Analysis and modeling of mobile traffic using real traces (original) (raw)
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Understanding traffic dynamics in cellular data networks
2011 Proceedings IEEE INFOCOM, 2011
We conduct the first detailed measurement analysis of network resource usage and subscriber behavior using a largescale data set collected inside a nationwide 3G cellular data network. The data set tracks close to a million subscribers over thousands of base stations. We analyze individual subscriber behaviors and observe a significant variation in network usage among subscribers. We characterize subscriber mobility and temporal activity patterns and identify their relation to traffic volume. We then investigate how efficiently radio resources are used by different subscribers as well as by different applications. We also analyze the network traffic from the point of view of the base stations and find significant temporal and spatial variations in different parts of the network, while the aggregated behavior appears predictable. Broadly, our observations deliver important insights into network-wide resource usage. We describe implications in pricing, protocol design and resource and spectrum management.
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.
Large-Scale Mobile Traffic Analysis: A Survey
IEEE Communications Surveys & Tutorials, 2016
This article surveys the literature on analyses of mobile traffic collected by operators within their network infrastructure. This is a recently emerged research field, and, apart from a few outliers, relevant works cover the period from 2005 to date, with a sensible densification over the last three years. We provide a thorough review of the multidisciplinary activities that rely on mobile traffic datasets, identifying major categories and sub-categories in the literature, so as to outline a hierarchical classification of research lines. When detailing the works pertaining to each class, we balance a comprehensive view of state-ofthe-art results with punctual focuses on the methodological aspects. Our approach provides a complete introductory guide to the research based on mobile traffic analysis. It allows summarizing the main findings of the current state-of-the-art, as well as pinpointing important open research directions.
Review of traffic data estimations extracted from cellular networks
IET Intelligent Transport Systems, 2008
One of the main characteristics of modern society is the never-ending increase in mobility. This leads to a series of problems such as congestion and increased pollution. To resolve these problems, it is imperative to have a good road network management and planning. To efficiently identify the characteristics of traffic in the road network, it would be necessary to perform a permanent monitorisation of all roadway links. This would involve an excessive cost of installation and maintenance of road infrastructure. Hence, new alternatives are required which can characterise traffic in a real time with good accuracy at an acceptable price. Mobile telephone systems are considered as a promising technology for the traffic data collection system. Its extensive use in converting its subscribers in a broad sample to draw information from phones becomes anonymous probes to monitor traffic. It is reviewed how to obtain parameters related to traffic from cellular-network-based data, describing methods used in existing simulation works as well as field tests in the academic and industrial field. 2 Mobility management in mobile phone networks The mobility required today is modifying the life style both at an individual and collective level. The result is the need for
Cellular data meet vehicular traffic theory
Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp '12, 2012
Road traffic can be monitored by means of static sensors and derived from floating car data, i.e., reports from a sub-set of vehicles. These approaches suffer from a number of technical and economical limitations. Alternatively, we propose to leverage the mobile cellular network as a ubiquitous mobility sensor. We show how vehicle travel times and road congestion can be inferred from anonymized signaling data collected from a cellular mobile network. While other previous studies have considered data only from active devices, e.g., engaged in voice calls, our approach exploits also data from idle users resulting in an enormous gain in coverage and estimation accuracy. By validating our approach against four different traffic monitoring datasets collected on a sample highway over one month, we show that our method can detect congestions very accurately and in a timely manner.
Transport Analytics Based on Cellular Network Signalling Data
Linköping Studies in Science and Technology. Dissertations
Cellular networks of today generate a massive amount of signalling data. A large part of this signalling is generated to handle the mobility of subscribers and contains location information that can be used to fundamentally change our understanding of mobility patterns. However, the location data available from standard interfaces in cellular networks is very sparse and an important research question is how this data can be processed in order to efficiently use it for traffic state estimation and traffic planning. In this thesis, the potentials and limitations of using this signalling data in the context of estimating the road network traffic state and understanding mobility patterns is analyzed. The thesis describes in detail the location data that is available from signalling messages in GSM, GPRS and UMTS networks, both when terminals are in idle mode and when engaged in a telephone call or a data session. The potential is evaluated empirically using signalling data and measurements generated by standard cellular phones. The data used for analysis of location estimation and route classification accuracy (Paper I-IV in the thesis) is collected using dedicated hardware and software for cellular network analysis as well as tailor-made Android applications. For evaluation of more advanced methods for travel time estimation, data from GPS devices located in Taxis is used in combination with data from fixed radar sensors observing point speed and flow on the road network (Paper V). To evaluate the potential in using cellular network signalling data for analysis of mobility patterns and transport planning, real data provided by a cellular network operator is used (Paper VI). The signalling data available in all three types of networks is useful to estimate several types of traffic data that can be used for traffic state estimation as well as traffic planning. However, the resolution in time and space largely depends on which type of data that is extracted from the network, which type of network that is used and how it is processed. First of all, I would like to thank my main supervisor Prof. Johan M Karlsson and my co-supervisor Prof. Di Yuan for all their patient support, encouragement and guidance throughout the years. I am also very thankful to all the colleagues at the division of Communication and Transport Systems for making it such a stimulating working environment with inspiring discussions and friendly atmosphere. A special thanks to Prof. Jan Lundgren for his encouragement and support along the way. I am also grateful to Clas Rydergren and Joakim Ekström for fruitful traffic discussions, Erik Bergfeldt and Vangelis Angelakis for the corresponding telecom discussions as well as Nils Breyer and Rasmus Ringdahl for all the interesting technical discussions. I would also like to thank Prof. Alexandre M Bayen and Anthony D Patire at the University of California, Berkeley, Prof. Jaume Barcelo at Polytechnic University of Catalonia and Tomas Julner at the Swedish Transport Administration for a very inspiring collaboration over the years. The research included in this thesis has been financed by the Swedish Transport Administration through the Centre for Traffic Research, the Swedish Governmental Agency for Innovation Systems (VINNOVA) and Norrköping Municipality. Finally, I would like to thank all my family and friends for the support and inspiration you have given me. Thank you Sofia for all your support that made this possible and thank you Tim, Adam, Herman and Joel for being the best source of joy and inspiration! Norrköping, October 2018 David Gundlegård Contents
Deriving Traffic Data from a Cellular Network
2006
Acquiring high quality origin destination information for the vehicle traffic in a geographic area is a tedious and costly task. Traditional methods are expensive, time-consuming and generally only present a snapshot of the traffic situation at a certain time. The technique developed in this paper exploits the use of data already at hand in a GSM network. Instead of monitoring vehicles flows, mobile phones flows are measured and are correlated to the traffic flow. This methodology is based on the fact that a GSM network always knows an estimated position of each terminal, referred to the location area of the base station that provides services to it. For a pilot study a GSM network simulator has been designed to generate a synthetic database with location registers, which is then processed mathematically and transformed into traffic data. Primary results show great potential of this method.
4G LTE Network Data Collection and Analysis along Public Transportation Routes
GLOBECOM 2020 - 2020 IEEE Global Communications Conference
With the advancements in wireless network technologies over the past few decades and the deployment of 4G LTE networks, the capabilities and services provided to endusers have become seemingly endless. Users of smartphones utilize high-speed network services while commuting on public transit and hope to have a consistent, high-quality connection for the duration of their trip. Due to the massive load demand on cellular networks and frequent changes in the underlying radio channel, users often experience sudden unexpected variations in the connection quality. To overcome such variations and maintain a consistent connection, these variations need to be predicted before they occur. This can be accomplished by the spatio-temporal analysis of the different network quality parameters and the investigation of the main factors that affect the network's performance and QoS. To this end, we conducted a network survey via Kingston Transit in Kingston, Ontario, Canada. We used the Android network monitoring application G-NetTrack Pro to build a dataset of various client-side wireless network quality parameters. The dataset consists of 30 repeated public transit bus trips at three different times of the day, each lasting around one hour. In this paper, we describe the data collection process, present an analysis of the collected data, and investigate the effects of time and location on the network's measured throughput and signal strength. We made the collected data, including more than 190 thousand unique records, publicly available to researchers in a domain where open data is rare.
Traffic analysis at short time-scales: an empirical case study from a 3G cellular network
IEEE Transactions on Network and Service Management, 2000
The availability of synchronized packet-level traces captured at different links allows the extraction of one-way delays for the network section in between. Delay statistics can be used as quality indicators to validate the health of the network and to detect global performance drifts and/or localized problems. Since packet delays depend not only on the network status but also on the arriving traffic rate, the delay analysis must be coupled with the analysis of the traffic patterns at short time scales.
Trajectory estimation based on mobile network operator data for cellular network simulations
Eurasip Journal on Wireless Communications and Networking, 2016
In this paper, we present a framework for estimating trajectories of cellular networks users based on mobile network operator data. We use handover and location area update events of both speech and packet data users captured in the core network of the Austrian MNO A1 to estimate the subscribers' mobility behavior. By utilizing publicly available data, i.e., environmental information, road infrastructure data, transmitter power ranges and antenna characteristics, our approach allows the estimation of subscriber trajectories for both urban and semi-rural environments with a good accordance to the actual trajectories. Additionally, we present a method to estimate a particular subscriber's movement velocity, on the basis of mentioned data. Furthermore, we propose a methodology to estimate when a particular user started or ended a speech or packet data session during his journey, based on mobility-related network events. With this, our framework enables the creation of reproducible mobility situations for cellular network simulations at system level.