International Journal of Electrical and Computer Engineering (IJECE) Vol. 10, No. 2, April 2020, pp. 1515~1523 ISSN: 2088-8708, DOI: 10.11591/ijece.v10i2.pp1515-1523  1515 Journal homepage: http://ijece.iaescore.com/index.php/IJECE Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks Ali Abdul-hussian Hassan 1 , Wahidah Md Shah 2 , Mohd Fairuz Iskandar Othman 3 , Hayder Abdul Hussien Hassan 4 1,2,3 Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia 1 College of Education for Pure Sciences, University of Kerbala, Iraq 4 College of Management and Economics, University of Kerbala, Iraq Article Info ABSTRACT Article history: Received Jun 12, 2019 Revised Sep 25, 2019 Accepted Oct 5, 2019 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. Keywords: Balanced cluster size Clustering algorithms FCM KM WSNs Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Ali Abdul-hussian Hassan, Faculty of Information and Communication Technology, UniversitiTeknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. Email: altaeeali800@yahoo.com 1. INTRODUCTION Wireless Sensor Networks (WSNs) is utilized in numerous applications since they are suitable for various environments. It can function independently in conditions of harsh or hazardous places, where these places impose great risks to human beings, and is not advisable for them to be present there. Nevertheless, the sensor's lifetime is only related to their batteries, which are impossible to be replaced or recharged [1–3]. Consequently, with a view of prolonging the network lifetime, WSN used clustering approach for the clustering of the nodes, where the segregation of the sensor nodes into small clusters are executed based on their Euclidean distance. Each cluster employs one node to be the cluster head (CH). The CH possesses numerous functions in addition to sensing the environment such as; data gathering from all cluster members, and its conveyance to the main node termed as Base Station (BS), the conveyance of other CHs data to the next hop, and the fusion of the cluster data. Clustering approach is the most popular energy efficient technique which provides various advantages such as prolonging the network lifetime, scalability and