Indoor localisation based on Wi-Fi fingerprinting with fuzzy sets

Activity: SupervisionCompleted SURF Project


In an indoor environment where there is no line-of-sight signal from global positioning systems (GPSs), received signal strength (RSS) from wireless network infrastructure can be used for localisation through fingerprinting. For example, a vector of a pair of a service set identifier (SSID) and RSS for a Wi-Fi access point (AP) measured at a location becomes its location fingerprint. A position of a user/device then can be estimated by finding the closest match between its new RSS measurement and the location fingerprints in a database. A major challenge is how to deal with the random fluctuation in RSS measurements. This project investigates the use of fuzzy sets to take into account the RSS randomness in building a location fingerprint database and finding the closest match.
PeriodJun 2017Aug 2017
Degree of RecognitionLocal