Open AccessEnergy efficient clustering using the AMHC (adoptive multi-hop clustering) technique
Author(s) -
M. Vimala,
Rajeev Ranjan
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i2.pp1622-1631
Subject(s) - cluster analysis , computer science , data mining , principal component analysis , efficient energy use , wireless sensor network , cure data clustering algorithm , canopy clustering algorithm , data stream clustering , algorithm , fuzzy clustering , distributed computing , artificial intelligence , computer network , electrical engineering , engineering
IoT has gained fine attention in several field such as in industry applications, agriculture, monitoring, surveillance, similarly parallel growth has been observed in field of WSN. WSN is one of the primary component of IoT when it comes to sensing the data in various environment. Clustering is one of the basic approach in order to obtain the measurable performance in WSNs, Several algorithms of clustering aims to obtain the efficient data collection, data gathering and the routing. In this paper, a novel AMHC (Adaptive Multi-Hop Clustering) algorithm is proposed for the homogenous model, the main aim of algorithm is to obtain the higher efficiency and make it energy efficient. Our algorithm mainly contains the three stages: namely assembling, coupling and discarding. First stage involves the assembling of independent sets (maximum), second stage involves the coupling of independent sets and at last stage the superfluous nodes are discarded. Discarding superfluous nodes helps in achieving higher efficiency. Since our algorithm is a coloring algorithm, different color are used at the different stages for coloring the nodes. Afterwards our algorithm (AMHC) is compared with the existing system which is a combination of Second order data CC(Coupled Clustering) and Compressive-Projection PCA(Principal Component Analysis), and results shows that our algorithm excels in terms of several parameters such as energy efficiency, network lifetime, number of rounds performed.