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MPaaS: Mobility prediction as a service in telecom cloud

Haoyi Xiong (), Daqing Zhang (), Daqiang Zhang (), Vincent Gauthier, Kun Yang () and Monique Becker
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Haoyi Xiong: Institut Mines-Tèlècom, Tèlècom SudParis
Daqing Zhang: Institut Mines-Tèlècom, Tèlècom SudParis
Daqiang Zhang: Tongji University
Vincent Gauthier: Institut Mines-Tèlècom, Tèlècom SudParis
Kun Yang: University of Essex
Monique Becker: Institut Mines-Tèlècom, Tèlècom SudParis

Information Systems Frontiers, 2014, vol. 16, issue 1, No 6, 59-75

Abstract: Abstract 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.

Keywords: Mobility prediction; Mobile cloud computing; Telecommunication system; Telecom cloud; Collective behaviors (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-013-9476-z

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