Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization
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 spatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/10/6/192 |
| _version_ | 1819126745900515328 |
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| author | Kashif Sultan Hazrat Ali Adeel Ahmad Zhongshan Zhang |
| author_facet | Kashif Sultan Hazrat Ali Adeel Ahmad Zhongshan Zhang |
| author_sort | Kashif Sultan |
| collection | DOAJ |
| description | 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 <i>Telecom Italia</i>. 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. |
| first_indexed | 2024-12-22T08:00:55Z |
| format | Article |
| id | doaj.art-5fbf6f463e5a4940ac3f960042f7244a |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-22T08:00:55Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-5fbf6f463e5a4940ac3f960042f7244a2022-12-21T18:33:15ZengMDPI AGInformation2078-24892019-06-0110619210.3390/info10060192info10060192Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and OptimizationKashif Sultan0Hazrat Ali1Adeel Ahmad2Zhongshan Zhang3School of Computer and Communication Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, ChinaDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanSchool of Computer and Communication Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, ChinaThe 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 <i>Telecom Italia</i>. 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.https://www.mdpi.com/2078-2489/10/6/192call details recorddata analyticsmachine learningmobile networks |
| spellingShingle | Kashif Sultan Hazrat Ali Adeel Ahmad Zhongshan Zhang Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization Information call details record data analytics machine learning mobile networks |
| title | Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization |
| title_full | Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization |
| title_fullStr | Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization |
| title_full_unstemmed | Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization |
| title_short | Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization |
| title_sort | call details record analysis a spatiotemporal exploration toward mobile traffic classification and optimization |
| topic | call details record data analytics machine learning mobile networks |
| url | https://www.mdpi.com/2078-2489/10/6/192 |
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