publication . Article . Preprint . 2017

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Nicola Bui; Matteo Cesana; S. Amir Hosseini; Qi Liao; Ilaria Malanchini; Joerg Widmer;
Open Access
  • Published: 19 Apr 2017
  • Publisher: Zenodo
  • Country: Italy
A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.
Comment: 31 pages, 5 figures, 6 tables, accepted for publications in IEEE Communications Survey and Tutorials
free text keywords: Anticipatory, Prediction, Optimization, 5G, Mobile Networks, Anticipatory, Prediction, Optimization, 5G, Mobile Networks, Computer Science - Networking and Internet Architecture, 90B18, 62M20, Electrical and Electronic Engineering, 5G; Anticipatory; mobile networks; optimization; prediction; Electrical and Electronic Engineering, Computer science, Context (language use), Context model, Next-generation network, Information technology, business.industry, business, Mobile telephony, Network performance, Data science, Network dynamics, Mobile computing
Funded by
Anticipatory Networking Techniques in 5G and Beyond
  • Funder: European Commission (EC)
  • Project Code: 643002
  • Funding stream: H2020 | MSCA-ITN-EID
Validated by funder
Measuring Mobile Broadband Networks in Europe
  • Funder: European Commission (EC)
  • Project Code: 644399
  • Funding stream: H2020 | RIA
Validated by funder
112 references, page 1 of 8

[1] C. Song, Z. Qu, N. Blumm, and A.-L. Baraba´si, “Limits of predictability in human mobility,” Science, vol. 327, no. 5968, pp. 1018-1021, 2010. [OpenAIRE]

[2] X. Lu, E. Wetter, N. Bharti, A. J. Tatem, and L. Bengtsson, “Approaching the limit of predictability in human mobility,” Scientific reports, vol. 3, 2013.

[3] Y. Jiang, D. C. Dhanapala, and A. P. Jayasumana, “Tracking and prediction of mobility without physical distance measurements in sensor networks,” in Communications (ICC), 2013 IEEE International Conference on. IEEE, 2013, pp. 1845-1850. [OpenAIRE]

[4] L. Ghouti, T. R. Sheltami, and K. S. Alutaibi, “Mobility prediction in mobile ad hoc networks using extreme learning machines,” Procedia Computer Science, vol. 19, pp. 305-312, 2013. [OpenAIRE]

[5] X. Chen, F. Me´riaux, and S. Valentin, “Predicting a user's next cell with supervised learning based on channel states,” in Signal Processing Advances in Wireless Communications (SPAWC), 2013 IEEE 14th Workshop on, June 2013, pp. 36-40.

[6] H. Xiong, D. Zhang, D. Zhang, V. Gauthier, K. Yang, and M. Becker, “MPaaS: Mobility prediction as a service in telecom cloud,” Information Systems Frontiers, vol. 16, no. 1, pp. 59-75, 2014.

[7] J.-K. Lee and J. C. Hou, “Modeling steady-state and transient behaviors of user mobility: formulation, analysis, and application,” in Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing. ACM, 2006, pp. 85-96.

[8] H. Abu-Ghazaleh and A. S. Alfa, “Application of mobility prediction in wireless networks using Markov renewal theory,” Vehicular Technology, IEEE Transactions on, vol. 59, no. 2, pp. 788-802, 2010.

[9] D. Barth, S. Bellahsene, and L. Kloul, “Mobility prediction using mobile user profiles,” in Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2011 IEEE 19th International Symposium on. IEEE, 2011, pp. 286-294.

[10] --, “Combining local and global profiles for mobility prediction in LTE femtocells,” in Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems. ACM, 2012, pp. 333-342.

[11] G. Gido´falvi and F. Dong, “When and where next: Individual mobility prediction,” in Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems. ACM, 2012, pp. 57-64.

[12] Y. Chon, N. D. Lane, Y. Kim, F. Zhao, and H. Cha, “Understanding the coverage and scalability of place-centric crowdsensing,” in Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM, 2013, pp. 3-12.

[13] Y. Chon, H. Shin, E. Talipov, and H. Cha, “Evaluating mobility models for temporal prediction with high-granularity mobility data,” in Pervasive Computing and Communications (PerCom), 2012 IEEE International Conference on. IEEE, 2012, pp. 206-212.

[14] Y. Chon, E. Talipov, H. Shin, and H. Cha, “SmartDC: Mobility prediction-based adaptive duty cycling for everyday location monitoring,” Mobile Computing, IEEE Transactions on, vol. 13, no. 3, pp. 512-525, 2014.

[15] --, “Mobility prediction-based smartphone energy optimization for everyday location monitoring,” in Proceedings of the 9th ACM conference on embedded networked sensor systems. ACM, 2011, pp. 82-95.

112 references, page 1 of 8
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