{
  "abstract": "In a mobile ad-hoc network, it has been observed that the existing weighted clustering algorithm induces high computational overhead on the mobile nodes in the cluster-head election process and distributes the load among the cluster-heads unfairly. Also, the cluster maintenance cost is very high due to the expiration or movement of the cluster-head. The purpose of this work is to mitigate the weaknesses of the existing weight-based clustering algorithm. The proposed “Marking-based Load Balancing Weighted Clustering Framework for Mobile ADHOC Network (MLWCM)” marks the weak nodes before the beginning of the election process and spares them from massive weight calculation, thus saving the scarce resources of the network. The concept of inmate affiliation reduces the maintenance cost considerably. To ensure fair load distribution among the cluster-heads, a handling factor has been computed, which is the number of nodes that a cluster-head can handle based on its available battery power. Also, to minimize the maintenance cost, a new event called inmate affiliation has been introduced. The simulation outcome reveals that the proposed framework outperforms the existing weighted clustering algorithm. MLWCM produces healthier and more stable cluster-heads. The number of members is fairly assigned to cluster-heads. Inmate affiliation allows a displaced node to get attached to an available nearby cluster silently, without inducing clustering in the complete network, which results in lower maintenance costs. Obtained results: By the simulation, it has been revealed that the MLWCM yields healthy cluster-heads in terms of battery power at a low computational cost, hence prolonging the life of the network and enabling efficient routing in the mobile ad-hoc network. For the proposed framework, MLWCM, the average battery life of the cluster-head is 73.8%, whereas for the existing WCA, the average battery life is 41.8%; hence, MLWCM enhances the stability of the network.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India",
    "Department of CSE, MRIIRS, Faridabad, India",
    "Department of Electrical, Electronics and Communication Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India",
    "Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Prashant Dixit"
    },
    {
      "affs": [
        1
      ],
      "name": "Sonia Setia"
    },
    {
      "affs": [
        2
      ],
      "name": "Ramesh Chandra Sahoo"
    },
    {
      "affs": [
        3
      ],
      "name": "Manish Sharma"
    },
    {
      "name": "Sameena Pathan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a90yzfgbs25jx.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360099",
  "fulltext_url": null,
  "id": "a90yzfgbs25jx",
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    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0360099",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1705|Mobile Ad Hoc Networks"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Physiological parameters/Body weight"
    ],
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      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Clustering algorithms"
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      "/Physical sciences/Mathematics/Statistics/Confidence intervals"
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      "/Research and analysis methods/Mathematical and statistical techniques/Cluster analysis/k means clustering"
    ],
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      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Clustering algorithms"
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  "openalex": "W7221992601",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0360099&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/a90yzfgbs25jx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0360099",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0360099"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:statistics",
      "subfield": "statistics"
    },
    {
      "field": "medicine",
      "origin": "plos:physiology",
      "subfield": "physiology"
    }
  ],
  "title": "Marking-based load balancing Weighted Clustering Framework for Mobile ADHOC Network",
  "title_text": "Marking-based load balancing Weighted Clustering Framework for Mobile ADHOC Network",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:23Z",
  "url": "https://research.draconitic.com/paper/a90yzfgbs25jx",
  "venue": "PLOS One",
  "versions": []
}