Elekspot: A Platform for Urban Place Recognition via Crowdsourcing

The proliferation of Wi-Fi infrastructures has facilitated numerous indoor localization techniques using Wi-Fi location fingerprints. They make it possible to identify a room or a place in urban environment, which is especially important in enabling many interesting location-based services. As there are too many rooms and places such as cafes and restaurants to be recognized in urban environment, the crowdsourcing approach has been proposed to collect Wi-Fi location fingerprints based on user participation. However, its actual deployment in a large-scale urban environment presents numerous design and implementation challenges due to urban characteristics such as a large crowd, dense region, and device diversity. This paper presents Elekspot system, whose design goal is to support system scalability, device heterogeneity, robustness against lack of contributions, and localization accuracy. Through several experiments and implementation of actual applications targeting urban places we confirmed that the architecture and methods of Elekspot can effectively meet the design goals.