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Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

Author(s): Vanheel Frank | Verhaevert Jo | Laermans Eric | Moerman Ingrid | Demeester Piet

Journal: EURASIP Journal on Wireless Communications and Networking
ISSN 1687-1472

Volume: 2011;
Issue: 1;
Start page: 38;
Date: 2011;
Original page

Keywords: algorithm design and analysis | correlation and regression analysis | wireless sensor networks | localization

Abstract Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure.
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