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| Content Provider | ACM Digital Library |
|---|---|
| Author | Elhamshary, Moustafa Youssef, Moustafa |
| Abstract | Existing location-based social networks (LBSNs), e.g. Foursquare, depend mainly on GPS or network-based localization to infer users' locations. However, GPS is unavailable indoors and network-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 its name and semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as automatically detect new places with similar signatures. A novel algorithm for handling incorrect 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. Evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can provide the actual user location within the top five venues 99% of the time. This is compared to 17% only in the case of current LBSNs. In addition, it can increase the coverage of current LBSNs by more than 25%. |
| Starting Page | 607 |
| Ending Page | 618 |
| Page Count | 12 |
| File Format | PDF QT / MOV |
| ISBN | 9781450329682 |
| DOI | 10.1145/2632048.2632051 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-09-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Indoor location-based services Crowd-sensing Semantic floorplans |
| Content Type | Video Text |
| Resource Type | Article |
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