{
  "abstract": "With the development of the Internet of Things (IoT) and Information Communication Technology (ICT), a large number of computation-intensive terminal applications have emerged. Multi-access Edge Computing (MEC) is a promising technology to meet terminal devices’ computational and battery-capacity demands, yet it also generates significant energy consumption and carbon emissions. This paper investigates the dynamic task offloading problem in Energy Harvesting (EH)-assisted edge-cloud systems. We aim to minimize the overall carbon emissions of the system while ensuring long-term stability and satisfying multi-user, multi-server constraints by optimizing CPU frequencies, offloaded task sizes, and offloading decisions. Specifically, we formulate the problem as a multi-slot stochastic optimization problem, which is NP-hard. Employing a stochastic optimization framework, we transform and decompose the problem into multiple subproblems that can be solved efficiently. Considering the coupling between offloading decisions and resource allocation, we propose the Carbon-Aware Resource Allocation and Offloading (CARAE) algorithm to obtain an efficient carbon-aware task offloading strategy. A series of theoretical analyses and simulations are conducted to verify that the proposed CARAE algorithm effectively reduces overall carbon emissions while maintaining system performance.",
  "abstract_shown": true,
  "affiliations": [
    "International College, Hebei University",
    "Olin Business School, Washington University",
    "Department of Computing Technologies, Swinburne University of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Han Fu"
    },
    {
      "affs": [
        1
      ],
      "name": "Qiaochu Lu"
    },
    {
      "affs": [
        2
      ],
      "name": "Yaozong Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/etpcp0p69e4p0.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62950-0",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62950-0",
  "id": "etpcp0p69e4p0",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62950-0",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1705|IoT and Edge/Fog Computing"
    ]
  ],
  "openalex": "W7170033988",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62950-0_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/etpcp0p69e4p0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:59Z",
      "native_id": "ff103463f6e54b19849ce2257dcfbe67",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-62950-0"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Carbon-aware resource allocation and task offloading in EH-assisted edge-cloud systems",
  "title_text": "Carbon-aware resource allocation and task offloading in EH-assisted edge-cloud systems",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:59Z",
  "url": "https://research.draconitic.com/paper/etpcp0p69e4p0",
  "venue": "Scientific Reports",
  "versions": []
}