Paper Info
Title
Mobile Internet Activity Estimation and Analysis at High Granularity: SVR Model Approach
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.
Year
DOI
Venue
2018
10.1109/PIMRC.2018.8581040
2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Keywords
Field
DocType
Big Data Analytics,Mobile Internet Traffic Estimation,High Granularity Spatiotemporal Analysis,SVR
Mobile network operator,Computer science,Support vector machine,Real-time computing,Artificial intelligence,Cellular network,Granularity,Deep learning,Big data,Machine learning,Internet traffic,The Internet
Conference
ISSN
ISBN
Citations 
2166-9570
978-1-5386-6010-2
0
PageRank 
References 
Authors
0.34
4
5
Name
Order
Citations
PageRank
Ali Rizwan121.70
Kamran Arshad232826.17
Francesco Fioranelli3516.54
Ali Imran438240.89
Muhammad Ali Imran52920278.27