Information Systems Frontiers

, Volume 16, Issue 1, pp 59–75 | Cite as

MPaaS: Mobility prediction as a service in telecom cloud

  • Haoyi Xiong
  • Daqing Zhang
  • Daqiang Zhang
  • Vincent Gauthier
  • Kun Yang
  • Monique Becker


Mobile applications and services relying on mobility prediction have recently spurred lots of interest. In this paper, we propose mobility prediction based on cellular traces as an infrastructural level service of telecom cloud. Mobility Prediction as a Service (MPaaS) embeds mobility mining and forecasting algorithms into a cloud-based user location tracking framework. By empowering MPaaS, the hosted 3rd-party and value-added services can benefit from online mobility prediction. Particularly we took Mobility-aware Personalization and Predictive Resource Allocation as key features to elaborate how MPaaS drives new fashion of mobile cloud applications. Due to the randomness of human mobility patterns, mobility predicting remains a very challenging task in MPaaS research. Our preliminary study observed collective behavioral patterns (CBP) in mobility of crowds, and proposed a CBP-based mobility predictor. MPaaS system equips a hybrid predictor fusing both CBP-based scheme and Markov-based predictor to provide telecom cloud with large-scale mobility prediction capacity.


Mobility prediction Mobile cloud computing Telecommunication system Telecom cloud Collective behaviors 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Haoyi Xiong
    • 1
  • Daqing Zhang
    • 1
  • Daqiang Zhang
    • 2
  • Vincent Gauthier
    • 1
  • Kun Yang
    • 3
  • Monique Becker
    • 1
  1. 1.CNRS UMR 5157 SAMOVARInstitut Mines-Tèlècom, Tèlècom SudParisEvryFrance
  2. 2.School of Software EngineeringTongji UniversityShanghaiChina
  3. 3.School of Computer Science & Electronic EngineeringUniversity of EssexColchesterUK

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