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DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications

Cluster Computing volume 26, pages 1077–1098 (2023)Cite this article

Abstract

Developing clustering algorithms for energy optimization, for the Internet of Things (IoT) applications based mainly on wireless sensors networks services, is a major research challenge. The large amount of data generated by this type of networks, requires a huge quantity of energy and a large number of Internet connections to support communication between different devices via the Internet. Therefore, clustering protocols for data routing in IoT, also referred to as hierarchical routing protocols, need to take into account energy efficiency, the number of individual connections and scalability. In this paper, we propose a novel distributed clustering protocol based on the objects’ performances aggregation for hierarchical communications in IoT applications focused on Wireless Sensors Networks (WSNs) for the self election of Cluster Heads (CHs) and the clusters construction. Each node calculates a threshold T(i) considered as a competition timer to become a CH. A node can be a CH only once among a fixed number of rounds. An optimum number of clusters is computed based on the network parameters. Simulation results show that our proposed solution is better with energy management performance parameters compared to the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol and two other developed energy-efficient protocols namely LEACH-MAC and TB-LEACH.

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Authors and Affiliations

  1. Laboratoire d’Informatique Médicale (LIMED), Faculté des Sciences Exactes, Université de Bejaia, 06000, Bejaia, Algeria

    Foudil Mir

  2. Data Science Research Centre, University of Derby, Derby, UK

    Farid Meziane

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  1. Foudil Mir
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  2. Farid Meziane
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FM: \(70\%\) ; FM: \(30\%\).

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Mir, F., Meziane, F. DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications. Cluster Comput 26, 1077–1098 (2023). https://doi.org/10.1007/s10586-022-03741-w

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