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    This study introduces distributed k-means and fuzzy c-means algorithms for wireless sensor networks (WSNs). These algorithms efficiently cluster sensor data, achieving results comparable to centralized methods.

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    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Networked Systems

    Background:

    • Wireless Sensor Networks (WSNs) generate vast amounts of data requiring efficient processing.
    • Centralized clustering algorithms face scalability challenges in distributed WSN environments.
    • Effective data partitioning is crucial for WSN applications like environmental monitoring and target tracking.

    Purpose of the Study:

    • To develop distributed k-means and fuzzy c-means algorithms tailored for WSNs.
    • To enhance clustering accuracy and convergence speed using a distributed k-means++ initialization.
    • To enable decentralized data analysis and pattern discovery within WSNs.

    Main Methods:

    • Utilized consensus algorithms from multiagent consensus theory for sensor data exchange.
    • Proposed a distributed k-means++ algorithm for optimal initial centroid selection.
    • Implemented distributed k-means and fuzzy c-means algorithms for data partitioning.

    Main Results:

    • The distributed algorithms effectively partition WSN data into measure-dependent groups.
    • Achieved small within-group and large out-group distances for k-means clustering.
    • Fuzzy c-means provided data partitioning with degrees of membership from 0 to 1.
    • Simulation results demonstrated performance comparable to centralized clustering algorithms.

    Conclusions:

    • The developed distributed clustering algorithms are effective for WSN data analysis.
    • Distributed k-means++ initialization improves convergence and global optimum likelihood.
    • These algorithms offer a scalable and efficient solution for decentralized data clustering in WSNs.