Ricerc@Sapienza

Virtual fingerprints have been proposed in the context of WiFi Fingerprinting Indoor Positioning systems in order to reduce the effort dedicated to offline measurements. In this work, the use of Multi-Wall Multi-Floor indoor propagation models to generate such virtual fingerprints is investigated. A strategy taking into account the impact of user/device orientation on the signal propagation is proposed, leading to the creation of virtual and oriented fingerprints.

Ultra-wideband (UWB) and WiFi technologies have been widely proposed for the implementation of accurate and scalable indoor positioning systems (IPSs). Among different approaches, fingerprinting appears particularly suitable for WiFi IPSs and was also proposed for UWB IPSs, in order to cope with the decrease in accuracy of time of arrival (ToA)-based lateration schemes in the case of severe multipath and non-line-of-sight (NLoS) environments. However, so far, the two technologies have been analyzed under very different assumptions, and no fair performance comparison has been carried out.

Seamless location awareness is considered a cornerstone in the successful deployment of the Internet of Things (IoT). Support for IoT devices in indoor positioning platforms and, vice versa, availability of indoor positioning functions in IoT platforms, are however still in their early stages, posing a significant challenge in the study and research of the interaction of indoor positioning and IoT.

Indoor localization of wireless mobile devices, also referred to as indoor positioning, is nowadays an intensively investigated research topic, toward the extension of outdoor location-based services to indoor environments. Among the available communication technologies and infrastructures, Wi-Fi appears as an excellent localization support, since it is largely widespread in indoor environments, implying low implementation time and costs; in turn, fingerprinting is one of the most investigated techniques for the implementation of Wi-Fi indoor positioning systems (IPSs).

Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available.