6/20/2023 0 Comments Rssi values wifiTools used: Jupyter Notebooks and Docker, Xamp. Timestamp: UNIX Time when the capture was taken. PhoneID: Android device identifier (see below). RelativePosition: Relative position with respect to the Space (1 - Inside, 2 - Outside in Front of the door). SpaceID: Internal ID number to identify the Space (office, corridor, classroom) where the capture was taken. Measures were taken in three different buildings. Integer values from 0 to 4.īuildingID: ID to identify the building. Censored data: Positive value 100 used if WAP was not detected. Negative integer values from -104 to 0 and +100. AP will be the acronym used for rest of this notebook. WAP001-WAP520: Intensity value for Wireless Access Point (AP). In this project, we design, implement and evaluate machine learning algorithms for WLAN fingerprint-based localization. In the positioning phase, when a user reports the RSSI measurements for the multiple APs, the fit algorithm predicts the user position.Ī key challenge in wireless localization is that RSSI value at a given location can have large fluctuations due to Wi-Fi interference, user mobility, environmental mobility etc. This calibration data is used to train the localization algorithm. In the calibration phase, an extensive radio map is built consisting of RSSI values from multiple Wi-Fi Access Points (APs) at different knownlocations. Fingerprinting technique consists of two phases: calibration and positioning. In this project, we focus on fingerprint-based localization. WLAN-based positioning systems utilize the Wi-Fi received signal strength indicator (RSSI) value. With the widespread use of Wi-Fi communication in indoor environments, Wi-Fi or wireless local area network (WLAN) based positioning gained popularity to solve indoor localization. However, indoor localization is still an open problem mainly due to the loss of GPS signal in indoor environments. Outdoor localization problem can be solved very accurately thanks to the inclusion of GPS sensors into the mobile devices. Automatic user localization consists of estimating the position of the user (latitude, longitude and altitude) by using an electronic device, usually a mobile phone.
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