[WEEK 1 — Wi-Fi Based Indoor Positioning]
Team Members: Burak Emre Ozer, Huzeyfe Kocabas
Wi-Fi Based Indoor Positioning
In this week, we’ll talk about the Wi-Fi based indoor positioning project, our motivations and also diving into the dataset.
Introduction
Global Positioning System (GPS), which uses satellites, is the most popular outdoor positioning system, however its signals can be easily blocked by various structures and factors then it becomes useless for indoor environment because of signal loss. Unlike the GPS, Indoor Positioning Systems aims to detect the position of user or device by using Access Points’ signal also called “Wi-Fi fingerprint”.
With the advancing technology and spread of wireless networks, Indoor Positioning Systems become even more important place in the fields of augmented reality, social networking, personal tracking, guiding blind people, tracking small children or elderly individuals and location-based advertising etc.
Our motivation is to create a project that can provide solutions to these areas.
Data Set
As mentioned earlier, we will use UJIIndoorLoc Data Set in our project.
UJIIndoorLoc Data Set
The UJIIndoorLoc database covers three buildings of Universitat Jaume I with 4 or more floors and almost 110.000m2. It can be used for classification, e.g. actual building and floor identification, or regression, e.g. actual longitude and latitude estimation. It was created in 2013 by means of more than 20 different users and 25 Android devices. The database consists of 19937 training/reference records (trainingData.csv file) and 1111 validation/test records (validationData.csv file).
The 529 attributes contain the WiFi fingerprint, the coordinates where it was taken, and other useful information.
Each WiFi fingerprint can be characterized by the detected Wireless Access Points (WAPs) and the corresponding Received Signal Strength Intensity (RSSI).
Then the coordinates (latitude, longitude, floor) and Building ID are provided as the attributes to be predicted.
