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  • 标题:K-Means Clustering in WSN with Koheneon SOM and Conscience Function
  • 本地全文:下载
  • 作者:Asia Bataineh ; Mohammad Samkari ; Abdualla Abdualla
  • 期刊名称:Modern Applied Science
  • 印刷版ISSN:1913-1844
  • 电子版ISSN:1913-1852
  • 出版年度:2019
  • 卷号:13
  • 期号:8
  • 页码:63
  • DOI:10.5539/mas.v13n8p63
  • 语种:English
  • 出版社:Canadian Center of Science and Education
  • 摘要:Wireless Sensor Networks (WSNs) are broadly utilized in the recent years to monitor dynamic environments which vary in a rapid way over time. The most used technique is the clustering one, such as Kohenon Self Organizing Map(KSOM) and K means. This paper introduces a hybrid clustering technique that represents the use of K means clustering algorithm with the KSOM with conscience function of Neural Networks and applies it on a certain WSN in order to measure and evaluate its performance in terms of both energy and lifetime criteria. The application of this algorithm in a WSN is performed using the MATLAB software program. Results demonstrate that the application of K-means clustering algorithm with KSOM algorithm enhanced the performance of the WSN which depends on using KSOM algorithm only in which it offers an enhancement of 11.11% and 3.33% in terms of network average lifetime and consumed energy, respectively. The comparison among the current work and a previous one demonstrated the effectiveness of the proposed approach in this work in terms of reducing the energy consumption.
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