Abstract
This paper describes a software-based tool that tracks mobile node roaming and infers the time-to-handover as well as the preferential handover target, based on behavior inference solely derived from regular usage data captured in visited wireless networks. The paper presents the tool architecture; computational background for mobility estimation; operational guidelines concerning how the tool is being used to track several aspects of roaming behavior in the context of wireless networks. Target selection accuracy is validated having as baseline traces obtained in realistic scenarios.
COPELABS, Building U, First Floor, University Lusofona. Campo Grande 388, 1749-024 Lisboa, Portugal.
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
Notes
- 1.
Software is available as a beta version available directly via Google Apps, https:// play.google.com/store/apps/details?id=eu.uloop.mobilitytracker, via http://copelabs.ulusofona.pt/~uloop/ or at http://copelabs.ulusofona.pt/scicommons/index.php/publications/show/489.
- 2.
The MTracker as proof-of-concept has already given rise to the tool WiRank (https://play.google.com/store/apps/details?id=eu.uloop.wirank), intended to improve Android connectivity and to integrate some aspects of prediction with other features, such as context-awareness based on location.
- 3.
References
Sofia, R., Mendes, P.: User-provided networks: consumer as provider. IEEE Commun. Mag. 46, 86–91 (2008)
Aldini, A., Bogliolo, A.: User-Centric Networking - Future Perspectives. Lecture Notes in Social Networking. Springer (2014). ISBN 978-3-319-05218-2
Kotz, D., Henderson, T., Abyzov, I.: Crawdad trace dartmouth. http://crawdad.cs.dartmouth.edu
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)
Rhee, I., Shin, M., Hong, S., Lee, K., Kim, S.J., Chong, S.: On the levy-walk nature of human mobility. IEEE/ACM Trans. Networking (TON) 19, 630–643 (2011)
Denko, M.K.: Mobility prediction schemes in wireless ad hoc networks. In: Furht, B., Wysocki, T.A., Dadej, A., Wysocki, B.J. (eds.) Advanced Wired and Wireless Networks. Multimedia Systems and Applications, vol. 26, pp. 171–186. Springer, US (2005)
Musolesi, M., Mascolo, C.: Designing mobility models based on social network theory. SIGMOBILE Mob. Comput. Commun. Rev. 11, 59–70 (2007)
Ribeiro, A., Sofia, R.C.: A survey on mobility models for wireless networks. Technical report SITI-TR-11-01, SITI, University Lusófona, February 2011
Song, B.C., Qu, N.Z., Barabasi, A.-L.: Limits of predictability in human mobility. Nature 327, 1018–1021 (2010)
Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: Mining user mobility features for next place prediction in location-based services. In: ICDM 2012, pp. 1038–1043 (2012)
Sofia, R.: Mobility management method and apparatus. EP 13186562.9, Method and Apparatus for Ranking Visited Networks
Acknowledgments
This work has been developed within the EU IST FP7 project ULOOP - User-centric Wireless Local Loop (grant number 247158). The author thanks Jonnahtan Saltarin for the implementation of the second version of the proof-of-concept software MTracker.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Sofia, R. (2015). A Tool to Estimate Roaming Behavior in Wireless Architectures. In: Aguayo-Torres, M., Gómez, G., Poncela, J. (eds) Wired/Wireless Internet Communications. WWIC 2015. Lecture Notes in Computer Science(), vol 9071. Springer, Cham. https://doi.org/10.1007/978-3-319-22572-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-22572-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22571-5
Online ISBN: 978-3-319-22572-2
eBook Packages: Computer ScienceComputer Science (R0)