Clustering methodology to prolong lifetime in wireless sensor networks

Wireless sensor network (WSN) is a low-powered network formed by the sensor nodes that finds application in civilian, military, visual sense models and many others. Improved network lifetime is an important task to be achieved by these sensor networks. The conservative routing protocols cannot be ap...

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Bibliographic Details
Published in:2017 International Conference on Information Communication and Embedded Systems (ICICES) pp. 1 - 8
Main Authors: Deepak Keynes, J. K., Punithavathani, D. Shalini
Format: Conference Proceeding
Language:English
Published: IEEE 01.02.2017
Subjects:
Algorithm design and analysis
Clustering algorithms
coverage based clustering
Data communication
distributed clustering algorithm
Energy consumption
Energy efficiency
network lifetime
Protocols
Wireless sensor network (WSN)
Wireless sensor networks
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Summary:Wireless sensor network (WSN) is a low-powered network formed by the sensor nodes that finds application in civilian, military, visual sense models and many others. Improved network lifetime is an important task to be achieved by these sensor networks. The conservative routing protocols cannot be applied here due to its battery powered nodes. To provision energy efficiency, nodes are frequently clustered in to non-overlapping clusters. This paper gives a brief overview on clustering process in wireless sensor networks. A hybrid energy efficient distributed clustering methodology for dense wireless sensor networks, the Capacity based Clustering Low Energy Adaptive Clustering Hierarchy (CC-LEACH) has been proposed and the results have been evaluated against the existing Low Energy Adaptive Clustering Hierarchy (LEACH) clustering methodology. Simulation results clearly show an excellent improvement in throughput, packet delivery ratio and number of packets received at the base station. Also, the proposed clustering methodology show a reduction in packet drop, energy consumption and end to end latency for dense wireless sensor networks.
DOI:10.1109/ICICES.2017.8070721