{
  "abstract": "The rapid growth of Internet of Things (IoT) applications has increased the need for task-offloading mechanisms that can operate efficiently under heterogeneous Fog resources, dynamic workloads, and stringent latency requirements. A recurring limitation in existing allocation and matching approaches is that the choice of resource-bundle granularity is often treated as a secondary sizing step rather than as an integral part of the association decision. This paper presents Task-Adaptive Virtual Resource Unit Matching (TAVRUM), a contract-level framework in which each allocation decision explicitly couples a task, a Fog node, and a discrete Virtual Resource Unit (VRU) level. TAVRUM applies feasibility-first filtering to enforce multidimensional resource and deadline constraints before preference ranking, incorporates allocation waste into contract utility, and uses risk-aware admission together with event-triggered local rematching and hysteresis for dynamic conditions. Under the trace-calibrated experimental setting, TAVRUM reduces allocation fragmentation from 0.733 for the EDF-Feasible comparator to 0.656, an improvement of approximately 10.5%, while its task-outage rate is 33.27% compared with 32.89% for EDF-Feasible. This resource-efficiency gain is accompanied by higher mean latency (1.431 versus 1.216) and nearly unchanged P95 latency (3.247 versus 3.235). On the evaluated small instances, TAVRUM has a mean objective gap of 11.76% relative to the exact MILP reference. The results therefore characterize a measurable resource-efficiency–QoS trade-off rather than universal superiority across all metrics.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah 21961, Saudi Arabia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Deafallah Alsadie",
      "orcid": "0000-0002-4952-1136"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bwdx7t8fjf22e.bib",
  "cited_by": 0,
  "doi": "10.3390/math14193546",
  "fulltext_url": null,
  "id": "bwdx7t8fjf22e",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/19/3546",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1705|IoT and Edge/Fog Computing"
    ]
  ],
  "openalex": "W7214899770",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/bwdx7t8fjf22e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14193546",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/19/3546"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "TAVRUM: Feasibility-First Contract Matching for Fragmentation-Aware Resource Allocation in IoT–Fog Networks",
  "title_text": "TAVRUM: Feasibility-First Contract Matching for Fragmentation-Aware Resource Allocation in IoT–Fog Networks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/bwdx7t8fjf22e",
  "venue": "Mathematics",
  "versions": []
}