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Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review.

Damien Wohwe Sambo1, Blaise Omer Yenke2, Anna Förster3

  • 1Faculty of Science, University of Ngaoundéré, 454 Ngaoundéré, Cameroon. wsdamieno@gmail.com.

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Summary

Optimized clustering algorithms enhance Wireless Sensor Networks (WSNs) performance. Swarm Intelligence excels in large-scale WSNs needing low energy and high scalability, while Fuzzy Logic suits smaller deployments.

Keywords:
clusteringcomputational intelligencelarge wireless sensor networksmachine learningmetaheuristic

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSNs) are crucial for smart cities and ecological monitoring.
  • Hierarchical clustering improves WSN performance and network lifetime by organizing nodes into clusters.
  • Traditional clustering methods struggle with the complexity of large-scale WSNs.

Purpose of the Study:

  • To review and compare optimized clustering algorithms for Wireless Sensor Networks (WSNs).
  • To evaluate clustering solutions based on 10 key performance parameters.
  • To identify the most suitable clustering paradigms for diverse WSN applications.

Main Methods:

  • Conducted a comprehensive review of Machine Learning and Computational Intelligence-based clustering algorithms for WSNs.
  • Evaluated algorithms against 10 defined parameters, including energy consumption, data delivery rate, and scalability.
  • Compared centralized and decentralized clustering approaches, analyzing paradigm-specific strengths.

Main Results:

  • Centralized Swarm Intelligence-based clustering demonstrates superior performance for applications demanding low energy consumption, high data delivery, and scalability.
  • Fuzzy Logic-based solutions are effective for WSN applications with a limited number of nodes.
  • Optimized clustering algorithms based on environmental behaviors outperform traditional methods.

Conclusions:

  • Swarm Intelligence is highly recommended for large-scale, performance-critical WSN applications.
  • Fuzzy Logic offers a viable alternative for smaller, less complex WSN deployments.
  • The choice of clustering paradigm should align with specific WSN application requirements and constraints.