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Wahidah Md Shah
UniversitiTeknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering

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Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks Ali Abdul-hussian Hassan; Wahidah Md Shah; Mohd Fairuz Iskandar Othman; Hayder Abdul Hussien Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (673.948 KB) | DOI: 10.11591/ijece.v10i2.pp1515-1523

Abstract

The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.
Co-Authors Ali Abdul-hussian Hassan Hayder Abdul Hussien Hassan Mohd Fairuz Iskandar Othman