SNOT-WiFi: Sensor network-optimized training for wireless fingerprinting

Ubiquitous computing and contextual services have growing importance in nowadays literature due to the current availability of smart personal devices like smartphones. It is becoming increasingly important to have a reliable way to localize users in any possible scenario. In outdoor it's possible to use GNSS based systems like GPS or GLONASS, but for indoor scenarios a low-energy and affordable positioning system is still under heavy research. In this setting, wireless-based techniques like WiFi fingerprinting allow exploiting of currently available infrastructures to allow reasonable positioning accuracy while keeping power consumption at a minimum. WiFi fingerprinting systems operate in two phases: one training phase, in which signals are collected with regards to different spots in the interesting area, and one usage or tracking phase in which recorded data are used to localize users in space. The usage phase is effective and reliable, but the training phase can be very time consuming for large areas and it has to be repeated over time in order to maintain localization accuracy, in case of network structure changes or to adapt to environmental changes (like humidity, pressure or temperature). In this paper we propose a novel framework which makes use of an appropriate Wireless Sensor Network (WSN) which allows continuous training over time, in order to achieve real-time updating of the fingerprints database with no human intervention.