Fuzzy-Kohonen Self-organizing Clustering Algorithm in Wireless Sensor Networks

• Published in: Applications of Computing and Communication Technologies Vol. 899; pp. 225 - 236
• Main Authors: Kashyap, Pankaj Kumar, Kumar, Kirshna, Kumar, Sushil
• Format: Book Chapter
• Language: English
• Published: Singapore Springer 01.01.2018; Springer Singapore
• Series: Communications in Computer and Information Science
• Subjects: Fuzzy C-Means; Kohonen self-organizing map; Learning rate; Membership function
• ISBN: 9811320349; 9789811320347
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-981-13-2035-4_20

Rapid development of smart device because of internet of thing it opens the door for configures the wireless sensor network by self-organization and force to use soft computing technique rather than mathematical tool. The most appealing issue in wireless sensor networks to produce self-organized network which balance the network load. In this paper we proposed self-organizing cluster technique based on Fuzzy C-Means and Kohonen clustering network (KCN). KCN is well known for cluster formation but it have some disadvantage such as termination is not converged, learning strategy does not optimized any model. So we use the feature of Fuzzy C-means algorithm of self-optimization and membership function for learning rate in Kohonen-model which significantly enhance the clustering formation process, better convergence rate and optimized self-organization by size of neighborhood updated. The simulations shows that our algorithm outperforms from other clustering based protocol with best convergence rate and formed evenly distributed clusters.