Piotr Sapiezynski
ABSTRACT
Knowing the physical location of a mobile device is crucial for a number of context-aware applications. This information is usually obtained using the Global Positioning System (GPS), or by calculating the position based on proximity of WiFi access points with known location (where the position of the access points is stored in a database at a central server). To date, most of the research regarding the creation of such a database has investigated datasets collected both artificially and over short periods of time (e.g., during a one-day drive around a city). In contrast, most in-use databases are collected by mobile devices automatically, and are maintained by large mobile OS providers.
As a result, the research community has a poor understanding of the challenges in creating and using large-scale WiFi localization databases. We address this situation using the deployment of over 800 mobile devices to real users over a 1.5 year period. Each device periodically records WiFi scans and its GPS coordinates, reporting the collected data to us. We identify a number of challenges in using such data to build a WiFi localization database (e.g., mobility of access points), and introduce techniques to mitigate them. We also explore the level of coverage needed to accurately estimate a user's location, showing that only a small subset of the database is needed to achieve high accuracy.
- N. Aharony, W. Pan, C. Ip, I. Khayal, and A. Pentland. Social fMRI: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing, 7(6):643--659, Dec. 2011. Google Scholar
Digital Library
- Apple. Apple Q&A on Location Data. http://goo.gl/dkPCAZ.Google Scholar
- P. Bahl and V. N. Padmanabhan. RADAR: An in-building RF-based user location and tracking system. In INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, volume 2, pages 775--784. Ieee, 2000.Google ScholarCross Ref
- Y.-C. Cheng, Y. Chawathe, A. LaMarca, and J. Krumm. Accuracy Characterization for Metropolitan-scale Wi-Fi Localization. In Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, MobiSys '05, pages 233--245, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
- K. Chintalapudi, A. Padmanabha Iyer, and V. N. Padmanabhan. Indoor localization without the pain. In Proceedings of the sixteenth annual international conference on Mobile computing and networking, pages 173--184. ACM, 2010. Google ScholarDigital Library
- S. Duncan, T. I. Stewart, M. Oliver, S. Mavoa, D. MacRae, H. M. Badland, and M. J. Duncan. Portable global positioning system receivers: static validity and environmental conditions. American journal of preventive medicine, 44(2):e19--e29, 2013.Google Scholar
- A. Eustace. WiFi data collection: An update. http://goo.gl/VFJ9mM.Google Scholar
- Google. Android Developer Reference: Location. http://goo.gl/21VB7P.Google Scholar
- A. Haeberlen, E. Flannery, A. M. Ladd, A. Rudys, D. S. Wallach, and L. E. Kavraki. Practical robust localization over large-scale 802.11 wireless networks. In Proceedings of the 10th Annual International Conference on Mobile Computing and Networking, MobiCom '04, pages 70--84, New York, NY, USA, 2004. ACM. Google ScholarDigital Library
- A. Hammad and P. Faith. Location based authentication, Oct. 24 2008. US Patent App. 12/258,322.Google Scholar
- D. Han, D. G. Andersen, M. Kaminsky, K. Papagiannaki, and S. Seshan. Access point localization using local signal strength gradient. In Passive and Active Network Measurement, pages 99--108. Springer, 2009. Google ScholarDigital Library
- M. Hata. Empirical formula for propagation loss in land mobile radio services. Vehicular Technology, IEEE Transactions on, 29(3):317--325, 1980.Google Scholar
- M. Hidayab, A. H. Ali, and K. B. A. Azmi. Wifi signal propagation at 2.4 GHz. In Microwave Conference, 2009. APMC 2009. Asia Pacific, pages 528--531. IEEE, 2009.Google ScholarCross Ref
- S. Kumar, S. Gil, D. Katabi, and D. Rus. Accurate indoor localization with zero start-up cost. In Proceedings of the 20th annual international conference on Mobile computing and networking, pages 483--494. ACM, 2014. Google ScholarDigital Library
- H. Lim, L.-C. Kung, J. C. Hou, and H. Luo. Zero-configuration, robust indoor localization: Theory and experimentation. In Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM'06), 2006.Google ScholarCross Ref
- J.-H. Lin and J. S. Vitter. Approximation algorithms for geometric median problems. Information Processing Letters, 44(5):245--249, 1992. Google ScholarDigital Library
- J. Lindqvist, T. Aura, G. Danezis, T. Koponen, A. Myllyniemi, J. MĠki, and M. Roe. Privacy-preserving 802.11 access-point discovery. In Proceedings of the second ACM conference on Wireless Network Security (WiSec'09), 2009. Google ScholarDigital Library
- B. Meyerson. Aol introduces location plug-in for instant messaging so users can see where buddies are. http://goo.gl/2W1uYh.Google Scholar
- Microsoft. Location and my privacy faq. http://goo.gl/vvaZwZ.Google Scholar
- N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location-support system. In Proceedings of the 6th annual international conference on Mobile computing and networking, pages 32--43. ACM, 2000. Google ScholarDigital Library
- A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen. Zee: Zero-effort crowdsourcing for indoor localization. In Proceedings of the 18th annual international conference on Mobile computing and networking, pages 293--304. ACM, 2012. Google ScholarDigital Library
- P. Sapiezynski, A. Stopczynski, R. Gatej, and S. Lehmann. Tracking Human Mobility Using WiFi Signals. PLoS ONE, 10(7), 07 2015.Google Scholar
- A. Stopczynski, V. Sekara, P. Sapiezynski, A. Cuttone, M. M. Madsen, J. E. Larsen, and S. Lehmann. Measuring large-scale social networks with high resolution. PLoS ONE, 9(4), 04 2014.Google ScholarCross Ref
- P. Tao, A. Rudys, A. M. Ladd, and D. S. Wallach. Wireless LAN Location-sensing for Security Applications. In Proceedings of the 2nd ACM Workshop on Wireless Security, WiSe '03, pages 11--20, New York, NY, USA, 2003. ACM. Google ScholarDigital Library
- Wikipedia. Ieee 802.11. http://goo.gl/molLPd.Google Scholar
- D. Wu, Q. Liu, Y. Zhang, J. McCann, A. Regan, and N. Venkatasubramanian. CrowdWiFi: efficient crowdsensing of roadside WiFi networks. In Proceedings of the 15th International Middleware Conference, pages 229--240. ACM, 2014. Google ScholarDigital Library
- S. Yang, P. Dessai, M. Verma, and M. Gerla. Freeloc: Calibration-free crowdsourced indoor localization. In INFOCOM, 2013 Proceedings IEEE, pages 2481--2489. IEEE, 2013.Google ScholarCross Ref
- Z. Zhang, X. Zhou, W. Zhang, Y. Zhang, G. Wang, B. Y. Zhao, and H. Zheng. I am the antenna: Accurate outdoor AP location using smartphones. In Proceedings of the 17th annual international conference on Mobile computing and networking, pages 109--120. ACM, 2011. Google ScholarDigital Library
Index Terms
- Opportunities and Challenges in Crowdsourced Wardriving
Recommendations
How to Mitigate Signal Dragging during Wardriving
In wardriving, test devices in moving vehicles measure received signal strengths (RSSs) from nearby base stations (BSs) and record them with location information, which is a key component in localization technologies. In this article, the authors ...
Challenges and opportunities to support learning with mobile devices
MexIHC '10: Proceedings of the 3rd Mexican Workshop on Human Computer InteractionThe convergence of mobile and computing technologies have revolutionized the current technology and human interaction environments through the use of portable devices that offer the possibility to access information and communication services in a ...
M2M Challenges and Opportunities in 4G
4G technology includes a profile for M2M communications in order to improve the efficiency of the foreseeable connection of hundreds of devices to the broadband wireless networks. These devices impose different requirements to those of human ...
Comments