Dynamic clustering method based on power demand and information volume for intelligent and green IoT

Amrit Mukherjee, Pratik Goswami, Lixia Yang, Ziwei Yan, Mahmoud Daneshmand

Research output: Contribution to journalReview articlepeer-review

24 Scopus citations

Abstract

The Internet of Things (IoT) is an integrated part of the future network which requires comprehensive awareness, reliable delivery and intelligent processing. Therefore, it faces the challenge of energy efficiency during real-time implementations based on respective applications. The work proposes an efficient and green dynamic clustering mechanism based on power demand and information volume. The machine learning approach is based on dynamic cluster formation by avoiding any information loss. Initially, among all the available Poisson distributed nodes, the master node divides them into two temporary clusters based on their power requirements. Then, the power requirements within these two clusters are equalized by a set threshold and based on application requirements, the dynamic clusters are formed by measuring the amount of information in each cluster. The simulation results presents that the proposed method equalizes the power demand of the network and maximizes the information in the cluster, which further improves the energy efficiency of the whole network. The results are also compared with traditional methods to justify the proposed work.

Original languageEnglish
Pages (from-to)119-125
Number of pages7
JournalComputer Communications
Volume152
DOIs
StatePublished - 15 Feb 2020

Keywords

  • Copula theory
  • Dynamic clustering
  • Information
  • Internet of things (IoT)
  • Power demand

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