Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2674377.2674386acmconferencesArticle/Chapter ViewAbstractPublication Pagesacm-devConference Proceedingsconference-collections
research-article

PlaceMap: Discovering Human Places of Interest Using Low-Energy Location Interfaces on Mobile Phones

Published: 05 December 2014 Publication History

Abstract

Emerging class of context-aware mobile applications, such as Google Now and Foursquare require continuous location sensing to deliver different location-aware services. Existing research, in finding location at higher abstraction, use GPS and WiFi location interfaces to discover places, which result in high power consumption. These interfaces are also not available on all feature phones that are in majority in developing countries.
In this paper, we present a framework PlaceMap that discovers different places and routes, solely using GSM information, i.e., Cell ID. PlaceMap stores and manages all the discovered places and routes, which are used to build spatio-temporal mobility profiles for the users. PlaceMap provides algorithms that can complement GSM-based place discovery with an initial WiFi-based training to increase accuracy. We performed a comprehensive offline evaluation of PlaceMap algorithms on two large real-world diverse datasets, self-collected dataset of 62 participants for 4 weeks in India and MDC dataset of 38 participants for 45 weeks in Switzerland. We found that PlaceMap is able to discover up to 81% of the places correctly as compared to GPS. To corroborate the potential of PlaceMap in real-world, we deployed a life-logging application for a small set of 18 participants and observed similar place discovery accuracy.

References

[1]
LifeMap Google Play Application, https://play.google.com/store/apps/details?id=com.mobed.lifemap
[2]
PlaceMap Google Play Application, https://play.google.com/store/apps/details?id=com.iiitd.muc.placemap
[3]
Moves Google Play Application, https://play.google. com/store/apps/details?id=com.protogeo.moves
[4]
MIT Reality Mining Dataset, http://realitycommons. media.mit.edu/realitymining.html
[5]
Open Cell ID Database, www.opencellid.org
[6]
Barabi A., Understanding individual human mobility patterns," Nature 453, 779--782.
[7]
Mun, Min, et al. "PEIR, the personal environmental impact report, as a platform for participatory sensing systems research." ACM MobiSys'09.
[8]
Khan, A. J., Subbaraju, V., Misra, A., & Seshan, S. Mitigating the true cost of advertisement-supported free mobile applications. ACM HotMobile'12.
[9]
Ludford, P. J., et al. Because I carry my cell phone anyway: functional location-based reminder applications. ACM CHI 2006.
[10]
Chon, Yohan, Wanchang Ryu, and Hojung Cha. "Predicting smartphone battery usage using cell tower ID monitoring." Pervasive and Mobile Computing (2013).
[11]
Jeongyeup P., Kim J., and Govindan R. "Energy-efficient rate-adaptive gps-based positioning for smartphones." ACM MobiSys'10.
[12]
Kim, Donnie H., et al. "SensLoc: sensing everyday places and paths using less energy." ACM SenSys'10.
[13]
Chon, Yohan, et al. "SmartDC: Mobility Prediction-based Adaptive Duty Cycling for Everyday Location Monitoring.", IEEE Transactions on Mobile Computing, 2013.
[14]
Yadav K., Naik V., Singh A., Singh P.,Kumaraguru P., and Chandra U.,Challenges and novelties while using mobile phones as ICT devices for Indian masses: short paper, NSDR'10.
[15]
K. Yadav, A. Kumar, A. Bharti, and V.Naik . Characterizing Mobility Patterns of People in Developing Countries using Their Mobile Phone Data, COMSNETS 2014.
[16]
Bayir M.A., Demirbas M.,PRO, and Eagle N.: Discovering spatiotemporal mobility profiles of cellphone users, WOWMOM'09.
[17]
Vu L., Do Q., and Nahrstedt K., Jyotish: Constructive approach for context predictions of people movement from joint Wifi/Bluetooth trace, IEEE PerCom'11.
[18]
Ficek M., Kencl L., Spatial extension of the Reality Mining Dataset. MASS 2010.
[19]
Burbey I.E., Predicting Future Locations and Arrival Times of Individuals. Doctoral Thesis, Blacksburg, Virginia, April 2011.
[20]
Thiagarajan, A. et al. Accurate, low-energy trajectory mapping for mobile devices. USENIX NSDI 2011.
[21]
Yadav, K., Naik, V., & Singh, A. (2012). MobiShare: cloud-enabled opportunistic content sharing among mobile peers. IIIT-D Technical Report, IIITD-TR-2012-009.
[22]
Changqing Z. et al. "Discovering personally meaningful places: An interactive clustering approach." ACM Transactions on Information Systems (TOIS) 25, no. 3 (2007): 12.
[23]
Wang, H. et al. "WheelLoc: Enabling Continuous Location Service on Mobile Phone for Outdoor Scenarios.", IEEE INFOCOM'13.
[24]
Kaisen L., Kansal A., Lymberopoulos D., and Zhao F. "Energy-accuracy trade-off for continuous mobile device location." ACM MobiSys 2010.
[25]
Kang J., Welbourne W., Stewart B., and Borriello G. "Extracting places from traces of locations." ACM workshop on Wireless mobile applications and services on WLAN hotspots'04.
[26]
Laurila, Juha K. et al. "The mobile data challenge: Big data for mobile computing research." In Proceedings of the Workshop on the Nokia Mobile Data Challenge, Pervasive 2012.
[27]
Jeongyeup, Kim K, Singh J., and Govindan R. "Energy-efficient positioning for smartphones using cell-id sequence matching." ACM MobiSys 2011.
[28]
Kari L. et al. "Adaptive on-device location recognition." In Pervasive Computing, pp. 287--304.
[29]
Do, T., and Gatica-Perez D. "The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data." IEEE Transactions on Mobile Computing, 2013.
[30]
Iacopo C. et al., "Matador: Mobile Task Detector for Context-Aware Crowd-Sensing Campaigns.", PERCOM 2013
[31]
Yadav, Kuldeep, et al. "Low Energy and Sufficiently Accurate Localization for Non-Smartphones." IEEE MDM'12.

Cited By

View all
  • (2018)Towards a context-aware Wi-Fi-based Fog Node discovery scheme using cellular footprints2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMOB.2018.8589116(1-6)Online publication date: Oct-2018
  • (2014)WallahProceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.4108/icst.mobiquitous.2014.258030(188-197)Online publication date: 2-Dec-2014
  • (2014)PMWareProceedings of the Middleware Industry Track10.1145/2676727.2676730(1-7)Online publication date: 8-Dec-2014

Index Terms

  1. PlaceMap: Discovering Human Places of Interest Using Low-Energy Location Interfaces on Mobile Phones

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      ACM DEV-5 '14: Proceedings of the Fifth ACM Symposium on Computing for Development
      December 2014
      142 pages
      ISBN:9781450329361
      DOI:10.1145/2674377
      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 the author(s) 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].

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 05 December 2014

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. energy-efficient
      2. gps
      3. gsm
      4. place discovery
      5. places of interest
      6. wifi

      Qualifiers

      • Research-article

      Funding Sources

      Conference

      ACM DEV-5
      Sponsor:
      ACM DEV-5: Annual Symposium on Computing for Development
      December 5 - 6, 2014
      California, San Jose, USA

      Acceptance Rates

      ACM DEV-5 '14 Paper Acceptance Rate 11 of 37 submissions, 30%;
      Overall Acceptance Rate 52 of 164 submissions, 32%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 14 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2018)Towards a context-aware Wi-Fi-based Fog Node discovery scheme using cellular footprints2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMOB.2018.8589116(1-6)Online publication date: Oct-2018
      • (2014)WallahProceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.4108/icst.mobiquitous.2014.258030(188-197)Online publication date: 2-Dec-2014
      • (2014)PMWareProceedings of the Middleware Industry Track10.1145/2676727.2676730(1-7)Online publication date: 8-Dec-2014

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media