The Neural Network based Technique for Fault Tolerance in Wireless Sensor Networks

Authors(2) :-Sarbjeet Singh, Dr. Sandeep Kautish

Wireless sensor network is the self-configuring network where any sensor node can join or leave the network when they want. In Wireless sensor network no central controller is present. Wireless sensor nodes are responsible for data routing in the network. Wireless Sensor nodes are very small in size and have limited resources. In such far places it is very difficult to recharge or replace the battery of the sensor nodes. In such conditions, we focus to reduce the battery consumption of the sensor nodes. In this work, a new technique is proposed to reduce battery consumption. It will be based on the dynamic clustering using neural network. Before data transmission sensor nodes form the cluster dynamically using Boltzmann learning of the neural network and weights are adjust according to the situation and it also enhance the efficiency of the dynamic clustering. Experimental results show that new proposed technique is more efficient, reliable and provide more throughput as compare to the existing technique.

Authors and Affiliations

Sarbjeet Singh
Research Scholar, University College of Computer Applications, Guru Kashi University, Talwandi Sabo, Bathinda, Punjab, India
Dr. Sandeep Kautish
Professor in Computer Science, Guru Kashi University, Talwandi Sabo, Bathinda, Punjab, India

WSN, Neural Networks, Boltzmann learning

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Publication Details

Published in : Volume 3 | Issue 5 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 169-172
Manuscript Number : IJSRSET1173446
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Sarbjeet Singh, Dr. Sandeep Kautish, " The Neural Network based Technique for Fault Tolerance in Wireless Sensor Networks, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 5, pp.169-172, July-August-2017.
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