Radio Map Prefetching for Indoor Navigation in Intermittently Connected Wi-Fi Networks

Abstract

Wi-Fi (or WLAN) based indoor navigation applications for mobiles rely on cloud-based services (s) that take care of a user's (u) localization task using structures called RadioMaps (RMs). It is imperative for u to have a stable Wi-Fi connection in order to either continuously receive location updates from s or to download RMs a priori for offline navigation. Wi-Fi networks however, suffer from intermittent connectivity due to poor network planning that results in sparse deployment of access points and effectively areas where Wi-Fi coverage cannot be guaranteed. This inherently affects the localization accuracy and therefore the navigation experience of users. In this paper, we propose an innovative framework for accurate and fast indoor localization over an intermittently connected Wi-Fi network, coined Prefetching Localization (PreLoc). In Preloc, we prioritize the download of RM records based on knowledge acquired from historic traces of other users inside the same building. Instead of downloading the complete RM from s to u, we propose a Probabilistic Group Selection (PGS) strategy, which identifies RM records that have a higher probability of being necessary to a user moving inside a target area. We have evaluated our framework using a real prototype developed in Android, as well as realistic Wi-Fi traces we collected at the University of Cyprus. Our experimental study reveals that PreLoc using PGS and conventional fingerprint-based indoor positioning algorithms can yield accuracy that is as good as using the same algorithms with a complete RM, even under scenarios of weak Wi-Fi coverage.

Internet-Based Indoor Navigation Services

“…The Navigator also uses the onboard smartphone sensors (i.e., accelerometer, gyroscope and digital compass), which are seamlessly integrated in our tracking module to smooth the Wi-Fi locations and enhance the navigation experience. We currently investigate the intelligent prefetching of Radiomaps [16] using historic analysis of trajectories. The Logger application enables users to record RSS readings from nearby Wi-Fi APs and upload them to our Server through a Web 2.0 API (in JSON).…”

The anatomy of the anyplace indoor navigation service

“…In [4] we devise the Temporal Vector Map (TVM) algorithm, where a user u camouflages its location from s, by requesting a subset of k entries from s, where k is a user-defined constant. Access the localization structures efficiently, is another complementary problem that we model and address using the Preloc framework [5]. …”