Friday 29th of March 2024
 

Evolving a Hybrid K-Means Clustering Algorithm for Wireless Sensor Network Using PSO and GAs


Alaa Sheta and Basma Solaiman

Wireless Sensor Networks (WSN) became an essential component in many real-life applications such as military, smart energy, commercial, health and many others. However, WSN still suffer many problems related to energy consumption. Clustering found to be an effective technique to solve the energy consumption problem for WSN by avoiding long distance communication. In this paper, we explore our initial idea on developing a hybrid clustering algorithm which has two folds 1) Use the K-Means unsupervised learning algorithms to select the sensors belonging to each cluster using an arbitrary number of clusters 2) Use Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) separately to select the best CHs. We name these two algorithms as KPSO and KGAs. The developed hybrid algorithms are tested over number of experiments with various layouts. KPSO provided better results compared to the KGAs.

Keywords: Wireless Sensor Network, Clustering Algorithms, K-Means, Particle Swarm Optimization, Genetic Algorithms

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ABOUT THE AUTHORS

Alaa Sheta
Alaa Sheta received his B.E., M.Sc. degrees in Electronics and Communication Engineering from the Faculty of Engineering, Cairo University in 1988 and 1994, respectively. He received his Ph.D. degree from the Computer Science Department, School of Information Technology and Engineering, George Mason University, Fairfax, VA, USA in 1997. Currently, Prof. Sheta is a faculty member with the Software Engineering Department, Zarqa University, Jordan. He is on leave from the Computers and Systems Department, Electronics Research Institute (ERI), Cairo, Egypt. He published over 100 journal and conference papers, book chapters and three books in the area of image processing, manufacture process modeling and business intelligence. His research interests include Evolutionary Computation, System Identification, Automatic Control, Image Processing, Swarm Intelligence, Fuzzy Logic, Neural Networks Software Cost Estimation and Software Reliability Modeling.

Basma Solaiman
Basma Solaiman received her BSc. in Electrical Engineering and Automatic Control from Ain Shams University, Cairo, Egypt in 1990 and M.Sc. in Computer Science from the American University in Cairo in 2004. She is on leave from the New and Renewable Energy Authority (NREA), Cairo, Egypt. She is currently a PhD student with the Computer Science Department, Sudan University of Science and Technology (SUST), Khartoum, Sudan. Her research interests include Evolutionary Computation, Wireless Sensor Network and Data Mining.


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