Indoor user localization using data distribution framework

摘要

Indoor localization has been a significant area of interest of many researchers from a long time. Earlier the methods used for networking restricted the researchers in the field such as Ethernet, RFID, Bluetooth, and GPS were used to pinpoint the location of the user indoors but all of them had their own drawbacks and could not succeed in maintaining high levels of accuracy. Wi-Fi signals to the contrary have been proved much beneficial as the indoor hindrances affect it less than its counterparts. Thus we use a Wi-Fi based dataset to determine the user location by analyzing the RSS (received signal strength) of multiple users within an area using machine learning. We have used methods to maximize the use of the attributes in the system thus increasing the level of accuracy.