Abstract
Accurate and fine-grained prediction of future user location and geographical profile has interesting and promising applications including targeted content service, advertisement dissemination for mobile users, and recreational social networking tools for smart-phones. Existing techniques based on linear and probabilistic models are not able to provide accurate prediction of the location patterns from a spatio-temporal perspective, especially for long-term estimation. More specifically, they are able to only forecast the next location of a user, but not his/her arrival time and residence time, i.e., the interval of time spent in that location. Moreover, these techniques are often based on prediction models that are not able to extend predictions further in the future.
In this paper we present NextPlace, a novel approach to location prediction based on nonlinear time series analysis of the arrival and residence times of users in relevant places. NextPlace focuses on the predictability of single users when they visit their most important places, rather than on the transitions between different locations. We report about our evaluation using four different datasets and we compare our forecasting results to those obtained by means of the prediction techniques proposed in the literature. We show how we achieve higher performance compared to other predictors and also more stability over time, with an overall prediction precision of up to 90% and a performance increment of at least 50% with respect to the state of the art.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aalto, L., Göthlin, N., Korhonen, J., Ojala, T.: Bluetooth and WAP Push Based Location-aware Mobile Advertising System. In: Proceedings of MobiSys 2004, pp. 49–58 (2004)
Ashbrook, D., Starner, T.: Using GPS to Learn Significant Locations and Predict Movement Across Multiple Users. Journal of Personal and Ubiquitous Computing 7(5), 275–286 (2003)
Balachandran, A., Voelker, G.M., Bahl, P., Rangan, P.V.: Characterizing User Behavior and Network Performance in a Public Wireless LAN. In: Proceedings of SIGMETRICS 2002 (2002)
Balazinska, M., Castro, P.: Characterizing Mobility and Network Usage in a Corporate Wireless Local-Area Network. In: Proceedings of MobiSys 2003, San Francisco, CA (May 2003)
Chaintreau, A., Hui, P., Crowcroft, J., Diot, C., Gass, R., Scott, J.: Impact of Human Mobility on Opportunistic Forwarding Algorithms. IEEE Transactions on Mobile Computing 6(6), 606–620 (2007)
Chatfield, C.: The Analysis of Time Series: An Introduction, 5th edn. Chapman & Hall/CRC, London (July 1995)
Eagle, N., Pentland, A.S.: Reality Mining: Sensing Complex Social Systems. Personal Ubiquitous Comput. 10(4), 255–268 (2006)
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding Individual Human Mobility Patterns. Nature 453(7196), 779–782 (2008)
Henderson, T., Kotz, D., Abyzov, I.: The Changing Usage of a Mature Campus-wide Wireless Network. In: Proceedings of MobiCom 2004, New York, NY, USA, pp. 187–201 (2004)
Jain, S., Fall, K., Patra, R.: Routing in a Delay Tolerant Network. In: Proceedings of SIGCOMM 2004 (2004)
Kang, J.H., Welbourne, W., Stewart, B., Borriello, G.: Extracting Places from Traces of Locations. SIGMOBILE Mobile Computing Communication Review 9(3), 58–68 (2005)
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (2004)
Karagiannis, T., Le Boudec, J.-Y., Vojnovic, M.: Power Law and Exponential Decay of Inter-contact Times Between Mobile Devices. In: Proceedings of MobiCom 2007, pp. 183–194 (2007)
Kim, M., Kotz, D., Kim, S.: Extracting a Mobility Model from Real User Traces. In: Proceedings of INFOCOM 2006 (April 2006)
Kotz, D., Henderson, T., Abyzov, I.: CRAWDAD trace dartmouth/campus/movement/01_04 (v. 2005-03-08) (March 2005), http://crawdad.cs.dartmouth.edu/
Krumm, J., Horvitz, E.: Predestination: Inferring Destinations from Partial Trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243–260. Springer, Heidelberg (2006)
LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J., Potter, F., Tabert, J., Powledge, P., Borriello, G., Schilit, B.: Place Lab: Device Positioning Using Radio Beacons in the Wild. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 116–133. Springer, Heidelberg (2005)
Lenczner, M., Gregoire, B., Roulx, F.: CRAWDAD data set ilesansfil/wifidog (v. 2007-08-27) (August 2007), http://www.crawdad.cs.dartmouth.edu/ilesansfil/wifidog
Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Building Personal Maps from GPS Data. In: Proceedings of IJCAI Workshop on Modeling Others from Observation (2005)
Marmasse, N., Schmandt, C.: Location-Aware Information Delivery with ComMotion. In: Thomas, P., Gellersen, H.-W. (eds.) HUC 2000. LNCS, vol. 1927, pp. 157–171. Springer, Heidelberg (2000)
Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., Campbell, A.T.: Sensing Meets Mobile Social Networks: the Design, Implementation and Evaluation of the CenceMe Application. In: Proceedings of SenSys 2008, pp. 337–350. ACM, New York (2008)
Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of SIGKDD 2009, pp. 637–646. ACM, New York (2009)
Nicholson, A.J., Noble, B.D.: BreadCrumbs: Forecasting Mobile Connectivity. In: Proceedings of MobiCom 2008, pp. 46–57. ACM, New York (2008)
Piorkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: CRAWDAD trace set epfl/mobility/cab (v. 2009-02-24) (February 2009), http://crawdad.cs.dartmouth.edu/epfl/mobility/cab
Schreiber, T.: Efficient Neighbor Searching in Nonlinear Time Series. International Journal on Bifurcations and Chaos 5, 349–358 (1995)
Song, L., Deshpande, U., Kozat, U.C., Kotz, D., Jain, R.: Predictability of WLAN Mobility and its Effects on Bandwidth Provisioning. In: Proceedings of INFOCOM 2006 (April 2006)
Song, L., Kotz, D.: Evaluating Location Predictors with Extensive Wi-Fi Mobility Data. In: Proceedings of INFOCOM 2004, pp. 1414–1424 (2004)
Zhou, C., Frankowski, D., Ludford, P., Shekhar, S., Terveen, L.: Discovering Personally Meaningful Places: An Interactive Clustering Approach. ACM Trans. Inf. Syst. 25(3), 12 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Scellato, S., Musolesi, M., Mascolo, C., Latora, V., Campbell, A.T. (2011). NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems. In: Lyons, K., Hightower, J., Huang, E.M. (eds) Pervasive Computing. Pervasive 2011. Lecture Notes in Computer Science, vol 6696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21726-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-21726-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21725-8
Online ISBN: 978-3-642-21726-5
eBook Packages: Computer ScienceComputer Science (R0)