Design and Implementation of a Wireless System to Locate a User in Indoor Environments | ||
| Engineering and Technology Journal | ||
| Article 7, Volume 38, 11A, November 2020, Pages 1640-1651 PDF (1.34 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.30684/etj.v38i11A.1592 | ||
| Authors | ||
| Amnah A. Kareem* 1; Wisam H. Ali2; Manal H. Jasim1 | ||
| 1Department of Electrical Engineering, University of Technology, Baghdad, Iraq. | ||
| 2Department of Electrical Engineering University of Technology. Baghdad. Iraq | ||
| Abstract | ||
| The technology of indoor positioning has pulled in the consideration of researchers the expanding capability of smartphones and the advancement of sensor innovation, alongside the increase the time people spend working inside the building or being indoors. Sensor innovation, which is one of the most generally utilized information hotspots for indoor setting, has a favorable position that sensors can receive information from a cell phone without introducing any additional device. The idea of the proposed system is to use the Wi-Fi access points, inside the building, together with a Smartphone Wi-Fi sensor which lets the building administrator locate those carrying smartphones, wherever they exist inside the building. The proposed system consists of two-stage the testing stage (or preparation phase) and, the second stage is the training stage (or positioning phase). The data is collected and selected for accurate readings; a router is used, which is the Mikrotik access point type from which we can read the RSS value. The RSS value represents the Wi-Fi signal strength of the target device. The proposed IPS detection system is independent and can work in unconstrained environments. The database used to measure the performance of the proposed IPS detection system is collected from 14 locations (different in size). The number of readings obtained from the collected database is 1199 readings consist of received signal strength value from five access points. The proposed IPS accuracy is 96.8595% and the mean error is about 1.2 meters are achieved when using, K-Nearest Neighbor (K-NN), used the classifier to make a decision in the last stage of IPS. The K-NN classifier was built by FPGA Model using Xilinx system generator and implemented on Spartan-3A 700 A Kit. | ||
| Keywords | ||
| Indoor positioning; Localization; positioning; RSS; Wireless; Wi-Fi | ||
| References | ||
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