K-Means Clustering in Wireless Sensor Networks

Sasikumar, P; Хара, Сибарам

doi:10.1109/cicn.2012.136

Cited by 153 publications

(98 citation statements)

References 11 publications

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“…A Turing pattern is formed through reaction-diffusion equations (Eqs. (12) and (13)). Each node in the network contains two factors, activator a and inhibitor h, and these values change over time according to the following differential equations:…”

Section: Bio-inspired Methods Based On Reaction-diffusion Equationsmentioning

confidence: 99%

Power Consumption Characteristics of Autonomous Decentralized Clustering Based on Local Interaction

Hamamoto

Takano

Ishida

et al. 2014

IEICE Trans. Inf. & Syst.

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Ryo HAMAMOTO†a) , Student Member, Chisa TAKANO †b) , Kenji ISHIDA †c) , and Masaki AIDA † †d) , Members SUMMARYMobile ad hoc networks (MANETs) consist of mobile terminals that directly connect with one another to communicate without a network infrastructure, such as base stations and/or access points of wireless local area networks (LANs) connected to wired backbone networks. Large-scale disasters such as tsunamis and earthquakes can cause serious damage to life, property as well as any network infrastructure. However, MANETs can function even after severe disasters have destroyed regular network infrastructure. We have proposed an autonomous decentralized structure formation technology based on local interaction, and have applied it to implement autonomous decentralized clustering on MANETs. This method is known to configure clusters that reflect the network condition, such as residual battery power and the degree of each node. However, the effect of clusters that reflect the network condition has not been evaluated. In this study, we configure clusters using our method, the back-diffusion method, and a bio-inspired method, which is a kind of autonomous decentralized clustering that cannot reflect the network condition. We also clarify the importance of clustering that reflects the network condition, with regard to power consumption and data transfer efficiency.

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Section: Bio-inspired Methods Based On Reaction-diffusion Equationsmentioning

confidence: 99%

Power Consumption Characteristics of Autonomous Decentralized Clustering Based on Local Interaction

Hamamoto

Takano

Ishida

et al. 2014

IEICE Trans. Inf. & Syst.

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“…Depending on the number of members present in every cluster, every member will get a TDMA slot at regular interval as shown in Figure 2. In [7] authors have proposed …”

Section: Related Workmentioning

confidence: 99%

Improving Energy Estimation based Clustering with Energy Threshold for Wireless Sensor Networks

Raval¹,

Bhavsar²

2015

IJCA

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In this paper an energy usage estimation technique (LCEFCM) has been proposed which employs the Fuzzy C-Means clustering for creating clusters in the Wireless Sensor Networks. LCEFCM reduces the energy consumption considerably compared to other clustering methods like simulated annealing and K-Means clustering. It applies the dynamic clustering mechanism combined with balanced clustering method. LCEFCM outperforms LEACHC, LEACHC Estimate(LCE) and LCEKMeans for various performance measuring factors like network lifetime, data received, alive nodes etc.

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“…Many clustering algorithms have also been proposed in the past in various contexts. In kmean clustering algorithm [7], authors proposed two types of clustering: Centralized k-mean clustering and Distributed k-mean clustering. These algorithms use Euclidian distances and energies for choosing cluster head.…”

Section: Related Workmentioning

confidence: 99%