A Fine-grained Indoor Location-based Social Network

TL;DR

This paper introduces CheckInside, an indoor location-based social network system that significantly improves location accuracy and venue coverage indoors by leveraging crowd-sensed data and semantic fingerprints, outperforming existing LBSNs.

Contribution

CheckInside presents a novel approach combining crowd-sensed data, semantic fingerprints, and algorithms for detecting fake check-ins and expanding venue coverage indoors.

Findings

Achieves 99% accuracy in inferring user locations within top five venues

Increases venue coverage of existing LBSNs by over 37%

Demonstrates effectiveness in four mall environments over six weeks

Abstract

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%.