TY - JOUR
T1 - Dynamic clustering method based on power demand and information volume for intelligent and green IoT
AU - Mukherjee, Amrit
AU - Goswami, Pratik
AU - Yang, Lixia
AU - Yan, Ziwei
AU - Daneshmand, Mahmoud
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/2/15
Y1 - 2020/2/15
N2 - 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.
AB - 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.
KW - Copula theory
KW - Dynamic clustering
KW - Information
KW - Internet of things (IoT)
KW - Power demand
UR - http://www.scopus.com/inward/record.url?scp=85078165176&partnerID=8YFLogxK
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U2 - 10.1016/j.comcom.2020.01.026
DO - 10.1016/j.comcom.2020.01.026
M3 - Review article
AN - SCOPUS:85078165176
SN - 0140-3664
VL - 152
SP - 119
EP - 125
JO - Computer Communications
JF - Computer Communications
ER -