Sensor Node Position Estimation using Neural Network for Wireless Sensor Networks
Ajay Kumar1, V. K. Jain2, P. P. Bhattacharya3
1Ajay Kumar, Department of CSE, SET, MUST, Lakshmangarh, Sikar (Rajasthan), India.
2V. K. Jain, Department of CSE, SET, MUST, Lakshmangarh, Sikar (Rajasthan), India.
3P.P. Bhattacharya, Department of ECE, SET, MUST, Lakshmangarh, Sikar (Rajasthan), India.
Manuscript received on 28 March 2019 | Revised Manuscript received on 07 April 2019 | Manuscript Published on 11 April 2019 | PP: 179-183 | Volume-8 Issue-4C, April 2019 | Retrieval Number: D24410484C19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Applications such as smart home, environmental monitoring, smart traffic management etc. are made possible with the help of Wireless Sensor Networks and other recent technologies. Identification and communication setup is very important task, especially in Internet of Things or Internet of Everything, among the different sensor nodes in the network and other mobile devices. Designing a efficient, reliable, scalable and cost effective localization process is required for the effective communication. Range free node estimation technique named Centroid algorithm is used to obtain the estimated positions to improve the precision. Other neural network-based technique is also used to improve the location precision for the sensor nodes. The neural network-based methods like radial basis, feed forward and recurrent network has been used to improve the accuracy of node position. A comparison of all the techniques has been done and it has been discovered that the neural network-based processes are better and provides result with higher accuracy.
Keywords: Centroid Algorithm, Internet of Things, Neural Network, Wireless Sensor Networks.
Scope of the Article: Wireless ad hoc & Sensor Networks