Cellular Data Networks Research Articles

Energy consumption prediction using people dynamics derived from cellular network data

Energy efficiency is a key challenge for building sustainable societies. Due to growing populations, increasing incomes and the industrialization of developing countries, the world primary energy consumption is expected to increase annually by 1.6%. This scenario raises issues related to the increasing scarcity of natural resources, the accelerating pollution of the environment, and the looming threat of global climate change. In this paper we introduce a new and original approach to predict next week energy consumption based on human dynamics analysis derived out of the anonymized and aggregated telecom data, which is processed from GSM network call data records (CDRs). We introduce an original problem statement, analyze regularities of the source data, provide insight on the original feature extraction method and discuss peculiarities of the regression models applicable for this big data problem. The proposed solution could act on energy producers/distributors as an essential aid to smart meters data for making better decisions in reducing total primary energy consumption by limiting energy production when the demand is not predicted, reducing energy distribution costs by efficient buy-side planning in time and providing insights for peak load planning in geographic space.

Automatic identification of relevant places from cellular network data

We present a methodology to automatically identify users’ relevant places from cellular network data.11The approach described in this paper is the subject of Patent Application PCT/EP2014/058003 filed by Telecom Italia on April 2014. In this work we used anonymized Call Detail Record (CDR) comprising information on where and when users access the cellular network. The key idea is to effectively cluster CDRs together and to weigh clusters to determine those associated to frequented places. The approach can identify users’ home and work locations as well as other places (e.g., associated to leisure and night life). We evaluated our approach threefold: (i) on the basis of groundtruth information coming from a fraction of users whose relevant places were known, (ii) by comparing the resulting number of inhabitants of a given city with the number of inhabitants as extracted by the national census. (iii) Via stability analysis to verify the consistency of the extracted results across multiple time periods. Results show the effectiveness of our approach with an average 90% precision and recall.

Estimating attendance from cellular network data

ABSTRACTAn automatic estimate of the number of attendees to events happening in the city can provide valuable information to geographic information systems and geo-located applications. We present a methodology to estimate the number of events’ attendees from cellular network data. In this work, we used anonymized Call Detail Records (CDRs) comprising data on where and when users access the cellular network. Our approach is based on two key ideas: (1) we identify the network cells associated with the event location. (2) We verify the attendance of each user, as a measure of whether (s)he generates CDRs during the event, but not during other times. We evaluate our approach to estimate the number of attendees to a number of events ranging from football matches in stadiums to concerts and festivals in open squares. Comparing our results with the best groundtruth data available, our estimates provide a median error of less than 15% of the actual number of attendees.

Traffic Information Estimation Methods From Handover Events

Abstract Fast growth of the economy and technology upgrades have led to improvements in the quality of traditional transport systems. As such, the use of intelligent transportation systems (ITS) has become more and more popular. The implementation and improvement of real-time traffic information systems are an important parts of ITS. Compared with other traditional methods, traffic information estimations from cellular network data are now readily available, more cost-effective, and easier to deploy and maintain. This study assumed that nonvehicle calls could be filtered out and vehicles could be tracked on road segments. A novel ITS model was proposed to indicate the relationship between call arrival rate and traffic density. Moreover, the vehicle speed and traffic flow were estimated by using cellular floating vehicle data (CFVD) and the proposed novel ITS model. In experiments, this study used a VISSIM traffic simulator and adopted the average call inter-arrival time and call holding time to simulate communication behavior on road segments. The estimated traffic information was compared with the simulated traffic information from stationary vehicle detectors (VD). The results indicated that the average accuracies for vehicle speed estimation, traffic flow estimation, and traffic density estimation in the congested flow case were 97.63, 89.72, and 90.45 %, respectively. Therefore, this approach was feasible to estimate traffic information for ITS improvement.

Travel Mode Detection Exploiting Cellular Network Data

There has been growing interest in exploiting cellular network data for transportation planning purposes in recent years. In this paper, we utilize these data for determining mode of travel in the city of Shiraz, Iran. Cellular data records -including location updates in 5minute time intervals- of 300,000 users from the city of Shiraz has been collected for 40 hours in three consecutive days in a cooperation with the major telecommunications service provider of the country. Depending on the density of mobile BTS’s in different zones of the city, the user location can be located within an average of 200 meters. Considering data filtering and smoothing, data preparation and converting them to comprehensible traces is a large portion of the work. A novel approach to identify stay locations is proposed and implemented in this paper. Origin-Destination matrices are then created based on trips detected, which shows acceptable consistency with current O-D matrices. Finally, Travel times for all trips of a user is estimated as the main attribute for clustering. Trips between same origin and destination zones are combined together in a group. Using K-means algorithm, records within each group are the portioned in two or three clusters, based on their travel speeds. Each cluster represents a certain mode of travel; walking, public transportation or driving a private car.

Crowdsourcing emergency data in non-operational cellular networks

In overloaded or partially broken (i.e., non-operational) cellular networks, it is imperative to enable communication within the crowd to allow the management of emergency and crisis situations. To this end, a variety of emerging short-range communication technologies available on smartphones, such as, Wi-Fi Direct, 3G/LTE direct or Bluetooth/BLE, are able to enable users nowadays to shape point-to-point communication among them. These technologies, however, do not support the formation of overlay networks that can be used to gather and transmit emergency response state (e.g., transfer the location of trapped people to nearby people or the emergency response guard). In this paper, we develop techniques that generate the k-Nearest-Neighbor (kNN) overlay graph of an arbitrary crowd that interconnects over some short-range communication technology. Enabling a kNN overlay graph allows the crowd to connect to its geographically closest peers, those that can physically interact with the user and respond to an emergency crowdsourcing task, such as seeing/sensing similar things as the user (e.g., collect videos and photos). It further allows for intelligent synthesis and mining of heterogeneous data based on the computed kNN graph of the crowd to extract valuable real-time information. We particularly present two efficient algorithms, namely Akin+ and Prox+, which are optimized to work on a resource-limited mobile device. We use Rayzit, a real-world crowd messaging framework we develop, as an example that operates on a kNN graph to motivate and evaluate our work. We use mobility traces collected from three sources for evaluation. The results show that Akin+ and Prox+ significantly outperform existing algorithms in efficiency, even under a skewed distribution of users.

A hybrid local and distributed sketching design for accurate and scalable heavy key detection in network data streams

Real-time characterization of network traffic anomalies, such as heavy hitters and heavy changers, is critical for the robustness of operational networks, but its accuracy and scalability are challenged by the ever-increasing volume and diversity of network traffic. We address this problem by leveraging parallelization. We propose LD-Sketch, a data structure designed for accurate and scalable traffic anomaly detection using distributed architectures. LD-Sketch combines the classical counter-based and sketch-based techniques, and performs detection in two phases: local detection, which guarantees zero false negatives, and distributed detection, which reduces false positives by aggregating multiple detection results. We derive the error bounds and the space and time complexity for LD-Sketch. We further analyze the impact of ordering of data items on the memory usage and accuracy of LD-Sketch. We compare LD-Sketch with state-of-the-art sketch-based techniques by conducting experiments on traffic traces from a real-life 3G cellular data network. Our results demonstrate the accuracy and scalability of LD-Sketch over prior approaches.

WiScape: A Framework for Measuring the Performance of Wide-Area Wireless Networks

We present WiScape, a framework for benchmarking and understanding the behavior of wide-area wireless networks, e.g., city-wide or nation-wide cellular data networks using active participation from clients. The goal of WiScape is to provide a coarse-grained view of a wide-area wireless landscape that allows operators and users to understand broad performance characteristics of the network. In WiScape, a centralized controller instructs clients to collect minimal measurement samples over time and space in an opportunistic manner. To limit the overheads of this measurement framework, WiScape partitions the world into zones, contiguous areas with relatively similar user experiences, and partitions time into zone-specific epochs over which network statistics are relatively stable. For each epoch in each zone, WiScape takes a minimalistic view—it attempts to collect a small number of measurement samples to characterize the client experience in a zone at a specific epoch, thereby limiting the bandwidth and energy overheads for collecting measurements at client devices. For this effort, we have collected ground truth measurements for three commercial cellular networks across a nation-wide area in USA for a period of more than one year. We justify our design choices of WiScape through collected data, demonstrate that WiScape can provide an accurate performance characterization of the networks over a wide-area (within 4 percent error for more than 70 percent of instances) with a low overhead on the clients, and illustrate multiple applications of this framework through a sustained and ongoing measurement study.

Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Significant geospatial and temporal correlations, in terms of traffic volume and application access, exist in cellular network usage as shown in recent studies on cellular network measurement. Such geospatial and temporal correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. To the best of our knowledge, in this paper, we provide the first fine-grained joint characterization of the geospatial and temporal dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio access network (containing location records) and the core network (containing traffic records) of a tier-1 cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial and temporal dynamics of application usage across different geographical regions. The results of our measurement study present cellular network operators with fine-grained insights that can be leveraged to tune network parameter settings for better network performance and user experience.

Disease Containment Strategies based on Mobility and Information Dissemination.

Human mobility and social structure are at the basis of disease spreading. Disease containment strategies are usually devised from coarse-grained assumptions about human mobility. Cellular networks data, however, provides finer-grained information, not only about how people move, but also about how they communicate. In this paper we analyze the behavior of a large number of individuals in Ivory Coast using cellular network data. We model mobility and communication between individuals by means of an interconnected multiplex structure where each node represents the population in a geographic area (i.e., a sous-préfecture, a third-level administrative region). We present a model that describes how diseases circulate around the country as people move between regions. We extend the model with a concurrent process of relevant information spreading. This process corresponds to people disseminating disease prevention information, e.g., hygiene practices, vaccination campaign notices and other, within their social network. Thus, this process interferes with the epidemic. We then evaluate how restricting the mobility or using preventive information spreading process affects the epidemic. We find that restricting mobility does not delay the occurrence of an endemic state and that an information campaign might be an effective countermeasure.

Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization

A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data. The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data. With this data-driven approach, our proposed approach is versatile: it is compatible with other data sources, and it is model agnostic and thus compatible with user equilibrium, system-optimum, Stackelberg concepts, and other models. Using a dimensionality reduction scheme, we design a projected gradient algorithm suitable for the proposed route flow estimation problem. The algorithm solves a block isotonic regression problem in the projection step in linear time. The accuracy, computational efficiency, and versatility of the proposed approach are validated on the I-210 corridor near Los Angeles, where we achieve 90% route flow accuracy with 1033 traffic sensors and 1000 cellular towers covering a large network of highways and arterials with more than 20,000 links. In contrast to long-term land use planning applications, we demonstrate the first system to our knowledge that can produce route-level flow estimates suitable for short time horizon prediction and control applications in traffic management. Our system is open source and available for validation and extension.

HRS Expert Consensus Statement on remote interrogation and monitoring for cardiovascular implantable electronic devices.

DavidSlotwiner,MD, FHRS, FACC(Chair),Niraj Varma,MD,PhD, FRCP(Co-chair), JosephG.Akar,MD,PhD, George Annas, JD, MPH, Marianne Beardsall, MN/NP, CCDS, FHRS, Richard I. Fogel, MD, FHRS, Nestor O. Galizio, MD, Taya V. Glotzer, MD, FHRS, FACC, Robin A. Leahy, RN, BSN, CCDS, FHRS, Charles J. Love, MD, CCDS, FHRS, FACC, FAHA, Rhondalyn C. McLean, MD, Suneet Mittal, MD, FHRS, Loredana Morichelli, RN, MSN, Kristen K. Patton, MD, Merritt H. Raitt, MD, FHRS, Renato Pietro Ricci, MD, John Rickard, MD, MPH, Mark H. Schoenfeld, MD, CCDS, FHRS, FACC, FAHA, Gerald A. Serwer, MD, FHRS, FACC, Julie Shea, MS, RNCS, FHRS, CCDS, Paul Varosy, MD, FHRS, FACC, FAHA, Atul Verma, MD, FHRS, FRCPC, Cheuk-Man Yu, MD, FACC, FRCP, FRACP From the Hofstra School of Medicine, North Shore Long Island Jewish Health System, New Hyde Park, New York, Cleveland Clinic, Cleveland, Ohio, Yale University School of Medicine, New Haven, Connecticut, Boston University School of Public Health, Boston, Massachusetts, Southlake Regional Health Centre, Newmarket, Ontario, Canada, St. Vincent Medical Group, Indianapolis, Indiana, Favaloro Foundation University Hospital, Buenos Aires, Argentina, Hackensack University Medical Center, Hackensack, New Jersey, Sanger Heart & Vascular Institute, Carolinas HealthCare System, Charlotte, North Carolina, New York University Langone Medical Center, New York City, New York, University of Pennsylvania Health System, Philadelphia, Pennsylvania, The Arrhythmia Institute at Valley Hospital, New York, New York, Department of Cardiovascular Diseases, San Filippo Neri Hospital, Rome, Italy, University of Washington, Seattle, Washington, VA Portland Health Care System, Oregon Health & Science University, Knight Cardiovascular Institute, Portland, Oregon, Johns Hopkins University, Baltimore, Maryland, Yale University School of Medicine, Yale-New Haven Hospital Saint Raphael Campus, New Haven, Connecticut, University of Michigan Congenital Heart Center, University of Michigan Health Center, Ann Arbor, Michigan, Brigham and Women’s Hospital, Boston, Massachusetts, Veterans Affairs Eastern Colorado Health Care System, University of Colorado, Denver, Colorado, and Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China.

A smartphone app to extend use of a cloud-based irrigation scheduling tool

Irrigation in Colorado, a headwaters state, is crucial for viable agricultural production; consequently, with the foreseen population growth, there will become a greater demand placed on precious water resources. Technology must be adopted and embraced as part of the solution to water shortage. Researchers at Colorado State University have created an online evapotranspiration-based irrigation scheduling tool called Water Irrigation Scheduling for Efficient Application (WISE) that uses the soil water balance method and data queries from Colorado Agricultural Meteorological Network (CoAgMet) and Northern Colorado Water Conservation District (NCWCD) weather stations. To expedite and mobilize required user interaction with the software interface, a smartphone app has been developed that allows users to quickly view their soil moisture deficit, weather measurements, and the ability to input applied irrigation amounts into WISE. Potential users: agricultural producers, irrigation managers, and research scientists, will benefit from this app as it allows lite access to the tool from any location within a cellular data network. Technology such as the scheduling tool and smartphone app, when adopted within Colorado and the western United States, allow irrigators another tool to better utilize water resources.

Modeling and mining the temporal patterns of service in cellular network

Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service (QoE) support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator's network in China. We propose a Zipf's ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method (Self-Organizing Map combined with kmeans) is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.

Cellpath: Fusion of Cellular and Traffic Sensor Data for Route Flow Estimation via Convex Optimization

A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data. The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data. With this data-driven approach, our proposed approach is versatile: it is compatible with other data sources, and it is model agnostic and thus compatible with user equilibrium, system- optimum, Stackelberg concepts, and other models. Using a dimensionality reduction scheme, we design a projected gradient algorithm suitable for the proposed route flow estimation problem. The algorithm solves a block isotonic regression problem in the projection step in linear time. The accuracy, computational efficiency, and versatility of the proposed approach are validated on the I-210 corridor near Los Angeles, where we achieve 90% route flow accuracy with 1033 traffic sensors and 1000 cellular towers covering a large network of highways and arterials with more than 20,000 links. In contrast to long-term land use planning applications, we demonstrate the first system to our knowledge that can produce route-level flow estimates suitable for short time horizon prediction and control applications in traffic management. Our system is open source and available for validation and extension.

Identifying traffic differentiation on cellular data networks

The goal of this research is to detect traffic differentiation in cellular data networks. We define service differentiation as any attempt to change the performance of network traffic traversing an ISP's boundaries. ISPs may implement differentiation policies for a number of reasons, including load balancing, bandwidth management, or business reasons. Specifically, we focus on detecting whether certain types of network traffic receive better (or worse) performance. As an example, a wireless provider might limit the performance of third-party VoIP or video calling services (or any other competing services) by introducing delays or reducing transfer rates to encourage users to use services provided by the wireless provider. Previous work explored this problem in limited environments. Glasnost focused on BitTorrent in the desktop/laptop environment, and lacked the ability to conduct controlled experiments to provide strong evidence of differentiation. NetDiff covered a wide range of passively gathered traffic from a large ISP but likewise did not support targeted, controlled experiments. We address these limitations with Mobile Replay.

Recognizing and characterizing dynamics of cellular devices in cellular data network through massive data analysis

SummaryThe user clients for accessing Internet are increasingly shifting from desktop computers to cellular devices. To be competitive in the rapidly changing market, operators, Internet service providers and application developers are required to have the capability of recognizing the models of cellular devices and understanding the traffic dynamics of cellular data network. In this paper, we propose a novel Jaccard measurement‐based method to recognize cellular device models from network traffic data. This method is implemented as a scalable paralleled MapReduce program and achieves a high accuracy, 91.5%, in the evaluation with 2.9 billion traffic records collected from the real network. Based on the recognition results, we conduct a comprehensive study of three characteristics of network traffic from device model perspective, the network access time, the traffic volume, and the diurnal patterns. The analysis results show that the distribution of network access time can be modeled by a two‐component Gaussian mixture model, and the distribution of traffic volumes is highly skewed and follows the power law. In addition, seven distinct diurnal patterns of cellular device usage are identified by applying unsupervised clustering algorithm on the collected massive traffic data. Copyright © 2014 John Wiley & Sons, Ltd.

Characterizing and Modeling Microblog Traffic in Cellular Data Network Based on Massive Data Analysis

In this paper, we present an approach to characterize and model microblog traffic in cellular data network. In contrast to previous methods, our approach is based on the cloud computing platform and the cluster system, including the Hadoop Distributed File System (HDFS) and the parallel processing software framework MapReduce. Whats more, we focus on the contrast of Sina and Tencent microblogs. We analyze the features of microblog traffic in four aspects of increasing details, which are (i) traffic diurnal pattern, (ii) modeling the traffic distribution, (iii) user distribution, (iv) diversity usage of microblogs. This approach of analyzing microblog traffic comprehensively is probably the most important contribution of this paper. Furthermore, our approach has two important features. First, the massive mobile subscriber data we used in our experiments was collected from a commercial Internet Service Provider (ISP) covering an entire province in Southern China. Therefore, it ensures the results indicate the true characteristics of microblog traffic in network. Second, we investigate that the microblog traffic fits with the power law distribution. We demonstrate the electiveness of our approach on three real datasets. Our results are important for cellular network operators to learn user behavior and optimize the future microblog application designs.

Quality of Service Evaluation of a Deployed 3G Data Network for a Campus wide e-Learning Platform

Ghana, the increase in the adoption of e-learning methodologies is improving the way teachers and students interact. With the combination of internet, multimedia and network technologies, e-learning is changing typical teaching and learning using its methodologies to improve access to education on many university campuses. A third generation (3G) data network has been deployed on Kwame Nkrumah University of Science and Technology (KNUST) campus with the aim of improving connectivity for students and lecturers to enable them access an implemented e-learning platform anywhere on the university campus. This paper evaluates the Quality of Service (QoS) provided by the 3G cellular data network. It addresses the gap between the technical capabilities and the QoS experienced by students and teachers who use the 3G network to access the platform. The analysis in the paper is based on live data which were collected on the network. An Analytic Hierarchy Process (AHP) methodology has been used to assess the QoS based on selected Key Performance Indicators (KPIs). Keywords-Learning; Quality of service; Network Connectivity; Third

Measuring and Simulating Cellular Switching System IP Traffic

The increasing popularity of smartphones and other data-enabled cellular devices has led to a rapid expansion of cellular data networks worldwide. These networks are constantly being improved to provide new functionality, improved capacity, and better reliability. Optimization, testing, and troubleshooting of these systems are all important areas of concern within the cellular network industry. Large amounts of engineering resources are devoted to these topics, with the result that analysis techniques and best practices are constantly being improved. This paper details recent work regarding the analysis of cellular data device behavior in a live system, and the design of a high performance load generator to simulate this behavior for use in system testing and debugging. Internet Protocol (IP) packet capture data is analyzed using a simple graph-based analysis technique that provides a detailed characterization of device behavior. This characterization is then used in the design of a second generation high performance load generator. ® 2014 Alcatel-Lucent.