NDLI: An approach to improved energy efficient hybrid clustering in wireless sensor networks

Content Provider	IEEE Xplore Digital Library
Author	Patra, A. Chouhan, S.
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. & Electr. Eng., Indian Inst. of Technol., Guwahati, Guwahati, India (Patra, A.; Chouhan, S.)
Abstract	In wireless sensor networks (WSNs), hierarchical clustering approach gives an efficient solution to achieve the goal of maximizing the network lifetime by minimizing energy utilization. In this paper, we introduce a cluster head (CH) selection process and cluster formation algorithm by re-selection of CHs called Improved Energy Efficient Hybrid Clustering Scheme (IEEHCS). In the proposed scheme, energy efficient CHs are selected by a centralized algorithm based on remaining energy, node density and minimum separation distance to reduce the control message overheads. The key idea is that the CH role is repeated with the same settings or shifted to a eligible member nodes instead of re-clustering the whole network at every round. In this way, IEEHCS reduces the frequency of updating CHs, avoids unnecessary re-clustering in every round and saves significant amount of node energy. Simulation results demonstrate that IEEHCS effectively reduces the energy consumption and prolongs the network lifetime for first node death upto 45.39% and 11.36% over LEACH-C and EEHCS, respectively, in certain network settings.
Starting Page	1
Ending Page	6
File Size	238805
Page Count	6
File Format	PDF
e-ISBN	9781479946655
DOI	10.1109/SPCOM.2014.6983962
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-22
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wireless sensor network Energy consumption Wireless sensor networks Time division multiple access Network lifetime Simulation Energy dissipation Clustering algorithms Energy efficiency Steady-state Clustering Remaining energy
Content Type	Text
Resource Type	Article

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