$Tack:$ Learning Towards Contextual and Ephemeral Indoor Localization With Crowdsourcing

At events, such as conferences, indoor localization is both contextual and ephemeral, in that localization is only needed within the context of and for the duration of the event. As such, the costs and requirements of providing such services need to be minimal. In this paper, we design, implement, and evaluate Tack, a new mobile application framework that is specifically engineered to support such contextual and ephemeral indoor localization during an event. To provide location-based services with Tack, an event organizer only needs to bring and place a small number of (reusable) beacons around the venue before the event begins. As a system framework, Tack uses a combination of known beacon locations, contacts over bluetooth low energy, crowdsourcing, and dead-reckoning to estimate and refine user locations. To make our location estimates more accurate, we embrace the inherent nature of beacons, design crowdsourcing-based inference algorithms, and present an extensive evaluation by running real-world experiments with iOS devices and beacons. Tack has been implemented as an open-source framework on the iOS platform and can be used by mobile applications designed for events with location-based services.