Automated Linear Regression Tools Improve RSSI WSN Localization in Multipath Indoor Environment
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 paper, the authors 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, they introduce a new method to provide bounds for the range, thereby further improving the accuracy of their simple and fast 2D localization algorithm based on corrected distance circles.