paper introduces a novel many-to-one matching-based algorithm, designed to address the externalities problem and optimize resource allocation within POMH scenarios. Additionally, we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH scenarios. The performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes, revealing many advantages of the proposed approach. The simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature, yielding a remarkable 52 reduction in task latency, particularly under high workloads.

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Keywords

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