research-article

CheckInside: a fine-grained indoor location-based social network

Authors:

Moustafa Elhamshary,

Moustafa YoussefAuthors Info & Claims

UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 607 - 618

https://doi.org/10.1145/2632048.2632051

Published: 13 September 2014 Publication History

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%.

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Cited By

Elbakly RYoussef M(2022)Robust Low-Overhead RF-Based Localization for Realistic EnvironmentsIEEE Transactions on Mobile Computing10.1109/TMC.2020.303462021:6(2168-2179)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TMC.2020.3034620
Kaur MSalim FRen YChan JTomko MSanderson M(2020)Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor BehaviorsACM Transactions on Sensor Networks10.1145/339369216:3(1-25)Online publication date: 13-Aug-2020
https://dl.acm.org/doi/10.1145/3393692
Chen JHanrahan BCarroll J(2019)WithshareInternational Journal of Mobile Human Computer Interaction10.4018/IJMHCI.201901010311:1(40-61)Online publication date: Jan-2019
https://doi.org/10.4018/IJMHCI.2019010103
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Index Terms

CheckInside: a fine-grained indoor location-based social network
1. Applied computing
  1. Law, social and behavioral sciences

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Published In

cover image ACM Conferences

UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2014

973 pages

ISBN:9781450329682

DOI:10.1145/2632048

General Chairs:
AJ Brush
Microsoft Research
,
Adrian Friday
Lancaster University, UK
,
Program Chairs:
Julie Kientz
University of Washington
,
James Scott
Microsoft Research, UK
,
Junehwa Song
Korea Advanced Institute of Science and Technology (KAIST), KR

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 September 2014

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UbiComp '14

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UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing

September 13 - 17, 2014

Washington, Seattle

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Elbakly RYoussef M(2022)Robust Low-Overhead RF-Based Localization for Realistic EnvironmentsIEEE Transactions on Mobile Computing10.1109/TMC.2020.303462021:6(2168-2179)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TMC.2020.3034620
Kaur MSalim FRen YChan JTomko MSanderson M(2020)Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor BehaviorsACM Transactions on Sensor Networks10.1145/339369216:3(1-25)Online publication date: 13-Aug-2020
https://dl.acm.org/doi/10.1145/3393692
Chen JHanrahan BCarroll J(2019)WithshareInternational Journal of Mobile Human Computer Interaction10.4018/IJMHCI.201901010311:1(40-61)Online publication date: Jan-2019
https://doi.org/10.4018/IJMHCI.2019010103
Feng PQin DZhao MGuo RBerhane T(2019)Unsupervised Indoor Positioning System Based on Environmental SignaturesEntropy10.3390/e2103032721:3(327)Online publication date: 26-Mar-2019
https://doi.org/10.3390/e21030327
Abbas MElhamshary MRizk HTorki MYoussef M(2019)WiDeep: WiFi-based Accurate and Robust Indoor Localization System using Deep Learning2019 IEEE International Conference on Pervasive Computing and Communications (PerCom10.1109/PERCOM.2019.8767421(1-10)Online publication date: Mar-2019
https://doi.org/10.1109/PERCOM.2019.8767421
Kaur MSalim FRen YChan JTomko MSanderson MGupta RHuang PGonzalez M(2018)Shopping intent recognition and location prediction from cyber-physical activities via wi-fi logsProceedings of the 5th Conference on Systems for Built Environments10.1145/3276774.3276786(130-139)Online publication date: 7-Nov-2018
https://dl.acm.org/doi/10.1145/3276774.3276786
Shokry ATorki MYoussef MBanaei-Kashani FHoel EGüting RTamassia RXiong L(2018)DeepLocProceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3274895.3274909(339-348)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3274895.3274909
Youssef MAbdelnasser HRobertson PPuyol MGrand EBruno LBanaei-Kashani FHoel EGüting RTamassia RXiong L(2018)Enabling landmark-based accurate and robust next generation indoor LBSsProceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3274895.3274900(401-403)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3274895.3274900
Elhamshary MYoussef MUchiyama AYamaguchi HHigashino T(2018)CrowdMeter: Congestion Level Estimation in Railway Stations Using Smartphones2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM.2018.8444602(1-12)Online publication date: Mar-2018
https://doi.org/10.1109/PERCOM.2018.8444602
Exler ABraith MMincheva KSchankin ABeigl M(2018)Smartphone-Based Estimation of a User Being in Company or Alone Based on Place, Time, and ActivityMobile Computing, Applications, and Services10.1007/978-3-319-90740-6_5(74-89)Online publication date: 6-May-2018
https://doi.org/10.1007/978-3-319-90740-6_5
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