Mobile Data Networks Research Articles

Mobility during the COVID-19 Pandemic: A Data-Driven Time-Geographic Analysis of Health-Induced Mobility Changes

The COVID-19 pandemic has profoundly affected the spatial mobility of a major part of the population in many countries. For most people, this was an extremely disruptive shock, resulting in loss of income, social contact and quality of life. However, forced to reduce human physical interaction, most businesses, individuals and households developed new action lines and routines, and were gradually learning to adapt to the new reality. Some of these changes might result in long-term changes in opportunity structures and in spatial preferences for working, employment or residential location choice, and for mobility behavior. In this paper we aim to extend the time-geographic approach to analyzing people’s spatial activities, by focusing on health-related geographical mobility patterns during the pandemic in Sweden. Starting from a micro-approach at individual level and then looking at an aggregate urban scale, we examine the space-time geography during the coronavirus pandemic, using Hägerstrand’s time-geography model. We utilize a massive but (location-wise) fuzzy dataset to analyze aggregate spatiotemporal impacts of the COVID-19 pandemic using a contemporary time-geographical approach. First, we address micro-level behavior in time-space to understand the mechanisms of change and to illustrate that a temporal drastic change in human mobility seems to be plausible. Then we analyze the changes in individuals’ mobility by analyzing their activity spaces in aggregate using mobile phone network data records. Clearly, it is too early for predicting long-term spatial changes, but a clear heterogeneity in spatial behavior can already be detected. It seems plausible that the corona pandemic may have long-lasting effects on employment centers, city roles and spatial mobility patterns.

Detecting Home and Work Locations from Mobile Phone Cellular Signaling Data

Obtaining the distribution of home and work locations is essential for city planning, as it defines the structure and mobility pattern of a city. With the development of telecommunication networks, mobile network data, having the advantages of large coverage and strong followability, have produced large amounts of information about human activities. Thus, it has become a popular research subject for human position detection. In this study, we proposed a new method to detect home and work locations based on the extraction of focal points in traces, identifying an individual’s working and resting hours, and analyzing the characteristics of city grids using mobile phone cellular signaling data (CSD). At the individual level, we validated the algorithm on ground-truth volunteer data and achieved a small deviation of under 500 and 565 m for home and work location detection 85% of the time. At the aggregate level, we tested it on a city-wide anonymized CSD set and found a high Pearson correlation between our result and the census data of 0.93. Compared to existing studies, this study improved the granularity and location accuracy of home and work location detection, as well as validated the method using both individually labeled ground-truth data and aggregate data for the first time. Applying the algorithm in a city, we captured the population distribution, commuting patterns, and job-housing balance of the city and demonstrated the potential in using mobile network data for urban planning and policy formulation.

An Analysis of Renewable Energy Usage by Mobile Data Network Operators

The exponential growth in mobile data traffic has resulted in massive energy usage and therefore has increased the carbon footprint of the Internet. Data network operators have taken significant initiatives to mitigate the negative impacts of carbon emissions (CE). Renewable Energy Sources (RES) have emerged as the most promising way to reduce carbon emissions. This article presents the role of renewable energy (RE) in minimizing the environmental impacts of mobile data communications for achieving a greener environment. In this article, an analysis of some selected mobile data network operators’ energy consumption (EC) has been presented. Based on the current statistics of different mobile network operators, the future energy values are estimated. These estimations of carbon emissions are based on the predicted data traffic in the coming years and the percentage consumption of energy from renewable sources by the network operators. The analysis presented in this article would be helpful to develop and implement energy policies that accelerate the process of increasing the renewable shares in total energy requirements. Incrementing the share of renewable energy in total energy requirements can be a way forward to reach Goal 7 of the United Nations Sustainable Development Goals (SDGs).

Contract-Less Mobile Data Access Beyond 5G: Fully-Decentralized, High-Throughput and Anonymous Asset Trading Over the Blockchain

The new “status quo” created by the emerging integration of Multi-Access Edge Computing (MEC) and Radio Access Network (RAN) infrastructures, combined with the openness of the 5G service market, have set new standards on how the multitude of 5G service domains should be incentivized and coordinated to comply with the performance requirements set on a per session basis. In this paper, we revisit the predominant offline contract-driven mobile data access model enabling users to gain access to the infrastructure of only a few network operators and propose a new blockchain-backed mobile data access model where the key 5G stakeholders can trade, share and consume mobile edge network assets (access to the internet, spectrum, processing, storage, local content etc.) in a fully decentralized, anonymous and highly-robust fashion. Blockchain-backed mobile data access should address critical practical challenges of blockchain decentralization, scalability and security in the context of 5G and Beyond networks. Accordingly, we provide a meticulous survey of existing solutions in the aforementioned three areas and present a comprehensive holistic framework of protocols to address the key challenges identified, including a delegated Proof-of-Stake (DPoS) for distributed blockchain consensus over 5G and Beyond mobile data networks, a future-proof smart-contract enabled payment relay service enabling instant off-chain payments in a highly robust fashion as well as a hybrid mixing protocol that enables payment relays to act as anonymous payment hubs while addressing the unique challenges opposed by the joint blockchain and network level interaction of the 5G and Beyond service components. A wide range of practical implications and mitigation measures for dishonest operation of the blockchain nodes are investigated and sophisticated yet highly-robust incentive engineering mechanisms are derived. Detailed numerical results also accompany the paper, bringing to light the unique performance trade-offs and valuable design guidelines towards blockchain-backed mobile data access for 5G and Beyond mobile data networks.

Towards a modular end-to-end statistical production process with mobile network data

Mobile network data has proved to be an outstanding data source for the production of statistics in general, and for Official Statistics, in particular. Similarly to another new digital data sources, this poses the remarkable challenge of refurbishing a new statistical production process. In the context of the European Statistical System (ESS), we substantiate the so-called ESS Reference Methodological Framework for Mobile Network Data with a first modular and evolvable proposed statistical process comprising (i) the geolocation of mobile devices, (ii) the deduplication of mobile devices, (iii) the statistical filtering to identify the target population, (iv) the aggregation into territorial units, and (v) the inference to the target population. The proposal is illustrated with synthetic data generated from a network event data simulator developed for these purposes.

Investigation and Technological Comparison of 4G and 5G Networks

Mobile cellular data networks have allowed users to access the Internet whilst on the move. Many companies use this technology in their products. Examples of this would be Smart Meters in the home and Tesla cars having their “over the air updates”. Both of these two companies use the 4G and 5G technology. So this report will include a technical overview of the technology and protocols (LTE Advanced) used in 4G and 5G networks and how they provide services to the user and how data is transferred within the networks. And there are lots of different parts about the network architecture between the 4G and 5G systems. This report will talk about some different parts between these two systems and some challenges in them.

Cooperative Content Caching in MEC-Enabled Heterogeneous Cellular Networks

Multimedia content delivery via the cellular infrastructure increases fast due to the very high volumes of mobile video traffic generated by the billions of end devices populating the mobile data network. A critical mass of mobile video content requests refers to the consumption of the same popular video content, which is consumed by different end terminals spanning small geographical regions. Such content requests place a great burden on the backhaul of content-agnostic cellular networks, which fail to exploit the correlation of video requests to decongest their backhaul links. This creates redundant retransmissions while fetching the same video content from a central server to the network edge, using the bandwidth-limited backhaul at peak-time periods. With the integration of multi-access edge computing (MEC) capabilities in 5G mobile cellular networks, mobile network operators can place popular video content closer to the network edge at off-peak time periods, predicting user requests exhibiting a high correlation for a given time interval over smaller geographical regions. In this paper, we investigate popular content placement in multi-tier heterogeneous cellular networks where the edge network infrastructure can cooperate to create content delivery (and placement) clusters to effectively serve correlated video requests. To this end, we model the cooperative content placement problem using the multiple knapsack problem (MKP) formulation and present an exact (optimal) bound-and-bound strategy to solve it. The performance of the proposed strategy is evaluated in-depth using extensive system-level simulations and is compared against that of other state-of-the-art algorithms. Valuable design guidelines and key performance trade-offs are discussed, paving the way towards cluster-based cooperative caching in MEC-enabled cellular network setups.

A Quantitative and Comparative Evaluation of Key Points Selection Algorithms for Mobile Network Data Sets Analysis

In recent years, network operators are receiving an outsize amount of data due to the increasing number of mobile network subscribers, network services and device signalling. This trend increases with the deployment of 5G that will provide advanced connectivity to wireless devices and develop new services. Network analytics must allow telecommunications operators to improve their services and the infrastructure extracting useful information from large amounts of data. A methodology based on orthogonal projections was developed in order to analyze the network information and facilitate the management and the operations to network providers. In the current study, different key points selection algorithms are investigated in order to make a quantitative and qualitative evaluation and analyze the performance of those algorithms which use different approaches to select these points, which will be utilized in the methodology. A novel synthetic data set has also been developed to statistically evaluate the effect of the key points selection algorithms in the clustering, as well as, measure the performance of the aforementioned methodology. Finally, these key points selection algorithms are used in a real scenario to evaluate the impact of the different approaches in the analysis.

Using mobile big data to support emergency preparedness and address economically vulnerable communities during the COVID-19 pandemic in Nigeria

AbstractWith the declaration of the coronavirus disease 2019 (COVID-19) pandemic in Nigeria in 2020, the Nigeria Governors’ Forum (NGF) instigated a collaboration with MTN Nigeria to develop data-driven insights, using mobile big data (MBD) and other data sources, to shape the planning and response to the pandemic. First, a model was developed to predict the worst-case scenario for infections in each state. This was used to support state-level health committees to make local resource planning decisions. Next, as containment interventions resulted in subsistence/daily paid workers losing their income and ability to buy essential food supplies, NGF and MTN agreed a second phase of activity, to develop insights to understand the population clusters at greatest socioeconomic risk from the impact of the pandemic. This insight was used to promote available financial relief to the economically vulnerable population clusters in Lagos state via the HelpNow crowdfunding initiative. This article discusses how anonymized and aggregated mobile network data (MBD), combined with other data sources, were used to create valuable insights and inform the government, and private business, response to the pandemic in Nigeria. Finally, we discuss lessons learnt. Firstly, how a collaboration with, and support from, the regulator enabled MTN to deliver critical insights at a national scale. Secondly, how the Nigeria Data Protection Regulation and the GSMA COVID-19 Privacy Guidelines provided an initial framework to open the discussion and define the approach. Thirdly, why stakeholder management is critical to the understanding, and application, of insights. Fourthly, how existing relationships ease new project collaborations. Finally, how MTN is developing future preparedness by creating a team that is focused on developing data-driven insights for social good.

Spatial and Temporal Exposure Assessment to PM2.5 in a Community Using Sensor-Based Air Monitoring Instruments and Dynamic Population Distributions

This research was to conduct a pilot study for two consecutive days in order to assess fine particulate matter (PM2.5) exposure of an entire population in a community. We aimed to construct a surveillance system by analyzing the observed spatio-temporal variation of exposure. Guro-gu in Seoul, South Korea, was divided into 2,204 scale grids of 100 m each. Hourly exposure concentrations of PM2.5 were modeled by the inverse distance weighted method, using 24 sensor-based air monitoring instruments and the indoor-to-outdoor concentration ratio. Population distribution was assessed using mobile phone network data and indoor residential rates, according to sex and age over time. Exposure concentration, population distribution, and population exposure were visualized to present spatio-temporal variation. The PM2.5 exposure of the entire population of Guro-gu was calculated by population-weighted average exposure concentration. The average concentration of outdoor PM2.5 was 42.1 µg/m3, which was lower than the value of the beta attenuation monitor measured by fixed monitoring station. Indoor concentration was estimated using an indoor-to-outdoor PM2.5 concentration ratio of 0.747. The population-weighted average exposure concentration of PM2.5 was 32.4 µg/m3. Thirty-one percent of the population exceeded the Korean Atmospheric Environmental Standard for PM2.5 over a 24 h average period. The results of this study can be used in a long-term aggregate and cumulative PM2.5 exposure assessment, and as a basis for policy decisions on public health management among policymakers and stakeholders.

Li-Fi delivering broad band and multimedia content under dimming in train

This paper focuses in delivering broad band and media content to numerous users while traveling in the train through light fidelity (Li-Fi), an innovative technology under Visible Light Communication (VLC). In our daily life, there is increasing traffic request for open Radio Frequency (RF) to support the users. In RF, even recurrence and spatial reuse of spectrum not able to support in conveying broad band and multimedia content to the internet users. For high-capacity mobile data networks, the VLC can balance the RF-based mobile communication systems. VLC conserves energy through dimming. Dimmed Light Emitting Diode (LED) luminaries provide visual comfort with appropriate optics while accessing internet. In the proposed work, LED dimming is achieved through digital dimming modulation technique, Variable Delta Sigma Modulation. The broad band and multimedia content can be delivered faster through Reverse Polarity Optical Orthogonal Frequency Division Multiplexing modulator to the internet users under dimming with better bit error rate and bit rate.

An IoT-Based School Bus and Vehicle Tracking System Using RFID Technology and Mobile Data Networks

The growing concerns of families about the safety and security of their children prompted a major interest in developing robust systems that offer efficient tracking and monitoring of children commuting between homes and schools. With the emergence of the Internet of Things (IoT) technology, in addition to Radio Frequency Identification (RFID), developing such systems became feasible and cost-effective. In this paper, we present the design and implementation of a comprehensive low-cost system based on IoT that allows schools, parents, and authorities to track the movement of children while in school buses or being transported in private vehicles in real time. The system is based on off-the-shelf passive RFID readers that are installed within buses, next to bus stations, and pick-up points at school entrances. We have implemented the system as an application connected to a MySQL database deployed over the Heroku’s versatile cloud platform. The different stakeholders (parents and school administrators) connect to the system through the Internet using a secure Java GUI. The parents can track their children along the bus route in real time. The system is built to integrate children monitoring within the buses, the tracking of the buses, and the tracking of private vehicles transporting children to/from school. The developed system provides additional security features such as access to safety records of bus/vehicle drivers and infringements. A variety of reports for different activities of buses and vehicles can be generated in real time at the school level. The system was tested successfully over the King Fahd University of Petroleum and Minerals large campus which includes several schools (kindergarten, primary, and secondary).

Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling.

Mobile sensing data has become a popular data source for geo-spatial analysis, however, mapping it accurately to other sources of information such as statistical data remains a challenge. Popular mapping approaches such as point allocation or voronoi tessellation provide only crude approximations of the mobile network coverage as they do not consider holes, overlaps and within-cell heterogeneity. More elaborate mapping schemes often require additional proprietary data operators are highly reluctant to share. In this paper, I use human settlement information extracted from publicly available satellite imagery in combination with stochastic radio propagation modelling techniques to account for that. I show in a simulation study and a real-world application on unemployment estimates in Senegal that better coverage approximations do not necessarily lead to better outcome predictions.

Mode choice and spatial distribution in long-distance passenger transport – Does mobile network data deliver similar results to other transportation models?

Sound knowledge of travel behavior is important for many findings and decisions in science, business and transport policy. This is not only about spatial distribution, but also and above all about the choice of transport mode. Anonymized mobile network data (MND) can provide valuable information here. This relatively new method is fundamentally superior to other approaches in many respects, but a comprehensive scientific evaluation is still pending. In this context, the present study should help to close research gaps by comparing the results of an MND analysis with the output of an existing transportation model based on System Dynamics. The comparison includes the number of trips within one year, the choice of means of transport and the spatial distribution in long-distance passenger transport within Germany (for all trips with a distance of 50 km or more). We found that the results for travel frequency and modal split were very similar at high aggregation (total annual values for the study area). Also, for distance categories (with an interval of 100 km each) the deviations were very small. However, there were significant differences for origin-destination pairs. In order to be able to make a well-founded comparison here, more in-depth research is necessary in the future.

Monitoring travel patterns in German city regions with the help of mobile phone network data

ABSTRACT This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning. Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes, a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access. Concepts need to serve the evaluation of policy objectives, for example in regional or local area plans. In this study, we, therefore, extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns. To accomplish this, we link mobile phone records with urban classifications and transport network data, using both visual and computational approaches to mine the data. The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany, testing hypotheses of transit-oriented regional development, as well as testing for congestion risks in the transport network. The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data.

Unveiling large-scale commuting patterns based on mobile phone cellular network data

In this study, with Estonia as an example,we established an approach based on Hidden Markov Model to extract large-scale commuting patterns at different geographical levels using a massive amount of mobile phone cellular network data, which is referred to as Call detail record (CDR). The proposed model is designed for reconstructing and transforming the trajectories extracted from the CDR data. This step allowed us to perform origin-destination matrix extraction among different geographical levels, which helped in depicting the commuting patterns. Besides, we introduced different techniques for analyzing the commuting at the urban level. Our results unveiled that there is great potential behind mobile data of the cellular networks after transforming it into meaningful mobility patterns. That can easily be used for understanding urban dynamics, large-scale daily commuting and mobility. The aggressive development and growth of ubiquitous mobile sensing have generated valuable data that can be used with our approach for providing answers and solutions to the growing problems of transportation, urbanization and sustainability.

Key Enabling Technologies for the Post-5G Era: Fully Adaptive, All-Spectra Coordinated Radio Access Network with Function Decoupling

With the rapidly growing bandwidth demand for wireless applications, new system technologies related to post-5G are emerging. In this article, an all-spectra fully adaptive and coordinated radio access network (RAN) is reported and discussed. By employing a fiber-wireless integration and networking architecture, all data-carrying channels could be aggregated in the same fiber access infrastructure. This enables a coordinated RAN with function decoupling, in which lower RF, 5G New Radio (NR), sub-THz, and even lightwave are employed; also, different types of services are delivered depending on their physical layer properties. Promising scenarios are discussed, such as integrated access of wireless NR-free space optical (FSO) backhauling and indoor systems via visible light communication (VLC) and efficient NR beamforming aided by VLC positioning system. The former use case can enhance the network throughput and reliability. This is because both FSO and NR can support high channel capacity due to their abundant bandwidth. Meanwhile, with the advancement of novel DSP techniques, the stability of the NR-FSO link under diverse weather turbulences or suffering from burst mode interference can be enhanced. The latter scenario provides an alternative solution for high-speed data link and a simplified beam management via the VLC-aided positioning system. VLC can concurrently provide ubiquitous indoor illumination, data transmission, and positioning. With the help of artificial intelligence algorithms, a VLC-based precision positioning system can provide a location accuracy of less than 1 cm, and it is able to meet the narrow beam size of the NR beamformer in a 3D model. Therefore, it is foreseeable that an all-spectra function decoupled RAN can serve as a unified network platform to support all wireless applications while optimizing system throughput, channel condition, network coverage, and software/ hardware complexity for post-5G mobile data networks.

Examining differences in perceptions of trust, privacy and risk in home and public Wi-Fi internet channels

Purpose Research of people’s perceptions of trust, privacy and risk on the internet has generally neglected the impact of the variety of channels used to access the internet. People primarily access the internet using internet channels at home, work, public Wi-Fi (hotspots) or through their mobile data network. The technology infrastructure of each of these channels combined with the vulnerabilities of the environment may form different perceptions, as it relates to trust, privacy and risk. The purpose of this study is to understand how people perceive the home and public Wi-Fi channel from a trust, privacy and risk perspective. Design/methodology/approach Adapting existing trust, privacy and risk scales, the authors conducted a survey of people’s perceptions, as it relates to home and public Wi-Fi internet channels. Findings The results of this study suggest significant differences in people’s perception of trust and risk depending on an internet channel. However, with regard to privacy, the results of this study provide non-conclusive, yet intriguing, outcomes motivating the need for future studies. Originality/value To the best of the authors’ knowledge, this is the first study that parses out people’s perceptions of trust, privacy and risk, as it pertains to specific internet channels. The authors expect future research to benefit from their findings of how different channel perceptions influence people’s online activities.

RAN-aware adaptive video caching in multi-access edge computing networks

Videos are expected to be a primary contributor to an anticipated massive surge in mobile network data. Caching the videos within the mobile network can significantly reduce the network load and Operational Expenditure (OPEX) for mobile network operators. Multi-access Edge Computing (MEC) can enable the video caching by providing processing and storage capabilities within the network. However, content providers usually employ Dynamic Adaptive Streaming over HTTP (DASH) for video streaming, which contains multiple bit-rate representations of videos. Constrained by its capacity, MEC can not cache all representations of popular videos. Video transcoding mitigates this issue to a certain extent by converting the higher available video bit-rate to a requested lower one; but, it can quickly exhaust the available edge processing power by transcoding a large number of videos in parallel. Therefore, caching appropriate video bit-rates that can serve the maximum number of users in the network is a non-trivial problem. To resolve this problem and to efficiently utilize the resources (processing and storage) at the network edge, we took a non-traditional approach for video caching that utilizes the network information provided by MEC's Radio Network Information (RNI) Application Program Interface (API). In particular, RNI API provides Radio Access Network (RAN) status information that can be employed to estimate the probability distribution of requested video qualities. In this work, we formulate the video caching problem as an Integer Linear Programming (ILP) for the hit-rate maximization. Since the optimization problem requires the knowledge of all future requests, it obviously cannot be used in real-time. Therefore, we develop a RAN-aware Adaptive VidEo cachiNg (RAVEN) method that uses network information to make an informed decision for video bit-rate selection in video caching coupled with transcoding and maximizes the number of served users form the network edge. Simulation results demonstrate that the RAVEN significantly outperforms state-of-the-art algorithms in the domain and performs closer to the optimal solution.

Network Big Data Analysis and Mining Based on DPI Technology

In recent years, the rapid development of mobile data network, with 4G commercial wireless network has entered the data era, more and more big data impact on the network of mobile operators, network monitoring and network security need to be strengthened. Depth packet detection technology DPI. The purpose of this paper is to complete network data analysis by in-depth identification of key words in IP packets, so as to meet the current demand of operators for network analysis. In this paper, DPI technology is applied to the actual network, and the DPI data analysis system is constructed by combining the 2G and 3G networks of China mobile. The key technologies in DPI include character recognition, behavior pattern recognition and application layer gateway recognition. At the end of the experiment, the mass signaling data from GB interface and iu-ps interface were obtained to effectively identify various applications and data mining in the network. This paper expounds big data analysis and mining technology and its decision application research from different aspects, hoping to provide theoretical reference for experts and scholars studying the application of big data analysis and mining technology.