Mobile Data Consumption Research Articles

Mobile traffic prediction with attention-based hybrid deep learning

Mobile data consumption is ever-increasing due to the continuous emergence of bandwidth-hungry mobile applications as well as the drastic increase in number of mobile terminals resulted from the pervasive adoption of Internet of Things. In the 5G+ era, network operators have to deal with new technical challenges on network management, resource optimization and mobility scheduling, which require the ability to perform efficient mobile traffic prediction. Traditional prediction methods using convex optimization, Markov model and linear equations cannot describe the various nonlinearity changes of network traffic. In this paper, we propose a novel traffic prediction algorithm for mobile networks by introducing a hybrid deep learning method equipped with the attention mechanism, which analyzes the traffic in cellular zones to determine the spatiotemporal characteristics in the residential community. The hybrid deep learning model includes autoregressive integrated moving average mode (ARIMA) preprocessing with decomposition ability for time series, external spatial feature variables, sequence-to-sequence (seq2seq; attention-based CNN-LSTM) pretraining and extreme gradient boosting (XGBoost) optimization processing for integrating nonlinear relationships. The CNN method shows good nonlinear generalization ability to mine deep features of community traffic and capture the nonlinear trend. Our proposed approach first decomposes the community traffic data into linear and residual parts through ARIMA and then seizes the deep features of community traffic through the constructed pretraining seq2seq framework. Finally, the pretraining outcomes are further adjusted by the XGBoost method. Simulation results show that the proposed solution improves the accuracy of prediction apparently.

Consumers' Preferences for Purchasing mHealth Apps: Discrete Choice Experiment.

There is growing interest in mobile health apps; however, not all of them have been successful. The most common issue has been users' nonadoption or abandonment of health apps because the app designs do not meet their preferences. Therefore, to facilitate design-preference fit, understanding consumers' preferences for health apps is necessary, which can be accomplished by using a discrete choice experiment. This study aims to examine consumer preferences for health apps and how these preferences differ across individuals with different sociodemographic characteristics and health app usage and purchase experiences. A cross-sectional discrete choice experiment questionnaire survey was conducted with 593 adults living in Hong Kong. A total of 7 health app attributes that might affect consumers' preferences for health apps were examined, including usefulness, ease of use, security and privacy, health care professionals' attitudes, smartphone storage consumption, mobile data consumption, and cost. Mixed-effect logit regressions were used to examine how these attributes affected consumer preferences for health apps. Fixed effects (coefficient β) of the attributes and random effects of individual differences were modeled. Subgroup analyses of consumer preferences by sex, age, household income, education level, and health app usage and purchase experiences were conducted. Cost was the attribute that had the greatest effect on consumers' choice of health apps (compared to HK $10 [US $1.27]-HK $50 [US $6.37]: β=-1.064; P<.001; HK $100 [US $12.75]: β=-2.053; P<.001), followed by security and privacy (compared to no security insurance-some security policies: β=.782; P<.001; complete security system: β=1.164; P<.001) and usefulness (compared to slightly useful-moderately useful: β=.234; P<.001; very useful: β=.979; P=.007), mobile data consumption (compared to data-consuming-a bit data-consuming: β=.647; P<.001; data-saving: β=.815; P<.001), smartphone storage consumption (compared to >100 MB-around 38 MB: β=.334; P<.001; <10 MB: β=.511; P<.001), and attitudes of health care professionals (compared to neutral-moderately supportive: β=.301; P<.001; very supportive: β=.324; P<.001). In terms of ease of use, consumers preferred health apps that were moderately easy to use (compared to not easy to use-moderately easy to use: β=.761; P<.001; very easy to use: β=.690; P<.001). Our results also showed that consumers with different sociodemographic characteristics and different usage and purchase experiences with health apps differed in their preferences for health apps. It is recommended that future health apps keep their mobile data and phone storage consumption low, include a complete security system to protect personal health information, provide useful content and features, adopt user-friendly interfaces, and involve health care professionals. In addition, health app developers should identify the characteristics of their intended users and design and develop health apps to fit the preferences of the intended users.

A discrete choice experiment to examine the factors influencing consumers' willingness to purchase health apps.

The benefits of health apps can only be realized when consumers purchase them for use. Thus, it is important to understand what factors influence consumers' willingness to purchase health apps. Therefore, this study aimed to examine the influence of health app attributes and sociodemographic characteristics on consumers' willingness to purchase health apps, and how the value of the health app attributes varies for individuals with different sociodemographic characteristics. A questionnaire-based discrete choice experiment (DCE) was conducted with a random sample of 561 adults. A standard logit regression was applied to assess the influence of health app attributes and sociodemographic characteristics on consumers' willingness to purchase health apps, and marginal willingness to pay (MWTP) was calculated for each factor using regression coefficients. Interaction effects were also examined to determine how the value of health app attributes varies by sociodemographic characteristics. Usefulness, ease of use, security and privacy, and attitudes of healthcare professionals toward consumers' use of health apps were the attributes of health apps that positively influenced consumers' willingness to purchase them. Conversely, smartphone storage consumption, mobile Internet data consumption, and app price negatively influenced consumers' willingness to purchase the apps. For sociodemographic characteristics, being male, having a household size greater than three, having a monthly household income of HK$30,000 or more, having a lower education level (below diploma), having previously used health apps, and having previously purchased health apps were associated with a higher willingness to purchase health apps. Health app attributes that influenced consumers' willingness to purchase the apps and populations that were less willing to purchase health apps were identified. Efforts should be made to improve health app attributes and enhance the promotion of health apps among these underserved populations.

Development of young sago palm environmental monitoring system with wireless sensor networks

• Real-time monitoring of the environmental parameters (temperature, relative humidity, and light intensity) of young sago palms in mist chambers. • Establishment of a web-based monitoring system with wireless sensor networks using Zigbee protocol and GPRS gateway. • Reliability and performance of the system is discussed in terms of packet loss rate and mobile data consumption. • Effect of functional system with respect to the environmental factors monitored is analysed. Environmental monitoring plays a significant role in the agriculture field as it manages the critical factors required for sustainable growth of plants, and thus enhancing the quality and productivity of the crops. In this paper, the development of a wireless sensor network (WSN) monitoring system for young sago palms in mist chambers is presented. The effect of the functional system with respect to the environmental factors monitored is also discussed. The system was set up to monitor three environmental factors i.e., temperature, relative humidity, and light intensity. Instantaneous readings of the monitored parameters were made possible through a wireless network and a web portal connection. The system recorded the environmental reading at every 15-min interval and transmitted the data from node to base using the Zigbee network. The data was then stored in an online cloud platform through the general radio packet service (GPRS) gateway. Linear actuators were also installed to control the mist chamber doors opening for natural ventilation and they were automatically triggered by the threshold temperature. Besides, a smart alert system was added to the system prompting for actions via short messaging services (SMS) when triggered. The developed system achieved a reliable data transmission with a low packet loss rate (PLR) of 2.3%. The use of the GPRS gateway also enabled the system to operate at a lower cost as the mobile data consumption was low (10 MB per month). In conclusion, a WSN system for mist chambers in a sago palm greenhouse was successfully developed to real-time monitor the plant growing environment through data acquisition, data transmission and data visualisation. The developed WSN system had laid a foundation for smart farming. For future applications, the system could be further modified and developed for precision agriculture.

Sponsored Data: Smarter Data Pricing with Incomplete Information

With the development of data-intensive internet services, the world has witnessed explosive growth in mobile data consumption during the last couple of years. The upcoming generation of 5G-capable phones and networks will continue and even accelerate that process. At the same time, consumers are becoming more conscious about their data consumption because their monthly caps of mobile data plans can be easily exhausted by premium content, such as high-definition videos and virtual-reality games. In response, the mobile network operators (MNOs) have proposed a new business model, the so-called sponsored data plans, to subsidize consumers by transferring at least part of the data bills from consumers to content providers. Although industry practitioners claim that sponsored data plans increase consumer welfare, our analysis reveals that the impact of sponsored data on consumer surplus depends crucially on whether the MNO has complete information of the consumers’ valuation of mobile data. Our analysis helps provide a clearer picture of the impact of sponsored data on consumer surplus while reconciling the conflicting views from scholars, digital rights groups, and the network carriers.

Information technology Act implications on social media and cybercrime

Social media is seen as a key platform for freedom of expression: anyone with access to the internet can create an account, which allows them to express their own opinions on a daily basis, through tweets, status updates, and photographs. Freedom of expression is considered to be a basic human right, so in this respect we can praise social media for furthering the fulfilment of this human right, through its accessibility. Freedom of expression is an integral part of freedom to information. An interesting example of the role of social media in freedom of information was, Twitter’s decision to revoke the use of an account called ‘Politwoops’, which let the public see tweets politicians had deleted. This account was shut down as Twitter suggested its use violated the politician’s privacy settings. However, it has now been reinstated to allow public transparency and freedom of information. An ideal world we would like to assume that our social media pages are a safe place for us to express ourselves. On the one hand, social media is an excellent resource for enabling the human right of freedom of expression, but on the other hand, it does pose deeply problematic questions regarding self-censorship and surveillance. Social media platform is garnering attention like never before. With lockdowns and nowhere to go people are hooked to Social media like never before. It is become the go to place for all sorts of news, socializing, learning and arguments. In India an estimated 11 gigabits data is consumed by mobile users on social media. The overall mobile data consumption in the country is increased by 47%during the Covid affected lockdown in the year 2019.The speed of internet is one another factor on which every person working from home is investing on. In this constrained physical socializing space and increased dependency on social media, there are concerns about the contents posted by the social media users. The data analysis shows intrusion into privacy, derogatory posts and personal data protection problems and hoax messages on the social media. However, to address the real impact of social media on freedom of expression,the meaning of ‘freedom of expression’ must be defined. The exceptions to this rule are when an ‘expression’ directly, and violently, threatens another person or group. Though there are certain regulatory controls through new law and regulations. IT ACT was enacted in the year2000 in India, it is continuously evolving with the time and new regulations are being incorporated continuously. This paper is an exclusive study of main regulations imposed by the IT Act in India. The paper also discusses the loss of immunity clause for the organizations which are not abiding the recent restrictions. An exclusive case study is presented on much discussed social media platform “Twitter, in this paper. The Ministry of Electronics and Information Technology (MEITY), Government of India also set up experts committee to identify key data protection issues in India and recommend methods of addressing them. This paper analyzes the existing Information Technology Act 2000. The paper examines main provisions of the IT Act and the Rules that provide for the protection of personal data, the kind of personal data that they apply to, as well as some of the criticisms of these provisions and their enforcement.

Heterogeneous SDN controller placement problem—The Wi-Fi and 4G LTE-U case

The widespread deployment of 5G wireless networks has brought about a radical change in the nature of mobile data consumption. It requires the deployment of Long Term Evolution to the unlicensed spectrum (LTE-U) to alleviate the shortage of available licensed spectrum while Wi-Fi is the most dominant radio access technology in the unlicensed spectrum. This has led to suggestions concerning the coexistence of multi radio access technologies such as Wi-Fi and LTE-U. Clearly, Software Defined Networks (SDN) can utilize the LTE-U radio bandwidth efficiently. The SDN paradigm decouples the network’s control logic from the underlying routers and switches, which promotes centralization of network control. The controller placement problem is crucial to SouthBound interface optimization and involves the location of the controllers, their number and the assignment function of switches to controllers. In this paper, a novel strategy is presented to address the wireless controller placement problem, which improves the throughput, link failure probability and the transparency on the SouthBound interface in cases where hybrid providers choose either LTE-U or Wi-Fi as link layer technologies. The problem of determining the placement of LTE-U and Wi-Fi based controllers is modeled and two heuristic solutions are proposed. Simulations show that the proposed algorithms provide a fast and efficient solution.

Reward Optimization for Content Providers With Mobile Data Subsidization: A Hierarchical Game Approach

Mobile data subsidization launched by mobile network operators is a promising business model to provide economic benefits for the mobile data market and beyond. It allows content providers to partly subsidize mobile data consumption of mobile users in exchange for displaying a certain amount of advertisements. From a content provider perspective, it is of great interest to determine the optimal strategy for offering appropriate data subsidization (reward) in order to compete against others to earn more revenue and gain higher profit. In this paper, we take a hierarchical game approach to model the reward optimization process for the content providers. To analyze the relationship between the provider and the user, we first focus on the one-to-one interaction in a single-provider single-user system, and formulate a Mathematical Program with Equilibrium Constraints (MPEC). We apply the backward induction to solve the MPEC problem and prove the existence and uniqueness of the Stackelberg equilibrium. We then formulate an Equilibrium Program with Equilibrium Constraints (EPEC) to characterize the many-to-many interactions among multiple providers and multiple users. Considering the inherent high complexity of the EPEC problem, we utilize the distributed Alternating Direction Method of Multipliers (ADMM) algorithm to obtain the optimum solutions with fast-convergence and decomposition properties of ADMM.

CASCM2: Capability-Aware Supply Chain Management Model for QoS-driven offload-participator selection in Fog environments

Computational offloading happens to be a prominent solution for leveraging the performance of handheld devices. It raises the feasibility of executing computation-intensive and latency-conscious tasks with the help of task migration to proximate cloud servers. However, longer Wide Area Network latencies of cloud and greater mobile data consumption paved the way to adopting opportunistic offloading instead of remote offloading. This proposed work uses an edge-based solution of Fog computing to handle such tasks and to provide the users with a high Quality of Experience. This paper presents a Capability-Aware Supply Chain Management Model (CASCM $$^2$$ ) as an extension of traditional Supply Chain Management (SCM) model. CASCM $$^2$$ dynamically selects a crowd of competent mobile devices within a Foglet that is in proximity to the users and offloads the complex computational tasks to them. The proposed model aims at optimizing two parameters, such as communication overhead and conductance cost, as they possess a remarkable impact on offloading delay-sensitive tasks. Hence an overall objective optimization is achieved using a dual Lagrangian decomposition method, which subdivides and solves the optimization of parameters in parallel. Experimental analysis of the participator selection is performed for a single period as well as multiple periods. The performance results yield a considerable contribution that alleviates the issues in delay-sensitive applications deployed in the Fog framework.

Mobile-based Hospital Bed Management with Near Field Communication Technology

Bed management is one of the most crucial tasks in a hospital. A desktop-based (PC) information technology system has been widely adopted to assist this operation. The fixed location and the limited amount of available PCs shared among staff for different routine tasks can cause delays in bed information updating and therefore delays to the bed management processes. With the penetration of smartphones, a mobile-based application becomes a reliable alternative solution for bed management to complement the existing system. Smartphones offer a few benefits, especially mobility because users can update the information from any place and at any time. Our aim was to study the applicability of a mobile-based application for assisting bed management operation. The application consisted of ward-based bed information visualization, bed information updating, and ward information updating modules. Observation and interviews with expert users were performed to outline the required functions of the application. On a Likert 1-5 scale, the mobile application achieved a mean score of 3.45 for usefulness, 3.40 for ease of use, and 3.42 for satisfaction. Meanwhile, the respondents were also concerned about the mobile data consumption of the app, which took up 45.8% of the negative feedback. On the positive side, the respondents agree that the mobile application helps them in the bed management operation.

Forward-Looking Behavior in Mobile Data Consumption and Targeted Promotion Design: A Dynamic Structural Model

Are individual consumers forward looking in consuming mobile data volume every day, subject to a monthly quota limit? If so, how can mobile carriers easily identify these forward-looking users and design profitable promotions targeting these users? We discover distinct temporal usage patterns that can distinguish forward-looking users from myopic ones. An empirical test is constructed to test for individual forward-looking behavior and applied to a unique data set of individual mobile data users’ daily usage volume over multiple months. We find a considerable proportion of users (about 40%) are indeed forward looking and also find empirical evidence of individual consumer myopia. Our dynamic structural model enables sophisticated counterfactual simulations incorporating various factors (e.g., consumer anticipation, plan switch) to deliver rich implications for targeted promotion design. As we find, promotions targeting only forward-looking consumers could be significantly more profitable than blanket promotions uniformly applied to all. Properly designed end-of-month promotions targeting forward-looking users could help mobile carriers fully utilize the otherwise excess network bandwidth and increase revenue at little extra cost.

Understanding Urban Dynamics From Massive Mobile Traffic Data

Understanding the patterns of mobile data consumption is extremely valuable to reveal human activities and ecology in urban areas. This task is nontrivial in terms of three challenges: the complexity of mobile data consumption in large urban environment, the disturbance of abnormal events, and lack of prior knowledge for urban traffic patterns. We propose a novel approach to design a powerful system that consists of three subsystems: time series decomposing of mobile traffic data, extracting patterns from different components of the original traffic, and detecting anomalous events from noises. Our investigation involving the mobile traffic records of 6,400 cellular towers in Shanghai reveals three important observations. First, among all the 6,400 cellular towers, we identify five daily patterns corresponding to different human daily activity patterns. Second, we find that two natural patterns can be extracted from the weekly trend of mobile traffic consumption, which reflects modes of human activities. Last but not least, besides the regular patterns, we investigate how irregular activities affect mobile traffic consumption, and exploit this knowledge to successfully detect unusual events like concerts and soccer matches. Our proposed methodology therefore will aid a comprehensive understanding of large-scale mobile traffic consumption in urban areas.

5G features from operation perspective and fundamental performance validation by field trial

The fast deployment and penetration of 4G has cultivated human behaviors on mobile data consumption, leading to explosive growth in mobile traffic and stimulating new requirements on the capabilities of mobile networks. To meet the requirements of mobile networks toward year 2020, the next generation of mobile networks (termed as IMT-2020, or 5G) is designed to support 100 Mbps-1 Gbps user-experienced data rate, 1 ms radio transmission latency, and 1 million connections per square kilometer. Recalling the vision and requirements of 5G targeting for commercial launch in 2020, this article overviews the key features of 5G and compares with those of 4G, and reports the world first field trials conducted to validate the key performance of 5G radio interface in 3.5 GHz band. The trial results show that a 1 ms transmission latency and 1 Gbps data rate are achievable.

Energy efficient dynamic resource allocation for wireless systems using receiver puncturing technique

Given the massive increase in network infrastructure required to provide the high capacities expected in current wireless systems due to the exponential growth in mobile data consumption, substantial improvement in energy efficiency of the current 4G systems is necessary. High energy efficiency results in low operational cost and sustainable network deployment. In this paper, we propose a new receiver puncturing technique and a novel energy efficient dynamic resource allocation scheme. Improvements in BER performance and 20%~25% transmit-energy expenditure reduction, as compared to conventional proportional fair resource allocation schemes, are shown by both link level and system level simulations of a multi-cell downlink orthogonal frequency-division multiple access (OFDMA) system. Finally, the complexity issues inherent to the proposed technique are also evaluated.

Evaluating the Energy Consumption of Mobile Data Transfer—From Technology Development to Consumer Behaviour and Life Cycle Thinking

Mobile data consumption in Finland is among the highest in the world. The increase in mobile data usage has been rapid and continual future growth is foreseen. Simultaneously, consumer behaviour is changing. While new end-user devices are more and more energy-efficient and energy consumption per transferred gigabyte has significantly decreased, people spend more time and consume more data via their mobile devices than ever before. Does the increased usage outweigh the energy savings that have been achieved? What options are available for tackling increasing energy demand? And should consumers have a role to play in this discussion? This paper examines the current and future trends that results from the energy consumption of mobile data transfer and mobile networks in Finland. The findings presented in this paper are based on a top-down energy intensity estimate and publicly available data, which was employed to construct an illustrative trend (kWh/gigabyte) for the energy consumption of transmitted mobile data for the years 2010–2017. In addition, energy consumption related to mobile data transfer is discussed from a life cycle perspective, considering both direct and indirect energy use. Finally, the challenges in conducting such assessments are examined.

Widening the Data Pipeline: A Carrier Aggregation BAW Quadplexer Module

A better user experience for broadband multimedia applications and a robust alwayson high-speed Internet connection drive operators to meet many challenges in terms of data capacity. As illustrated by Shannon?s theorem, the upper bound for the capacity of a data link in bits per second (b/s) is a function of the available bandwidth and the link's signal-to-noise ratio. Even before 5G mobile networks are deployed and wider bandwidth is available, the demand for mobile data consumption is not slowing down, and requirements for user equipment data throughput continue to increase.

Inferring Correlation between User Mobility and App Usage in Massive Coarse-grained Data Traces

With the rapid growth in smartphone usage, it has been more and more important to understand the patterns of mobile data consumption by users. In this paper, we present an empirical study of the correlation between user mobility and app usage patterns. In particular, we focus on users' moving speed as the key mobility metric, and try to answer the following question: are there any notable relations between moving speed and the app usage patterns? Our study is based on a real-world, large-scale dataset of 2G phone network data request records. A critical challenge was that the raw data records are rather coarse-grained. More specifically, unlike GPS traces, the exact locations of users were not readily available. We inferred users' approximate locations according to their interactions with nearby cell towers, whose locations were known. We proposed a novel method to filter out noises and perform reliable speed estimation. We verify our methodology with out of sample data and show its improvement in speed estimation accuracy. We then examined several aspects of mobile data usage patterns, including the data volume, the access frequency, and the app categories, to reveal the correlation between these patterns and users' moving speed. Experimental results based on our large-scale real-world datasets revealed that users under different mobility categories not only have different smartphone usage motivations but also have different ways of using their smartphones.

Cost Efficiency for Economical Mobile Data Traffic Management From Users’ Perspective

Explosive demand for wireless internet services has posed critical challenges for wireless networks due to their limited capacity. To tackle this hurdle, wireless Internet service providers (WISPs) take the smart data pricing to manage data traffic loads. Meanwhile, from the users’ perspective , it is also reasonable and desired to employ mobile data traffic management under the pricing policies of WISPs to improve the economic efficiency of data consumption. In this paper, we introduce a concept of cost efficiency (CE) for user’s mobile data management, defined as the ratio of user’s mobile data consumption benefits and its expense. We propose an integrated CE-based data traffic management scheme, including long-term data demand planning, short-term data traffic pre-scheduling, and real-time data traffic management. The real-time data traffic management algorithm is proposed to coordinate user’s data consumption to tailor to the pre-scheduled data traffic profile. Numerical results demonstrate the effectiveness of CE framework in indicating and motivating mobile user’s data consumption behavior. The proposed management scheme can effectively motivate the user to adjust its data consumption profile to obtain the optimal data consumption CE.

Digital Technologies and New Spectrum: The (Future) Use of the 700 Mhz Frequency Band in the European Union Framework

In the vast development of technologies nowadays, demand for radio spectrum in the European Union (EU) is increasing exponentially, especially for mobile data consumption due to an increasing usage of smart devices with its bandwidth-hungry applications, and also the rising of machine-to-machine communications as the Internet of Things developed. In the light of that, recommendation to repurpose the 700 MHz frequency band has brought up in 2014. This paper aims to analyse how the repurposing will fit in the EU framework, how it will be a successful submission to maximize the economic and social benefits of spectrum frequency usage. This repurposing recommendation will be discerned by putting it in the scope of Directive 2002/21/EC (Framework Directive), also analysing feasible approach to safeguard the EU internal market such as harmonizing technical aspects as well as providing certainty for the stakeholders.

A teletraffic model for the Physical Downlink Control Channel in LTE

Recent advances in mobile handsets have led to new trends in mobile broadband data consumption, with a huge increase of signaling traffic generated by always-connected devices. Thus, a proper planning of control channels in mobile networks is necessary to avoid network capacity problems and provide an appropriate quality of service to the end user. A key issue in network planning is the availability of a complete and accurate system model. In this paper, an analytical performance model for the Long Term Evolution (LTE) Physical Downlink Control Channel (PDCCH) is constructed based on queuing theory. Unlike previous works, the proposed model considers important network features, such as link adaptation, and can be tuned with available network performance statistics. Model assessment is carried out by comparing performance estimates with request-level simulations and real performance measurements taken from a live LTE network. Results show that the proposed model can predict the PDCCH load distribution in a live network accurately.