A distributed multi-hop intra-clustering approach based on neighbors two-hop connectivity for IoT networks

Mohamed Sofiane Batta, Hakim Mabed, Zibouda Aliouat, Saad Harous

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Under a dense and large IoT network, a star topology where each device is directly connected to the Internet gateway may cause serious waste of energy and congestion issues. Grouping network devices into clusters provides a suitable architecture to reduce the energy consumption and allows an effective management of communication channels. Although several clustering approaches were proposed in the literature, most of them use the single-hop intra-clustering model. In a large network, the number of clusters increases and the energy draining remains almost the same as in un-clustered architecture. To solve the problem, several approaches use the k-hop intra-clustering to generate a reduced number of large clusters. However, k-hop proposed schemes are, generally, centralized and only assume the node direct neighbors information which lack of robustness. In this regard, the present work proposes a distributed approach for the k-hop intra-clustering called Distributed Clustering based 2-Hop Connectivity (DC2HC). The algorithm uses the two-hop neighbors connectivity to elect the appropriate set of cluster heads and strengthen the clusters connectivity. The objective is to optimize the set of representative cluster heads to minimize the number of long range communication channels and expand the network lifetime. The paper provides the convergence proof of the proposed solution. Simulation results show that our proposed protocol outperforms similar approaches available in the literature by reducing the number of generated cluster heads and achieving longer network lifetime.

Original languageEnglish
Article number873
Pages (from-to)1-28
Number of pages28
JournalSensors (Switzerland)
Volume21
Issue number3
DOIs
Publication statusPublished - Feb 1 2021

Keywords

  • Distributed clustering
  • Dynamic intra-clustering
  • IoT
  • Multi-hop clustering
  • WSN

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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