@inproceedings{51b65915c8294f35b19918b1d38d0d9a,
title = "Understanding and partitioning mobile traffic using internet activity records data - A spatiotemporal approach",
abstract = "The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network's resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based on this, we present mobile traffic partitioning scheme. Experimental results of the proposed model is helpful in modelling and partitioning of network traffic patterns.",
keywords = "Data analytics, Internet activity record, Machine learning, Mobile networks",
author = "Kashif Sultan and Hazrat Ali and Haris Anwaar and Nkabiti, {Kabo Poloko} and Adeel Ahamd and Zhongshan Zhang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 28th Wireless and Optical Communications Conference, WOCC 2019 ; Conference date: 09-05-2019 Through 10-05-2019",
year = "2019",
month = may,
doi = "10.1109/WOCC.2019.8770653",
language = "English",
series = "2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings",
address = "United States",
}