| Autor: |
Mtopi, Yann Brice Chebu, Bomgni, Alain Bertrand, Ali, Hafiz Munsub, Zanfack, David R. Gnimpieba, Ejaz, Waleed, Djamegni, Clémentin Tayou, Gnimpieba, Etienne Zohim |
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| Zdroj: |
Cluster Computing; Apr2023, Vol. 26 Issue 2, p993-1009, 17p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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