An Efficient Fuzzy C-Means with SAW and WPM Algorithms for the Cluster Head Selection
Anil Khandelwal1, Yogendra Kumar Jain2
1Anil Khandelwal, Department of Electronics & Communication, RGPV Bhopal (MP), India.
2Yogendra Kumar Jain, Associate Professor, Department of Electronics and Instrumentation Engineering, SATI Vidisha (MP), India.
Manuscript received on 18 February 2019 | Revised Manuscript received on 27 February 2019 | Manuscript published on 28 February 2019 | PP: 11-16 | Volume-8 Issue-3, February 2019 | Retrieval Number: C5645028319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wireless sensor network (WSN) is a type of ad hoc network self-configured and infrastructure less. This study provides the efficient approach for cluster heads (CHs) selection for achieving synchronous data sink operation. We have proposed FCM based clustering approach along with the simple additive weighting (SAW) and weighted product method (WPM) for the inner CHs selection based on the priority ranking. First the node weights were assigned based on the node operation. These values were considered for clustering. The cluster data provides the total coverage area and it shows the need of the nodes in the complete area. Then for the selection of CHs from the cluster, SAW and WPM methods have been applied. The results from the SAW and WPM provide an efficient way of inner cluster selection. The results comparison considered with the same parameters and the higher packet size. Despite of using the higher size the results from our approach is better than the traditional approaches in terms of packet delivery and energy consumption.
Keywords: Wireless Sensor Network, FCM, Simple Additive Weighting, Weighted Product Method.
Scope of the Article: WSN