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Wireless Communications and Mobile Computing
Volume 2017, Article ID 1390847, 7 pages
https://doi.org/10.1155/2017/1390847
Research Article

Energy-Efficient Constant Gain Kalman Filter Based Tracking in Wireless Sensor Network

Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Athwalines, Surat 395001, India

Correspondence should be addressed to Kirti Hirpara; moc.liamg@araparihitrik and Keyur Rana; ni.ca.tecs@anar.ruyek

Received 29 July 2016; Revised 28 February 2017; Accepted 13 March 2017; Published 6 April 2017

Academic Editor: Pierre-Martin Tardif

Copyright © 2017 Kirti Hirpara and Keyur Rana. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Target tracking is one of the most widely used applications of wireless sensor network (WSN). Efficient usage of energy is a key issue in WSN application such as target tracking. Another important criterion is a tracking accuracy that can be achieved by using appropriate tracking mechanism. Because of the special characteristic of WSN, there is a trade-off between tracking accuracy and power consumption. Our aim is to improve tracking accuracy as well as provide energy-efficient solution by integrating the concept of clustering and prediction techniques. This paper presents Energy-Efficient Constant Gain Kalman Filter based Tracking (EECGKFT) algorithm to optimize the energy usage and to increase the tracking accuracy. There is also a need to collect data from network having a mobile Base Station (BS). Hence, performance of proposed algorithm is analyzed for a static BS and also for mobile BS. The results depict that proposed algorithm performs better compared to the existing algorithms in energy efficiency and prediction accuracy. Analysis of results validates that EECGKFT increases energy efficiency by reducing transmission of unnecessary data in the sensor network environment and also provides good tracking results.