Ilias Leontiadis
Haewoon Kwak
David Wetherall
Konstantina Papagiannaki
ABSTRACT
Through their normal operation, cellular networks are a repository of continuous location information from their subscribed devices. Such information, however, comes at a coarse granularity both in terms of space, as well as time. For otherwise inactive devices, location information can be obtained at the granularity of the associated cellular sector, and at infrequent points in time, that are sensitive to the structure of the network itself, and the level of mobility of the device. In this paper, we are asking the question of whether such sparse information can help to identify the paths followed by mobile connected devices throughout the day. If such a task is possible, then we would not only enable continuous mobility path estimation for smartphones, but also for the millions of future connected "things".
The challenge we face is that cellular data has one to two orders of magnitude less spatial and temporal resolution than typical GPS traces. Our contribution is to devise path segmentation, de-noising, and inference procedures to estimate the device stationary location, as well as its mobility path between stationary positions. We call our technique Cell*. We complement the lack of spatio-temporal granularity with information on the cellular network topology, and GIS (Geographic Information System).
We collect more than 3,000 mobility trajectories over 8 months and show that Cell* achieves a median error of 230m for the stationary location estimation, while mobility paths are estimated with a median accuracy of 70m. We show that mobility path accuracy improves with its length and speed, and counter to our intuition, accuracy appears to improve in suburban areas. Cell* is the first technology, we are aware of, that allows location services for the new generation of connected mobile devices, that may feature no GPS, due to cost, size, or battery constraints.
- R. A. Becker, R. Caceres, K. Hanson, J. M. Loh, S. Urbanek, A. Varshavsky, and C. Volinsky. Route classification using cellular handoff patterns. In Proceedings of the 13th international conference on Ubiquitous computing, UbiComp'11, 2011. Google Scholar
Digital Library
- D. Bernstein and A. Kornhauser. An introduction to map matching for personal navigation assistants. 1998.Google Scholar
- N. Caceres, J. Wideberg, and F. Benitez. Deriving origin destination data from a mobile phone network. Intelligent Transport Systems, IET, 1(1):15--26, 2007.Google Scholar
- F. Calabrese, G. Di Lorenzo, L. Liu, and C. Ratti. Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Computing, 10(4):36--44, 2011. Google ScholarDigital Library
- S. Gambs, M.-O. Killijian, and M. N. del Prado Cortez. Next place prediction using mobility markov chains. In Proceedings of the First Workshop on Measurement, Privacy,and Mobility, page 3. ACM, 2012. Google ScholarDigital Library
- F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi. Trajectory pattern mining. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 330--339. ACM, 2007. Google ScholarDigital Library
- M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi. Understanding individual human mobility patterns. Nature, 453(7196):779--782, 2008.Google ScholarCross Ref
- O. Görnerup. Scalable mining of common routes in mobile communication network traffic data. In Proceedings of the 10th international conference on Pervasive Computing, Pervasive'12, pages 99--106, 2012. Google ScholarDigital Library
- J. S. Greenfeld. Matching gps observations to locations on a digital map. In National Research Council (US). Transportation Research Board. Meeting (81st: 2002: Washington, DC). Preprint CD-ROM, 2002.Google Scholar
- M. Haklay and P. Weber. Openstreetmap: User-generated street maps. Pervasive Computing, IEEE, 7(4):12--18, 2008. Google ScholarDigital Library
- S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, M. Martonosi, J. Rowland, and A. Varshavsky. Identifying important places in people's lives from cellular network data. In Pervasive Computing, pages 133--151. Springer, 2011. Google ScholarDigital Library
- S. Isaacman, R. Becker, R. Cáceres, M. Martonosi, J. Rowland, A. Varshavsky, and W. Willinger. Human mobility modeling at metropolitan scales. In Proceedings of the 10th international conference on Mobile systems, applications, and services, MobiSys'12, pages 239--252, 2012. Google ScholarDigital Library
- S. Kim and J.-H. Kim. Adaptive fuzzy-network-based c-measure map-matching algorithm for car navigation system. Industrial Electronics, IEEE Transactions on, 48(2):432--441, 2001.Google Scholar
- A. Kirmse, T. Udeshi, P. Bellver, and J. Shuma. Extracting patterns from location history. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '11, pages 397--400, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- W. Y. Ochieng, M. Quddus, and R. B. Noland. Map-matching in complex urban road networks. Revista Brasileira de Cartografia, 2(55), 2004.Google Scholar
- N. J. N. P. E. Hart and B. Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems, Science, and Cybernetics, SSC-4(2):100--107, 1968.Google ScholarCross Ref
- J. Paek, K.-H. Kim, J. P. Singh, and R. Govindan. Energy-efficient positioning for smartphones using cell-id sequence matching. In Proceedings of the 9th international conference on Mobile systems, applications, and services, pages 293--306. ACM, 2011. Google ScholarDigital Library
- M. A. Quddus, W. Y. Ochieng, and R. B. Noland. Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies, 15(5):312--328, 2007.Google ScholarCross Ref
- M. Saravanan, S. Pravinth, and P. Holla. Route detection and mobility based clustering. In Proceedings of IEEE 5th International Conference on Internet Multimedia Systems Architecture and Application (IMSAA), pages 1--7, 2011.Google ScholarCross Ref
- J. Schlaich, T. Otterstätter, and M. Friedrich. Generating trajectories from mobile phone data. In Proceedings of the 89th Annual Meeting Compendium of Papers, Transportation Research Board of the National Academies, 2010.Google Scholar
- C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability in human mobility. Science, 327(5968):1018--1021, 2010.Google ScholarCross Ref
- A. Varshavsky et al. Are gsm phones the solution for localization? In Mobile Computing Systems and Applications, 2006. WMCSA'06. Proceedings. 7th IEEE Workshop on, pages 34--42. IEEE, 2005. Google ScholarDigital Library
- S. Zhu and D. Levinson. Do people use the shortest path? an empirical test of wardrop's first principle. In 91th annual meeting of the Transportation Research Board, Washington, volume 8, 2010.Google Scholar
Index Terms
- From Cells to Streets: Estimating Mobile Paths with Cellular-Side Data
Recommendations
A novel location management in IP-based cellular networks
Mobile IP provides a simple and scalable global mobility solution that supports users to access the internet at anytime and anywhere. However, as the number or the velocity of the Mobile IP users grows, so will the signalling overhead associated with ...
Making Mobile Networks Fly
S3 '18: Proceedings of the 10th on Wireless of the Students, by the Students, and for the Students WorkshopAdvances in mobile (cellular) networks have ushered in an era of abundant connectivity. However, the stationary and expensive nature of their deployment has limited their ability to provide true "ubiquitous" connectivity under the 5G vision - especially ...
Mobility management in all-IP two-tier cellular networks
The seamless internetworking among multiple heterogeneous networks is in demand to provide ''always-on'' connectivity services with QoS provision quality, anywhere, at any time. The hybrid two-tier networks can provide high data rate and enhanced ...
Comments