Header menu link for other important links
X
Mobile Internet Activity Estimation and Analysis at High Granularity: SVR Model Approach
A. Rizwan, , F. Fioranelli, A. Imran, M.A. Imran
Published in Institute of Electrical and Electronics Engineers (IEEE)
2018
Volume: 2018-September
   
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. © 2018 IEEE.
Concepts (4)
  •  related image
    Big data analytics
  •  related image
    Mobile internet traffic estimation
  •  related image
    High granularity spatiotemporal analysis
  •  related image
    SVR