News Updates Wednesday 21st Nov 2018 :
  • Welcome to INPRESSCO, world's leading publishers, We have served more than 10000+ authors
  • Articles are invited in engineering, science, technology, management, industrial engg, biotechnology etc.
  • Paper submission last date of Nov/Dec 2018 extended to 20 Nov 2018, Submit online or at editor.ijcet@inpressco.com
  • Our journals are indexed in NAAS, University of Regensburg Germany, Google Scholar, Cross Ref etc.
  • DOI is given to all articles

An Energy Threshold based WSN Clustering Schema using PAM Algorithm


Author : Santokh Singh and Gagandeep Singh

Pages : 434-438
Download PDF
Abstract

Wireless sensor network consists of number of sensor nodes clustered together to collect various environmental or other physiological data like temperature, pressure, seismic graph, sound, location, etc. WSNs are used in various fields like healthcare, monitoring systems, military, etc. Sensor nodes run on battery without direct power supply. Hence, energy efficiency becomes the major issue in WSNs to make WSNs run for longer. Because these wireless sensor nodes run on batteries and they carry a limited battery life. Routing process and Sensing process consumes the battery power. Sensing process is the primary process of sensor nodes, hence, cannot make changes in this process. Routing process is the secondary process of the nodes, and is having a great possibility of improvements. In the existing approach, k-mean was used for the clustering algorithm for the cluster head selection. In this paper, we have proposed an effective and efficient cluster head selection method using k-Medoids to solve the stated problem, especially for large WSNs consisting of thousands or millions of nodes. k-Medoids is more efficient and correct than k-Means for large clusters. Both of K-Means and k-Medoids utilize expectation maximization (EM) strategy to converge to a minimum error condition. While k-Medoids require the cluster centers to be centroids, in k-Means the centers could be anywhere in the sample space. k-Medoid is more robust to outliners than k-Means therefore results in more quality clustering. It is also computationally more complex. Computer simulation will be performed in the NS2 environment and the proposed approach will be compared with LEAH and HEED.

Keywords: WSN, PAM etc.

Article published in International Journal of Current Engineering and Technology, Vol.5, No.1 (Feb-2015)

 

Call for Papers
  1. IJCET- Nov/Dec 2018 Issue

    Submission Last Date
    20 Nov
  2. DOI is given to all articles
  3. Current Issue
  4. IJTT-Dec-2018
  5. IJAIE-Dec-2018
  6. IJCSB-Dec-2018
  • Inpressco Google Scholar
  • Inpressco Science Central
  • Inpressco Global impact factor
  • Inpressco aap

International Press corporation is licensed under a Creative Commons Attribution-Non Commercial NoDerivs 3.0 Unported License
©2010-2018 INPRESSCO® All Rights Reserved