{
    "@context": "https://schema.org",
    "@type": "ScholarlyArticle",
    "identifier": {
        "jsc_id": 26,
        "DOI": "10.5281/zenodo.22853837",
        "DOI_url": "https://doi.org/10.5281/zenodo.22853837"
    },
    "title": "Energy- and Latency-Aware Computation Offloading in Edge–Fog–Cloud Architectures for Industry 4.0",
    "abstract": "Industry 4.0 transforms manufacturing through cyber-physical systems, the Industrial Internet of Things, digital twins, and data-driven intelligence, yet the volume of latency-sensitive data these technologies generate strains conventional cloud-only computing. Edge and fog computing have emerged as the central enabling paradigm, distributing computation across a device–fog–cloud continuum. This paper surveys the enabling technologies of Industry 4.0 with an emphasis on edge–cloud computing and formulates the resulting computation-offloading problem as a constrained, load-dependent optimization model that jointly minimizes task latency and device energy under deadline and capacity constraints. A genetic algorithm and a particle swarm optimizer are developed and benchmarked against five baseline strategies on synthetic industrial workloads. Across one hundred tasks averaged over ten instances, the genetic algorithm reduced the weighted objective by 34.1 percent relative to a greedy heuristic and by 82.6 percent relative to cloud-only execution, while lowering deadline violations to 1.6 percent. The latency–energy trade-off, convergence, and scalability are analyzed, and managerial implications for industrial deployment are discussed.",
    "authors": [
        {
            "sequence": "first",
            "given": "Mohammad Javad",
            "family": "Esfahani",
            "name": "Mohammad Javad Esfahani",
            "affiliation": [
                {
                    "name": "Faculty of member Academy of Social, Scientific And Applied Research ASSAAR, London, UK"
                }
            ],
            "email": "mj.esfahani@assaar.co.uk",
            "is_corresponding": true
        }
    ],
    "keywords": [
        "Industry 4.0",
        "Edge computing",
        "Computation offloading",
        "Metaheuristic optimization",
        "Industrial Internet of Things"
    ],
    "section": "Research Article",
    "language": "en",
    "journal": {
        "name": "Journal of Smart Computing",
        "abbreviation": "JSC",
        "issn": "2979-8752",
        "publisher": "Center of Intelligent Operations and Technology Research, United Kingdom",
        "url": "https://ciotr.com/jsc/"
    },
    "issue": {
        "volume": 1,
        "number": 1,
        "year": 2025,
        "url": "https://ciotr.com/jsc/issue/3"
    },
    "pages": {
        "first": "12",
        "last": "26",
        "range": "12-26"
    },
    "dates": {
        "submitted": "2026-06-21",
        "published": "2025-08-29"
    },
    "urls": {
        "landing_page": "https://ciotr.com/jsc/article/26",
        "pdf": "https://ciotr.com/jsc/pdf/article-26.pdf",
        "metadata": "https://ciotr.com/jsc/article/26.json"
    },
    "license": {
        "name": "Creative Commons Attribution 4.0 International",
        "code": "CC BY 4.0",
        "url": "https://creativecommons.org/licenses/by/4.0/"
    },
    "citation": {
        "apa": "Esfahani, M.J. (2025). Energy- and Latency-Aware Computation Offloading in Edge–Fog–Cloud Architectures for Industry 4.0. Journal of Smart Computing, 1(1), 12-26. https://doi.org/10.5281/zenodo.22853837"
    }
}