AI-Based Sustainable and Intelligent Offloading Framework for IIoT in Collaborative Cloud-Fog Environments

The cloud paradigm is one of the most trending areas in today's era due to its rich profusion of services. However, it fails to serve the latency-sensitive Industrial Internet of Things (IIoT) applications associated with automotives, robotics, oil and gas, smart communications, Industry 5.0, e...

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Bibliographic Details
Published in:IEEE transactions on consumer electronics 2024-02, Vol.70 (1), p.1414-1422
Main Authors: Kumar, Mohit, Walia, Guneet Kaur, Shingare, Haresh, Singh, Samayveer, Gill, Sukhpal Singh
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
artificial intelligence (AI)
Cloud computing
Costs
Decisions
Edge computing
Energy consumption
fog computing
Fuzzy control
Industrial applications
Industrial Internet of Things
Internet of Things
Internet of Things (IoT)
Multilayers
Quality of service
resource allocation
Resource management
Robotics
Task analysis
Task offloading
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Summary:The cloud paradigm is one of the most trending areas in today's era due to its rich profusion of services. However, it fails to serve the latency-sensitive Industrial Internet of Things (IIoT) applications associated with automotives, robotics, oil and gas, smart communications, Industry 5.0, etc. Hence, to strengthen the capabilities of IIoT, fog computing has emerged as a promising solution for latency-aware IIoT tasks. However, the resource-constrained nature of fog nodes puts forth another substantial issue of offloading decisions in resource management. Therefore, we propose an Artificial Intelligence (AI)-enabled intelligent and sustainable framework for an optimized multi-layered integrated cloud fog-based environment where real-time offloading decisions are accomplished as per the demand of IIoT applications and analyzed by a fuzzy based offloading controller. Moreover, an AI based Whale Optimization Algorithm (WOA) has been incorporated into a framework that promises to search for the best possible resources and make accurate decisions to ameliorate various Quality-of-Service (QoS) parameters. The experimental results show an escalation in makespan time up to 37.17%, energy consumption up to 27.32%, and execution cost up to 13.36% in comparison to benchmark offloading and allocation schemes.
ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2023.3320673