Mobile Data Usage Research Articles

An optimization scheme of data link security transmission based on mobile edge computing

For the past few years, the security of data transmission links has been increasingly valued. However, with the increasing requirements for the security performance of data transmission links, how to cut the cost of upgrading while improving security has become a serious challenge. In response to the issue of ensuring low-cost and secure data transmission in wireless communication environments with eavesdroppers, this article innovatively starts with idle roadside units and studies how to incentivize roadside units to transmit interference power to interfere with eavesdroppers, thereby efficiently and economically improving the security performance of mobile user data transmission. Firstly, the impact of roadside unit transmission interference power on the security performance of data transmission links was analyzed by establishing a theoretical model; Then, this paper proposes a three-layer model consisting of ground user groups, eavesdropping users, roadside units, and UAVs equipped with mobile edge computing servers. In this model, by encouraging idle roadside units to send interference power, ground users are assisted to safely unload their own data to mobile edge computing servers. Meanwhile, considering the selfishness of idle roadside units, an improved algorithm based on simulated annealing algorithm is proposed to achieve the optimal mapping between ground users and idle roadside units. Finally, through the analysis of simulation results, this scheme can achieve the goal of reducing system costs while improving the security of data transmission.

A Subsidization Scheme for Maximizing Social Welfare in Mobile Communications Markets

In contemporary mobile communications markets, various agents or players interact to pursue welfare. Regulatory policies enacted by governments in certain markets aim to maximize social welfare. However, some countries, both least developed and developing, often adopt successful models from developed nations without local market validation. Therefore, network economics serves as a pertinent framework for analyzing such policies. This paper introduces a novel scheme based on constrained optimization problems, where the constraints represent multilevel economic game equilibria within a system model involving three agents: the central planner, the mobile network operator, and the mobile data users. These agents strategically optimize their payoff functions by considering benefit factors and decision variables such as the subsidization factor, pricing, and data consumption. To this end, a three-stage dynamic game is proposed to model the players’ interactions, employing the backward induction method to ascertain the subgame perfect equilibrium from the Nash equilibrium. A case study is presented, demonstrating a 31.16% increase in social welfare between scenarios involving no adoption of the subsidization factor and its adoption at the optimal value when the central planner enacts it to other players in the game, even if they do not necessarily attain maximum payoff values. In countries aligning with this proposed model, social welfare is maximized through a subsidization scheme.

Five Disruptive Technologies in 6G to Support Digital Twin Networks

With the expected explosive growth of the number of mobile user devices and data traffic in the next decade and beyond, and the expected development of various new applications and services such as vehicle-to-everything, extended reality and smart city, the requirements for Quality of Service (such as ultra-high data transmission rate, ultra-low latency, support for the highest moving speed and seamless connection, etc.) will reach levels never seen before. The sixth generation (6G) of networks is conceived to address the requirements mentioned above. Digital Twin refers to a digital representation to represent a physical entity in virtual space while its corresponding physical object remains in the physical space. In this article, we first briefly introduce some basic knowledge of Digital Twins. We then outline possible innovative technologies expected in 6G networks that will help Digital Twin systems to reach their full potential and deliver the high-level performance needed for the next generation of communications.

Analyzing mobile app data demand and usage behavior in Sri Lanka

Owing to the rapid advancement of mobile computing, daily human practices have become more efficient and convenient. Consequently, the number of mobile app users has increased drastically over the last ten years, and a significant amount of energy is consumed by those mobile apps where the energy consumption depends on the data usage of the apps. Therefore, it is important to optimize data consumption to ensure sustainable digitalization. Mobile app personalization is one of the vital areas to optimize the data consumption of mobile apps. In this research, mobile app data usage and user experience data were collected and analyzed quantitatively and qualitatively. The data were collected from 40 Android smartphone users in Sri Lanka. Primarily, semi-structured interviews were conducted to collect qualitative data to identify mobile app usage patterns. In addition, data usage of each mobile app was collected using the in-built data usage tracker in Android smartphones for four months. Descriptive and correlation analyses were used to analyze quantitative data, whereas thematic and content analyses drew insights from the collected qualitative data. Our findings show the most data-demanding mobile app categories in terms of data consumption as social media, video players & editors, and communication. Moreover, users aged between 21 to 30 contribute to the high data consumption in mobile app usage. In addition, qualitative analysis shows that app usage behavior varied during and after the pandemic, and elderly (51-65 years) mobile app users use only a limited number of features from an app.

A State-of-the-Art Review on CMOS Radio Frequency Power Amplifiers for Wireless Communication Systems.

Wireless communication systems have undergone significant development in recent years, particularly with the transition from fourth generation (4G) to fifth generation (5G). As the number of wireless devices and mobile data usage increase, there is a growing need for enhancements and upgrades to the current wireless communication systems. CMOS transceivers are increasingly being explored to meet the requirements of the latest wireless communication protocols and applications while achieving the goal of system-on-chip (SoC). The radio frequency power amplifier (RFPA) in a CMOS transmitter plays a crucial role in amplifying RF signals and transmitting them from the antenna. This state-of-the-art review paper presents a concise discussion of the performance metrics that are important for designing a CMOS PA, followed by an overview of the trending research on CMOS PA techniques that focuses on efficiency, linearity, and bandwidth enhancement.

Shuffle Differential Private Data Aggregation for Random Population

Bridging the advantages of differential privacy in both centralized model (i.e., high accuracy) and local model (i.e., minimum trust), the shuffle privacy model has potential applications in many privacy-sensitive scenarios, such as mobile user data aggregation and federated learning. Since messages from users are anonymized by semi-trusted shufflers (e.g., anonymous channels, edge servers), every user could hide message among other users’ messages and inject only part of noises (a.k.a. privacy amplification). However, existing works assume that the participating user population is known in advance, which is unrealistic for dynamic environments (e.g., mobile computing, vehicular networks). In this work, we study the shuffle privacy model with a random participating population, and give privacy amplification bounds for population size with commonly encountered binomial, Poisson, sub-Gaussian distribution and etc. For further improving accuracy, we formulate and derive optimal dummy sizes for both non-adaptive and adaptive dummies. Finally, to break the error barrier due to the constraint of sending one single message per user, we design a multi-message shuffle private protocol supporting random population. Experiment results show that our approaches reduce more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$60\%$</tex-math></inline-formula> error when compared to the local model and naive approaches. We hope this work provides tailored solutions of shuffle privacy for dynamic mobile/distributed computing.

Mobile phone-enabled internet services and the technology readiness of the users

At a time the ecosystem of mobile phones continues to evolve, this study investigates the impact of the users’ technology readiness (TR) and perceived value (PV) of mobile phone-enabled internet usage (MPEIU). The primary aim of this study is to examine the potential roles of TR and the dimensions of PV as antecedents of mobile data usage and to contribute to the debate on the determinants of technology acceptance and usage by individuals. The perceived value of mobile phone-enabled internet is conceptualised under five dimensions, viz: (1) utilitarian value, (2) hedonic value, (3) uniqueness value, (4) epistemic value, and (5) economic value, in a survey conducted by using a validated questionnaire, with a sample of 550 adult mobile phone users in the Central Province of Sri Lanka. The data analysis employs covariance based structural equation modelling (CB-SEM) analytical process and the findings reveal that their adoption intention (AI) has a direct positive influence on the MPEIU. It is discovered that the utilitarian, epistemic, and hedonic value dimensions of MPEIU indicate a direct influence on their AI while TR indicates an indirect influence on it. The findings imply that the generic strategic approaches (i.e., cost and differentiation) to mobile internet services may be an ineffective solution and suggest that the focus should shift towards improving the utilitarian, epistemic, and hedonic components of mobile internet services. Accordingly, the users with a higher level of TR indicated an affinity to have a higher AI and higher usage. Therefore, the user’s TR is recommended here as the basis for market segmentation.

The spatiotemporal prediction method of urban population density distribution through behaviour environment interaction agent model

Based on the interrelationship between the built environment and spatial–temporal distribution of population density, this paper proposes a method to predict the spatial–temporal distribution of urban population density using the depth residual network model (ResNet) of neural network. This study used the time-sharing data of mobile phone users provided by the China Mobile Communications Corporation to predict the time–space sequence of the steady-state distribution of population density. Firstly, 40 prediction databases were constructed according to the characteristics of built environment and the spatial–temporal distribution of population density. Thereafter, the depth residual model ResNet was used as the basic framework to construct the behaviour–environment agent model (BEM) for model training and prediction. Finally, the average percentage error index was used to evaluate the prediction results. The results revealed that the accuracy rate of prediction results reached 76.92% in the central urban area of the verification case. The proposed method can be applied to prevent urban public safety incidents and alleviate pandemics. Moreover, this method can be practically applied to enable the construction of a “smart city” for improving the efficient allocation of urban resources and traffic mobility.

Location of fog nodes mounted on fixed-wing UAVs

Fog computing complements cloud computing by enabling a broad range of latency-sensitive applications. To make it feasible, fog nodes with processing capabilities are located in different regions to cope with variable demands in time and space. The fog nodes are usually fixed terrestrial infrastructures providing reliability and easy management; however, these nodes cannot cope with the heavy processing demands even for a limited time in a specific area. To help circumvent these limitations, the fog nodes can be mounted on unmanned aerial vehicles (UAVs) that can be promptly dispatched to cope with a peak of demands. Previous work investigated the use of rotary-wing UAVs as fog nodes. This paper formulates the fog node location problem considering fixed-wing UAVs. Such UAVs have an extended operation time, making them great assets for the improvement of service delivered to end-users. This paper models the location problem as an integer linear programming formulation and presents the Spatio-Temporal UAV Fog Node Location algorithm to solve the problem considering several requests, locations, and UAVs. The results obtained in an evaluation with actual data of mobile users has shown that fixed-wing UAVs could indeed be efficiently located to deal with the posed demands.

DUASVS: A Mobile Data Saving Strategy in Short-Form Video Streaming

Fueled by the emerging short video applications (e.g., TikTok), streaming short-form videos nowadays is ubiquitous among mobile users. During the viewing, one common action is to scroll the screen to switch videos, which is a handy operation for the viewers to quickly search for content of interest. However, our empirical measurements reveal that frequent video switching can result in nearly half of the mobile data quota being used for transferring the video data that is never watched. This problem is called data loss in this work. Given the immense cost of the network infrastructure, such a high proportion of data loss is financially tremendous to both mobile users and streaming vendors. To tackle the problem, this study proposes a novel system called Data Usage Aware Short Video Streaming (DUASVS), where a new Integrated Learning is used to capture the characters of past network conditions and then trains intelligent adaptation models to reduce data loss and save data usage. Extensive evaluations show that DUASVS is able to save 70.7%∼83.2% of mobile data usage without incurring any QoE degradation. Moreover, the system exhibits strong robustness, performing consistently over a wide range of network environments as well as video streaming sessions.

Adaptive Caching Scheme for Jointly Optimizing Delay and Energy Consumption in Heterogeneous Digital Twin IoT

With the massive increase of mobile user and sensor data in the heterogeneous Internet of Things (IoT), the limited caching resources are challenging to meet people's increased data traffic demand. Due to ignoring the influence of uplink, the existing joint caching studies cannot reflect the actual network performance. Furthermore, some emerging delay-sensitive applications in the IoT, such as Virtual Reality, Augmented Reality, and autonomous driving, require better reliability and stability. Digital twins (DT) can provide real-time and stable wireless connectivity for heterogeneous IoT physical and virtual spaces. To address those issues, we propose an adaptive caching scheme based on the evolutionary game to jointly optimize delay and energy consumption in the uplink and downlink of heterogeneous digital twins IoT (HDT-IoT) networks. Besides, we prove the existence of evolutionary stability strategies (ESSs) in the proposed caching scheme. Based on the evolutionary stability analysis results, we derive the related expression between the content popularity and the ESS condition. The numerical results verify the existence of the content evolutionary stability caching strategy and the accuracy of the derived corresponding ESS conditional expressions and evaluate the caching performance by comparing our scheme with the other two caching schemes.

Base Station Energy Use in Dense Urban and Suburban Areas

Growing energy consumption is a global problem. The information and communications technology (ICT) industry is in a critical role as an enabler of energy savings in other sectors. However, the power consumption of the ICT sector also needs to be addressed, to contribute to the overall reduction of power consumption and carbon emissions. A new era has begun as the fifth generation (5G) mobile data connection rollouts are advancing globally and are expected to reach a 10% share of end-user devices and connections by 2023. The available references on energy consumption in global mobile networks are rather old and highly averaged – only estimates of energy consumption relative to data volumes are available. There is an information gap regarding the energy consumption of emerging 5G and advanced 4G technologies. Therefore, it has been difficult to understand the actual electricity consumption differences between generations and spatially aggregated electricity consumption once these generations are combined to offer capacity and coverage. This article fills this gap by providing a reference on the energy consumption of base transceiver stations for reported mobile data usage for different Radio Access Technologies; 3G, 4G and 5G respectively. To the best of our knowledge, there is no reference to scientific research on the comparison of energy intensity per square kilometer for 3G, 4G and 5G mobile radio technologies, using actual operator data. The objective of this research was to improve the understanding of the actual energy consumption of different Radio Access Technologies (RAT). The results also give insight to decision makers on when to modernize the operator radio access network. The article reports on the results of field measurements on data and visitor volumes and shares of different RATs. The research contains two statistical RAT combination cases, one representing the European average and the other Finnish mobile networks. The analyses were done for dense urban (DU) and suburban (SU) areas.

Convolutional neural network-based deep Q-network (CNN-DQN) resource management in cloud radio access network

The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a prominent framework in the 5G mobile network to meet the above requirements by deploying low-cost and intelligent multiple distributed antennas known as remote radio heads (RRHs). However, achieving the optimal resource allocation (RA) in CRAN using the traditional approach is still challenging due to the complex structure. In this paper, we introduce the convolutional neural network-based deep Q-network (CNN-DQN) to balance the energy consumption and guarantee the user quality of service (QoS) demand in downlink CRAN. We first formulate the Markov decision process (MDP) for energy efficiency (EE) and build up a 3-layer CNN to capture the environment feature as an input state space. We then use DQN to turn on/off the RRHs dynamically based on the user QoS demand and energy consumption in the CRAN. Finally, we solve the RA problem based on the user constraint and transmit power to guarantee the user QoS demand and maximize the EE with a minimum number of active RRHs. In the end, we conduct the simulation to compare our proposed scheme with nature DQN and the traditional approach.

Fair Licensed Spectrum Sharing Between Two MNOs Using Resource Optimization in Multi-Cell Multi-User MIMO Networks

Licensed spectrum sharing has been a promised approach to provide mobile network operators (MNOs) with required spectrum at times of increased traffic, or to improve the mobile user data rate with limited spectral resources. In this paper, we investigate the use of multi-user multiple-input, multiple-output (MIMO) techniques to enable licensed spectrum sharing. Specifically, we present a fair spectrum sharing system between two MNOs in multi-cell multi-user MIMO networks. We impose fairness by ensuring that each MNO receives spectrum in proportion to the amount it contributes. We formulate a constrained optimization problem to determine resource allocation and user scheduling across two MNOs. Since the problem is non-convex, we develop an algorithm to provide an effective solution through fractional programming and block coordinate descent. Our numerical results illustrate that the proposed spectrum sharing scheme can achieve up to 60 % improvement in terms of the average user rate among the two operators while ensuring that neither MNO is exploited for participating in the sharing mechanism. This improvement is in relation to the baseline of each MNO using multi-user MIMO communications on its own. In addition, most users, especially cell-center users close to the BSs, take advantage of our proposed spectrum sharing framework.

Editorial: Special Issue “Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking”

In the last decade, the behavior of mobile data users has completely changed [...]

Urban population distribution in China: Evidence from internet population

Based on mobile internet user data, we construct an “Internet population” measure and reexamine spatial population distribution in China. The location based service (LBS) data of mobile internet uses is able to capture the accurate location of users' residence and solve the underestimation problem of missing migrants. We have three main findings. First, contrary to previous studies based on traditional population statistics, city size distribution of Internet population fits well into Zipf's law with a R2 of 90.7%. Second, the Internet population indicator is superior to traditional population statistics in explaining inelastic household consumption such as water consumption, electricity consumption, and garbage disposal. It suggests that the “Internet population” is a better proxy of actual city population. Third, the traditional population statistics systematically overestimate population in small cities and underestimate population in large cities. It indicates that the public resource distortions will continue to exist or even worsen off in China if the allocation process relies greatly on traditional population statistics. Although no measures are perfect, our new population measure provides important incremental information for future discussion.

‘Our old pastor thinks the mobile phone is a source of evil.’ Capturing contested and conflicting insights on digital wellbeing and digital detoxing in an age of rapid mobile connectivity

While Africa has largely been considered a digitally-disconnected country, recent studies have shown that connectivity figures are on a rise. In this paper, we theorize digital wellbeing in a context characterized by a fast-growing number of mobile data users despite a historically low Internet penetration. It is focused on an ongoing ethnographic research on mobile users and digital inequalities in Africa, zooming in on results from an explorative study featuring 10 in-depth interviews with young adult heavy users (more than 4–5 hours a day) and seeking to understand strategies they use in attaining digital wellbeing. The findings show how the sampled young adults (18–30 years old) struggle with the daily realities of digital participation including addiction and generational conflict in technology use. Results also reveal ways through which electronic connectivity is perceived both be a tool of freedom as well as a subtle form of potential digital enslavering.

The Effects of Information Nudges on Consumer Usage of Digital Services under Three-Part Tariffs

ABSTRACT We develop a dynamic model to examine how information nudges influence digital services’ consumer usage behavior and welfare under a three-part tariff structure. We study two types of information nudging: nudging through full and nudging through partial information provision. In the former, information nudges are provided to inform consumers of their usage status at every decision point in a billing cycle. In contrast, in the latter, consumers are nudged at one or more decision points within a billing cycle but not throughout the billing cycle. Our model considers two dimensions of consumer heterogeneity: inattentiveness and preference. Furthermore, our model investigates an important but under-investigated design element of information nudges, namely, the timing of the information nudges. We find that (1) nudging through information provision influences inattentive consumers’ usage decisions and improves consumption efficiency, (2) consumers’ welfare gains from full information nudging depict an inverted-U shape contingent on consumers’ preference heterogeneity, and (3) the timing of nudging matters. Our findings provide managerial implications for the design of information nudging strategies and procedures. Finally, we empirically illustrate the analytical results in the context of consumers’ mobile data usage behavior.

Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions

Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things (IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by integrating UAVs into MEC networks during the current novel coronavirus disease (COVID-19) era. UAV mobile edge computing (MEC) shares personal data with external parties (such as edge servers) during intelligent medical analytics. However, this technique raises privacy concerns about patients’ health data. More recently, the concept of federal learning (FL) has been set up to protect mobile user data privacy. Compared to traditional machine learning, federated learning requires a decentralized distribution system to enhance trust for UAVs. Blockchain technology provides a secure and reliable solution for FL settings between multiple untrusted parties with anonymous, immutable, and distributed features. Therefore, blockchain-enabled FL provides both theories and techniques to improve the performance of intelligent UAV edge computing networks from various perspectives. This survey begins by discussing the current state of research on blockchain and FL. Then, compare the leading technologies and limitations. Second, we will discuss how to integrate blockchain and FL into UAV edge computing networks and the associated challenges and solutions. Finally, we discuss the fundamental research challenges and future directions.

Optimizing the spatial scale for neighborhood environment characteristics using fine-grained data

The neighborhood effect of environment characteristics has been an important topic in social sciences that are concerned with people’s behaviors. However, defining a neighborhood and its aggregation scales are difficult challenges for the studies using neighborhood characteristics. Previous studies generally adopted fixed administrative units as the neighborhood and chose the same scale for all the variables due to data availability. Aggregation scales have a significant influence on examining the relationships between area-based input variables and outcome variables, but how to optimize the scale for each variable has been less investigated. This study proposes a multiscale feature extraction and scale optimization framework for simultaneously determining the optimal scale for each neighborhood environment variable and building an optimized multiscale model. First, we developed multiscale extraction methods for the socioeconomic and built environment characteristics within overlapping neighborhoods based on different types of data, including fine-grained mobile phone user data and point of interest data. Next, we proposed a random forest-based scale optimization method to quantify the impact of scales and determined an optimal spatial scale for each variable. Third, we used urban burglary risk modeling as an example, to demonstrate the usefulness of the scale optimization method. In the comparison, the model using an optimized multiscale feature set (R2 = 0.843, F1 score = 0.797 for high-risk samples) achieved better accuracy than all the models using fixed-scale combination feature sets. Moreover, the results revealed that there might be differences in the optimal scale of different neighborhood environment characteristics and demonstrated the effectiveness of our scale optimization method. Our multiscale feature extraction and scale optimization framework is well suited for assessing the neighborhood effect in social sciences.