Title: Data estimation with predictive switching mechanism in wireless sensor networks

Authors: Jyotirmoy Karjee; Martin Kleinsteuber

Addresses: Embedded Systems and Robotics, TCS Research and Innovation, Bangalore 560066, India ' Department of Electrical Engineering and Information Technology, Technische Universität München, München 80333, Germany

Abstract: In wireless sensor networks (WSN), sink node needs to store and track data efficiently in a timely manner. As data is stored in a regular time interval, massive data is being collected which results in poor data tracking at sink node. To overcome this problem, an online data tracking and estimation (ODTE) algorithm is developed. ODTE algorithm captures continuous data and does online data tracking at regular time interval. It also measures distortion factor (DF) which estimates an optimal data collected at sink node. Moreover, data transmission overhead is another major issue at sink node while transmitting continuous data from sensor nodes. To solve this, we develop data prediction systems (DPS) which allows sensor nodes to transmit data to a sink node within a limit, controlled by a switching operation. This make sink node for predicting data for sensor nodes thereby reducing transmission overhead in WSN.We validate ODTE algorithm and prediction models using simulation results.

Keywords: WSNs; wireless sensor networks; spatial correlation; online tracking; estimation; data prediction.

DOI: 10.1504/IJSNET.2017.087709

International Journal of Sensor Networks, 2017 Vol.25 No.3, pp.184 - 197

Received: 21 Sep 2015
Accepted: 12 Jul 2016

Published online: 27 Oct 2017 *

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