  Existing Location-based social networks (LBSNs), e.g., Foursquare, depend
mainly on GPS or cellular-based localization to infer users' locations.
However, GPS is unavailable indoors and cellular-based localization provides
coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor
environments, where people spend 89% of their time. This in turn affects the
user experience, in terms of the accuracy of the ranked list of venues,
especially for the small screens of mobile devices; misses business
opportunities; and leads to reduced venues coverage.
  In this paper, we present CheckInside: a system that can provide a
fine-grained indoor location-based social network. CheckInside leverages the
crowd-sensed data collected from users' mobile devices during the check-in
operation and knowledge extracted from current LBSNs to associate a place with
a logical name and a semantic fingerprint. This semantic fingerprint is used to
obtain a more accurate list of nearby places as well as to automatically detect
new places with similar signature. A novel algorithm for detecting fake
check-ins and inferring a semantically-enriched floorplan is proposed as well
as an algorithm for enhancing the system performance based on the user implicit
feedback. Furthermore, CheckInside encompasses a coverage extender module to
automatically predict names of new venues increasing the coverage of current
LBSNs.
  Experimental evaluation of CheckInside in four malls over the course of six
weeks with 20 participants shows that it can infer the actual user place within
the top five venues 99% of the time. This is compared to 17% only in the case
of current LBSNs. In addition, it increases the coverage of existing LBSNs by
more than 37%.
