Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization (original) (raw)
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Classifying call profiles in large-scale mobile traffic datasets
IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, 2014
Cellular communications are undergoing significant evolutions in order to accommodate the load generated by increasingly pervasive smart mobile devices. Dynamic access network adaptation to customers' demands is one of the most promising paths taken by network operators. To that end, one must be able to process large amount of mobile traffic data and outline the network utilization in an automated manner. In this paper, we propose a framework to analyze broad sets of Call Detail Records (CDRs) so as to define categories of mobile call profiles and classify network usages accordingly. We evaluate our framework on a CDR dataset including more than 300 million calls recorded in an urban area over 5 months. We show how our approach allows to classify similar network usage profiles and to tell apart normal and outlying call behaviors.
Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks
IEEE Access, 2018
—Mobile networks possess information about the users as well as the network. Such information is useful for making the network end-to-end visible and intelligent. Big data analytics can efficiently analyze user and network information, unearth meaningful insights with the help of machine learning tools. Utilizing big data analytics and machine learning, this work contributes in three ways. First, we utilize the call detail records (CDR) data to detect anomalies in the network. For authentication and verification of anomalies, we use k-means clustering, an unsupervised machine learning algorithm. Through effective detection of anomalies, we can proceed to suitable design for resource distribution as well as fault detection and avoidance. Second, we prepare anomaly-free data by removing anomalous activities and train a neural network model. By passing anomaly and anomaly-free data through this model, we observe the effect of anomalous activities in training of the model and also observe mean square error of anomaly and anomaly free data. Lastly, we use an autoregressive integrated moving average (ARIMA) model to predict future traffic for a user. Through simple visualization, we show that anomaly free data better generalizes the learning models and performs better on prediction task.
2019 28th Wireless and Optical Communications Conference (WOCC), 2019
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.
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2013 IEEE International Conference on Big Data, 2013
This information about our GSM calls is stored by the TelCo operator in large volumes and with strict privacy constraints making it challenging the analysis of these fingerprints for inferring mobility behavior. This paper proposes a strategy for mobility behavior identification based on aggregated calling profiles of mobile phone users. This compact representation of the user call profiles is the input of the mining algorithm for automatically classifying various kinds of mobility behavior. A further advantage of having defined the call profiles is that the analysis phase is based on summarized privacy-preserving representation of the original data. We show how these call profiles permit to design a two step process-implemented into a system-based on a bootstrap phase and a running phase for classifying users into behavior categories. We evaluated the system in two case studies where individuals are classified into residents, commuters and visitors. We conclude the paper with a discussion which emphasizes the role of the call profiles for the design of a new collaboration model between data provider and data analyst.
An Experimental Investigation of Mobile Network Traffic Prediction Accuracy
International journal of big data, 2016
The growth in the number of mobile subscriptions has led to a substantial increase in the mobile network bandwidth demand. The mobile network operators need to provide enough resources to meet the huge network demand and provide a satisfactory level of Quality-of-Service (QoS) to their users. However, in order to reduce the cost, the network operators need an efficient network plan that helps them provide cost effective services with a high degree of QoS. To devise such a network plan, the network operators should have an in-depth insight into the characteristics of the network traffic. This paper applies the time-series analysis technique to decomposing the traffic of a commercial trial mobile network into components and identifying the significant factors that drive the traffic of the network. The analysis results are further used to enhance the accuracy of predicting the mobile traffic. In addition, this paper investigates the accuracy of machine learning techniques-Multi-Layer Perceptron (MLP), Multi-Layer Perceptron with Weight Decay (MLPWD), and Support Vector Machines (SVM)-to predict the components of the commercial trial mobile network traffic. The experimental results show that using different prediction models for different network traffic components increases the overall prediction accuracy up to 17%. The experimental results can help the network operators predict the future resource demands more accurately and facilitate provisioning and placement of the mobile network resources for effective resource management.
Joint spatial and temporal classification of mobile traffic demands
IEEE INFOCOM 2017 - IEEE Conference on Computer Communications
Mobile traffic data collected by network operators is a rich source of information about human habits, and its analysis provides insights relevant to many fields, including urbanism, transportation, sociology and networking. In this paper, we present an original approach to infer both spatial and temporal structures hidden in the mobile demand, via a first-time tailoring of Exploratory Factor Analysis (EFA) techniques to the context of mobile traffic datasets. Casting our approach to the time or space dimensions of such datasets allows solving different problems in mobile traffic analysis, i.e., network activity profiling and land use detection, respectively. Tests with real-world mobile traffic datasets show that, in both its variants above, the proposed approach (i) yields results whose quality matches or exceeds that of state-of-the-art solutions, and (ii) provides additional joint spatiotemporal knowledge that is critical to result interpretation.
Data Mining for Mobile Internet Traffic Flow Forecasting
International Conference on Industrial Engineering and Operations Management Dubai, UAE, March 10-12, 2020, 2020
Internet traffic can be described as a general term that includes the transmission of internet data between different devices and systems. The analysis and prediction of internet traffic is a proactive approach to ensure secure, reliable and qualitative network communication. Several linear and non-linear models are proposed and tested to analyze the prediction of network traffic, including techniques based on regression analysis, artificial intelligence and data mining. These interesting combinations of internet traffic analysis and forecasting techniques are implemented to achieve efficient and effective results. Timely and accurate prediction of the use of internet data is a topic of great importance in the telecommunications industry. In addition, internet traffic data is important for many applications in telecommunications management, such as understand customer behavior, optimal planning of the capacity of networks, successful decision making and maintaining the quality of services at guarantee level in the future. This situation inspires us to rethink data mining with internet traffic forecasting problems. This study provides an overview of data mining in telecommunications and proposes a novel model for forecasting mobile internet traffic based on artificial neural networks. The analysis of mobile Internet traffic data during the last five years shows that the month of August has a higher traffic flow, while the lowest flow was the month of June. The selected model has three layers with a determination coefficient of 97.5%.
Hybrid Prediction Model for Mobile Data Traffic: A Cluster-level Approach
2020 International Joint Conference on Neural Networks (IJCNN), 2020
Mobile data consumption is rapidly growing following the ever-increasing bandwidth-hungry applications and improvements in network data rates. With the anticipated 5G right at the corner, operators are focusing on load-aware network dimensioning, optimization, and management, where traffic volume prediction plays a critical role. To this end, several researchers investigated different statistical and machine-learning models to exploit and predict the linear and nonlinear patterns that often arise due to the complexity of mobile networks and varying users' behaviors at different times and locations. In this paper, we propose a hybrid model composed of Double Seasonal ARIMA (D-SARIMA), which focuses on modeling the multi-seasonal nature of the data traffic and exploiting the residuals of DSARIMA via Long-Short Term Memory (LSTM)-based Networks. The residues contain the nonlinear component of the data. To incorporate the spatial dependency inherent in mobile data traffic collected ...
Understanding and Predicting Data Hotspots in Cellular Networks
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The unprecedented growth in mobile data usage is posing significant challenges to cellular operators. One key challenge is how to provide quality of service to subscribers when their residing cell is experiencing a significant amount of traffic, i.e. becoming a traffic hotspot. In this paper, we perform an empirical study on data hotspots in today's cellular networks using a 9-week cellular dataset with 734K+ users and 5327 cell sites. Our analysis examines in details static and dynamic characteristics, predictability, and causes of data hotspots, and their correlation with call hotspots. We show that using standard machine learning methods, future hotspots can be accurately predicted from past observations. We believe the understanding of these key issues will lead to more efficient and responsive resource management and thus better QoS provision in cellular networks. To the best of our knowledge, our work is the first to empirically characterize traffic hotspots in today's cellular networks.
IEEE Communications Letters, 2019
Spatio-temporal characterization of user traffic is the first step in designing, optimizing and automating a mobile cellular network. While it is well known that voice telephony follows Poisson distribution, the distribution of SMS and internet data usage along with voice calls and the factors influencing the distribution, is still an open question. We characterize the distribution of multi-faceted cellular traffic while identifying the factors influencing the parameterization of the distribution. Eight latent features that play a statistically significant role to characterize the traffic distribution variations over time and space are determined by leveraging a large real dataset. The features used to characterize the dynamism of the traffic distribution are Points of Interest, day of the week, special events and region. Results show that Generalized Extreme Value distribution best describes SMS, call and internet activity and it does not change with spatio-temporal features. Also, traffic distribution is not stationary. Insights gained from this analysis can pave the way towards more precise and resource efficient planning, designing and optimization of future cellular networks.