MPaaS: Mobility prediction as a service in telecom cloud

Haoyi Xiong (), Daqing Zhang (), Daqiang Zhang (), Vincent Gauthier, Kun Yang () and Monique Becker
Additional contact information
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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10796-013-9476-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:16:y:2014:i:1:d:10.1007_s10796-013-9476-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10796

DOI: 10.1007/s10796-013-9476-z

Access Statistics for this article

Information Systems Frontiers is currently edited by Ram Ramesh and Raghav Rao

More articles in Information Systems Frontiers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().