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Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization

1
School of Computer and Communication Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China
2
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
*
Author to whom correspondence should be addressed.
Information 2019, 10(6), 192; https://doi.org/10.3390/info10060192
Received: 29 March 2019 / Revised: 27 May 2019 / Accepted: 29 May 2019 / Published: 3 June 2019
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Abstract

The information contained within Call Details records (CDRs) of mobile networks can be used to study the operational efficacy of cellular networks and behavioural pattern of mobile subscribers. In this study, we extract actionable insights from the CDR data and show that there exists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can be leveraged by the mobile operators for effective network planning such as resource management and optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly available from Telecom Italia. Thus, on the basis of spatiotemporal insights, we propose a framework for mobile traffic classification. Experimental results show that the proposed model based on machine learning technique is able to accurately model and classify the network traffic patterns. Furthermore, we demonstrate the application of such insights for resource optimisation. View Full-Text
Keywords: call details record; data analytics; machine learning; mobile networks call details record; data analytics; machine learning; mobile networks
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Sultan, K.; Ali, H.; Ahmad, A.; Zhang, Z. Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization. Information 2019, 10, 192.

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