Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Clustering Issues and Literature Review of Distributed Clustering Mechanisms in Wireless Sensor Networkswith its Real-World Applications

Author : J.K.Deepak Keynes 1 Dr.D.Shalini Punithavathani 2

Date of Publication :7th December 2016

Abstract: To diminish the data transmission time and energy consumption, the sensor nodes are assembled into a number of little groups referred as clusters and the phenomenon is referred as clustering. Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. In this paper, clustering issues and literature review of distributed clustering mechanisms in wireless sensor networks with its real-world applications has been elaborated. A literaturereview of different distributed clustering algorithms used in WSNs, based on few metrics such as cluster count, cluster stability, cluster head mobility, cluster head role, clustering objective and cluster head selection is carried out. The research work concludes with comparison of few distributed clustering algorithms in WSNs based on these metrics.

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