Mobile internet activity estimation and analysis at high granularity: SVR model approach

Rizwan, A., Arshad, K., Fioranelli, F. , Imran, A. and Imran, M. A. (2018) Mobile internet activity estimation and analysis at high granularity: SVR model approach. In: 2018 IEEE 29th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Bologna, Italy, 09-12 Sep 2018, ISBN 9781538660096 (doi: 10.1109/PIMRC.2018.8581040)

Abstract

Understanding of mobile internet traffic patterns and capacity to estimate future traffic, particularly at high spatiotemporal granularity, is crucial for proactive decision making in emerging and future cognizant cellular networks enabled with self-organizing features. It becomes even more important in the world of `Internet of Things' with machines communicating locally. In this paper, internet activity data from a mobile network operator Call Detail Records (CDRs) is analysed at high granularity to study the spatiotemporal variance and traffic patterns. To estimate future traffic at high granularity, a Support Vector Regression (SVR) based traffic model is trained and evaluated for the prediction of maximum, minimum and average internet traffic in the next hour based on the actual traffic in the last hour. Performance of the model is compared with that of the State-of-the-Art (SOTA) deep learning models recently proposed in the literature for the same data, same granularity, and same predicates. It is concluded that this SVR model outperforms the SOTA deep and non-deep learning methods used in the literature.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Rizwan, Ali and Imran, Professor Muhammad and Fioranelli, Dr Francesco
• Authors: Rizwan, A., Arshad, K., Fioranelli, F., Imran, A., and Imran, M. A.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2166-9589
• ISBN: 9781538660096
• Published Online: 10 December 2018