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Issue title: Special section: Soft Computing and Intelligent Systems: Techniques and Applications
Guest editors: Sabu M. Thampi, El-Sayed M. El-Alfy and Ljiljana Trajkovic
Article type: Research Article
Authors: Chandrawanshi, Veervrat Singha; * | Tripathi, Rajiv Kumarb | Pachauri, Rahulc
Affiliations: [a] Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, Guna, M.P., India | [b] Department of Electronics and Communication Engineering, National Institute of Technology, Delhi, India | [c] Department of Computer Science Engineering, Jaypee University of Engineering and Technology, Guna, M.P., India
Correspondence: [*] Corresponding author. Veervrat Singh Chandrawanshi, Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, Guna, M.P., 473226, India. E-mail: veervrat@gmail.com.
Abstract: The design of a wireless sensor network (WSN) faces many constraints. Mostly, WSN is energy constraint because the sensor nodes are battery operated. Available power expenditure in WSN largely depends on the efficient use of limited resources and appropriate routing of the data packets. The power consumption can be minimized by balancing the energy consumption between the sensor nodes and selecting the minimum power consumption route for the data packets. Clustering is one of the most effective technique that not only uniformly distributes the energy among all the sensor nodes but also play a vital role in the designing of routing protocols. So based on these advantages, a low power consumption routing protocol is proposed that makes use of fuzzy c-means++ algorithm. The proposed approach minimizes the power consumption of the sensor network by the excellent management of the WSN and also raises the lifespan. The simulation result illustrates the effectiveness of the proposed routing method when compared with the recently developed protocols based on k-means and fuzzy c-means algorithms.
Keywords: Wireless sensor networks, clustering, cluster head, k-means, k-means++, fuzzy c-means algorithm, fuzzy c-means++ algorithm, energy efficient network
DOI: 10.3233/JIFS-179736
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6561-6570, 2020
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