Internet-Based Indoor Navigation Services

Abstract

The advances of smartphone technology in recent years is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based technologies in urban environments. IIN services rely on geolocation databases that store indoor models comprising of floor-maps and pointsof-interest along with wireless, light and magnetic signals used to localize users. Developing IIN services creates a new spectrum of information management challenges ranging from crowdsourcing indoor models, acquiring and fusing big-data velocity signals, localization algorithms, location privacy of custodians and others. In this work, we present the current landscape of academic and industrial IIN services using a multi-dimensional taxonomy of emerging topics in this domain, including location, crowdsourcing, privacy and modeling. We present the dimensions of our taxonomy through the lens of an open, modular, extensible and scalable IIN architecture, coined Anyplace, concluding with open challenges.

Image and WLAN Bimodal Integration for Indoor User Localization

“…WLAN has been a very popular technology for indoor location determination, mainly due to the ease of col- lecting and fingerprinting signal strength measurements with wireless mobile devices from the ubiquitous WLAN infrastructure inside buildings; see [18] and references therein for survey of recent advances. Many localization solutions complement WLAN with inertial sensors (i.e., accelerometer, gyroscope, magnetometer, barometer) and floorplan maps, like the Anyplace indoor navigation service [19], or further augment it with ambient light and sound signals as in SurroundSense [20]. This has been stirred with availability of sensor-rich mobile devices.…”

ACCES: Offline Accuracy Estimation for Fingerprint-Based Localization

“…We have implemented a prototype of ACCES in the popular and open-source Indoor localization architecture coined Anyplace [2]. In this section we summarize our developments by providing further details on the internal operation of our developments.…”

“…The third component is the Anyplace IIN service [2], which allows to interact with users for creation of venues, uploading floor plans, fingerprint mapping, FM analysis and localization. The Anyplace software stack consists of five main modules, including the Server, the Data Store, the Architect, the Viewer and two client applications running on Android smartphones, namely the Logger and the Navigator.…”

“…A user can then compare, using known localization algorithms, its currently observed signal fingerprint against the FM in order to find the best match. For example, our in-house Anyplace 1 Internetbased Indoor Navigation Service [2] takes an advantage of crowdsourcing-based fingerprint collection and achieves superb accuracy.…”

Towards Robust Methods for Indoor Localization using Interval Data

“…The core technology for IIN services is undoubtedly the type of hardware enabling the localization process. The taxonomy proposed in [8] focuses on whether new specialized hardware is needed to offer the location primitive, as opposed to either exploiting existing infrastructure (e.g., Wi-Fi network for the purpose of wireless connectivity) or no infrastructure whatsoever (e.g., inertial sensor systems). Infrastructure-free techniques exploit location-dependent measurements from existing wireless communication infrastructures, such as Wi-Fi access points and cellular base stations, without intervening on these systems.…”