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MultiHop optimal time complexity clustering for emerging IoT applications

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Abstract

The sixth-generation (6G) wireless communication networks are expected to support heterogeneous services and decentralized infrastructure with resource-aware smart self-organization for Internet of Things (IoT) applications. Large-scale IoT applications face challenges like load balancing and scalability within the network due to the inherent vulnerability of ad-hoc structures. This paper proposes a multiHop constant-time complexity clustering algorithm (MultiHopFast) for IoT networks to address these challenges. The proposed MultiHopFast algorithm reduces the computing burden from IoT nodes with smart load balancing to ensure IoT network scalability. The algorithm addresses the network load, scalability, and time efficiency challenges. Using neighbourhood heuristics, the MultiHopFast algorithm builds appropriate size (i.e., up to 5 hops) of clusters with participating IoT nodes. Each cluster is associated with a cluster head (CH) (or a coordinator). The MultiHopFast algorithm probabilistically selects CH for each cluster. When compared with state-of-the-art counterparts, MultiHopFast algorithm: (i) operates with constant-time complexity in a large scale network as well as in small-scale networks, (ii) runs without any impact on network scalability, and (iii) creates 12% fewer CHs to save precious resources such as energy. Better use of heuristics and resource-aware self-organization, constant-time computational complexity, and network operation with fewer CHs demonstrate that the performance of the MultiHopFast algorithm surpasses the compared algorithms in the literature. The MultiHopFast algorithm is envisioned as a better candidate to match the standard and expectations set by the 6G wireless communications.

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The present study is based on synthesized data generated randomly by the authors based on some parameters mentioned in the above text.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Dschang, Dschang, Cameroon

    Yann Brice Chebu Mtopi

  2. Department of Biomedical Engineering, University of South Dakota, Vermillion, USA

    Alain Bertrand Bomgni, Hafiz Munsub Ali & Etienne Zohim Gnimpieba

  3. AGT Technology, Paris, France

    David R. Gnimpieba Zanfack

  4. Department of Electrical Engineering, Lakehead University, Barrie, ON, Canada

    Waleed Ejaz

  5. Department of Computer Science, IUT-Bandjoun, Bandjoun, Cameroon

    Clémentin Tayou Djamegni

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  1. Yann Brice Chebu Mtopi
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Contributions

This work was conceptualized and designed by YC and ABB. Experiment was designed by YC, ABB, HMA and DGZ. Initial draft was prepared by YC and ABB. After initial draft, HMA, YC, ABB, DGZ, CTD, EGZ, WE give input to improve quality and presentation. EGZ is PI of this work and edited first and subsequent draft of the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Alain Bertrand Bomgni.

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Mtopi, Y.B.C., Bomgni, A.B., Ali, H.M. et al. MultiHop optimal time complexity clustering for emerging IoT applications. Cluster Comput 26, 993–1009 (2023). https://doi.org/10.1007/s10586-022-03637-9

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