Locating in Crowdsourcing-Based DataSpace: Wireless Indoor Localization without Special Devices

Locating a target in an indoor social environment with a Mobile Network is important and difficult for location-based applications and services such as targeted advertisements, geosocial networking and emergency services. A number of radio-based solutions have been proposed. However, these solutions, more or less, require a special infrastructure or extensive pre-training of a site survey. Since people habitually carry their mobile devices with them, the opportunity using a large amount of crowd-sourced data on human behavior to design an indoor localization system is rapidly advancing. In this study, we first confirm the existence of crowd behavior and the fact that it can be recognized using location-based wireless mobility information. On this basis, we design “Locating in Crowdsourcing-based DataSpace” (LiCS) algorithm, which is based on sensing and analyzing wireless information. The process of LiCS is crowdsourcing-based. We implement the prototype system of LiCS. Experimental results show that LiCS achieves comparable location accuracy to previous approaches even without any special hardware.