Mobile Data Usage Research Articles

Experience Effect in the Impact of Free Trial Promotions

Although the use of free samples is extensive across industries, the effects of free samples across individuals with varying levels of usage have yet to be systematically examined. The models discussed in the literature consider targeting only the current nonusers of a product. In this research, we examine the question of targeting the current users both analytically and empirically for an experience good. Our analytical discussions highlight the reasons why some current users may be effective targets for free-sample promotions. We then conduct an empirical analysis using a data set on pre- and post-free-sample promotion mobile data usage provided by a telecom firm. The empirical findings are consistent with our analytical results. Specifically, we find the initial usage level to be a key determinant of both the redemption rate of a free-sample offer and the subsequent change in usage owing to free-sample redemption. In our context, the redemption rate increased from the low-percentile users to the high-percentile users. We also find that the change in usage was (weakly) monotonically increasing up to the [Formula: see text] percentile of usage distribution. Beyond the [Formula: see text] percentile, the effect was generally not significant. We discuss the managerial and policy implications of our findings. This paper was accepted by Juanjuan Zhang, marketing.

Assessment of the impact of climate change on cities livability in China

As global warming worsens, climatic conditions in many regions are undergoing profound change, which could influence certain industries, such as agriculture and transportation, and affect the livability of cities. In this study, cities statistics and meteorological station data of the past decade in China were used to analyze the effect of climate change on cities livability. The livability of 288 Chinese cities during 2006–2016 was assessed using an analytic hierarchy process method. Results showed the mean cities livability index in China has improved about 12% throughout the past decade. Moreover, the Moran's I statistic and local indicators of spatial association revealed that the distribution of cities livability reflects a trend of gradual spatial agglomeration. In addition, geostatistical analysis was used to evaluate the impact of extreme weather events on cities livability. It was established that heatwaves and extreme precipitation events have significant impact on the livability of cities in southern China, whereas freezing weather drives the change of urban livability in northern areas. Combinations of different extreme weather conditions will have greater impact on urban livability. Based on mobile phone user data, the relationship between livability change and climate change in Chinese cities was elucidated to provide reference for urban environmental management.

Economic Viability of a Virtual ISP

Growing mobile data usage has led to end users paying substantial data costs, while Internet service providers (ISPs) struggle to upgrade their networks to keep up with demand and maintain high quality-of-service (QoS). This problem is particularly severe for smaller ISPs with less capital. Instead of simply upgrading their network infrastructure, ISPs can pool their networks to provide a good QoS and attract more users. Such a vISP (virtual ISP), for example, Google's Project Fi, allows users to access any of its partner ISPs' networks. We provide the first systematic analysis of a vISP's economic impact, showing that the vISP provides a viable solution for smaller ISPs attempting to attract more users, but may not maintain a positive profit if users' data demands evolve. To do so, we consider users' decisions of whether to defect from their current ISP to the vISP, as well as existing ISPs' decisions on whether to partner with the vISP. We derive the vISP's dependence on user behavior and partner ISPs: users with very light or very heavy usage are the most likely to defect, while ISPs with heavy-usage customers can benefit from declining to partner with the vISP. Our analytical results are verified with extensive numerical simulations.

Comparison of the spatiotemporal mobility patterns among typical subgroups of the actual population with mobile phone data: A case study of Beijing

The study of urban dynamics from spatio-temporal information contained in massive human-tracking data has become a hotspot in research. As numerous human mobility patterns and fine-grained varying dynamics of the urban environment have been ascertained using various data and methods, the people shaping such diverse urban vitality remain poorly understood. This study aimed to profile different mobility patterns of typical subgroups in the actual population of the city and then quantify the differences among subgroups. Based on one-month data of mobile phone users with the recorded presence in Beijing, 8 subgroups with different purposes of visit and related spatio-temporal patterns were ascertained. By combing the short-term phenomenon with long-term observations, the varying urban dynamics structured by residents, usual travelers and visitors over time were explained. The mobility patterns of different subgroups were compared according to spatial and temporal indicators, including population distribution by time, average length of stay, distribution of visited places by ring roads, and Land-use Visitation Intensity. These findings have been successfully referenced in Beijing's planning practices. This study is of high implication to optimize the allocation of urban resources and enhance the capacity of urban facilities to satisfy the demands of the actual population in the city.

The Impact of the New Generation Mobile Apps on Services Sector

A General Vehicle Technology such as Computer Applications and Internet Penetration affects all sectors of Economy. Previous studies have quantified the extent of growth in GDP that occurs due to a given growth level in internet subscriptions. However, the immediate impact of mobile app usage is naturally on services sector growth rather than the overall GDP. Since September 2016, when Reliance Jio unleashed a data price war, the app-based services sector saw a phenomenal growth. Taking this well-known fact into account, this paper constructs a regression model between mobile internet subscription and the growth of services sector in six states of India for the period 2014-18. The study also enquires if the spurt of mobile data usage since the year 2017 has resulted in a corresponding growth on services sector GSDP in the target states. Although there exists an extremely strong correlation between the growth of wireless internet subscriptions and the growth of services sector in the target states, the spurt in data usage since 2017 hasn’t resulted in a corresponding spurt in the growth of services sector. The study explores the implications of such results obtained through regression analysis between mobile internet and the growth in services sector GSDP.

Toward Cost-Effective Mobile Video Streaming through Environment-Aware Watching State Prediction.

Mobile video applications are becoming increasingly prevalent and enriching the way people learn and are entertained. However, on mobile terminals with inherently limited resources, mobile video streaming services consume too much energy and bandwidth, which is an urgent problem to solve. At present, research on cost-effective mobile video streaming typically focuses on the management of data transmission. Among such studies, some new approaches consider the user’s behavior to further optimize data transmission. However, these studies have not adequately discussed the specific impact of the physical environment on user behavior. Therefore, this paper takes into account the environment-aware watching state and proposes a cost-effective mobile video streaming scheme to reduce power consumption and mobile data usage. First, the watching state is predicted by machine learning based on user behavior and the physical environment during a given time window. Second, based on the resulting prediction, a downloading algorithm is introduced based on the user equipment (UE) running mode in the LTE system and the VLC player. Finally, according to the corresponding experimental results obtained in a real-world environment, the proposed approach, compared to its benchmarks, effectively reduces the data usage (14.4% lower than that of energy-aware, on average) and power consumption (about 19% when there are screen touches) of mobile devices.

Empowering Client Android Applications on Distributed Cloud Servers in Data Center

The advancement of technical knowhow in wireless mobile phone clients and their inceptions from cloud server, have been implemented as versatile applications that utilize an optimal user experience. By using fabrication components in the user domain, we have yet to instill peripheral interfaces such us CPUs, memory and batteries in to achieving a personal computer machine usage in android devices. To empower the user interface as deployed in android applications, we must design a distributed computing environment that can be deployed in cloud servers. This thin client distributed architecture needs to be effective in ensuring a efficient android user experience . To resolve delays and latency from asynchronous mobile data usage, an effective deployment design must be implemented in mobile-mobile environment, that uses Tcp/Ip packets in transferring from routable mobile switching server.

A Novel Analysis of Wireless Network Monitoring using Wireshark

In this paper, we describe Wireless networks have seen unprecedented rise in their size and number of users in recent years. This unprecedented rise is attributed to the rise in the number of mobile computing devices. Moreover the amount of data that is handled by these wireless networks has increased in recent years. One such wireless network that is widely used but is still prone to attacks is WiFi. Wi-Fi protocol (IEEE 802.11), over the years has been upgraded many times, but these upgrades have mainly resulted in increase in the overall data rate of the communication. Little has been done to improve the security of the protocol. This research focuses on inherent flaws in Wired Equivalent Privacy protocol (WEP) used by the 802.11 standard, Temporal Key Integrity Protocol (TKIP) which is considered an interim solution to legacy 802.11 equipment. The demand for ubiquitous personal communications is driving the development of wireless networks that can accommodate mobile voice and data users who move throughout buildings, cities, or countries. The objective of this chapter is to provide the fundamentals of wireless networks so that the general readers can be able to easily grasp some of the ideas in this area.

A Study of the Effects on QoS in WiFi-Cellular Offloading Scenarios

Nowadays the mobile data usage has been significantly increased by an unprecedented amount with the wide spread of smart devices, which is known as the explosion of data traffic. The rapid growth in mobile data traffic leads to a deficiency of cellular network capacity. To solve this problem, readily available Wi-Fi networks are used to offload the data traffic from cellular networks. The Wi-Fi offloading must ensure guaranteed throughput and delay performance for the users. However, if the user doesn’t meet any Wi-Fi network during the download period, the quality of experience gets degraded. Quality of experience can be improved with the help of various techniques such as resource allocation, scheduling, and handoff schemes. To know the effect of the offloading process, some key parameters are identified in this paper and the effect of offloading on these parameters is studied. Here, in this paper a study of various parameters like download time, number of users, data size on the throughput, delay and packet loss is done in the cellular network -WiFi offloading scenarios. This study highlights the need for an efficient QoS mechanism in future heterogeneous networks. It can be considered as a research aspect in upcoming integrated networks.

Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

Novel Data Sponsoring Control Scheme Based on the Dual-Leader Stackelberg Game Model

In the phase of the process that will lead to the future 5G networks, the demand of mobile data usage will continue to rise sharply, and the high cost of data will become a critical concern. As a major paradigm shift, data sponsoring has been introduced with the hope of benefiting the practice of the telecommunication industry. The main idea of data sponsoring is to subsidize the service payment while appealing to more users; it can potentially generate more profit gains for both the users and the system operators. In this paper, the intelligent interactions of three network entities, i.e., the users, the service providers, and the content providers, are analyzed based on the basic ideas of game theory, and we design an effective data sponsoring control scheme using a novel dual-leader Stackelberg game model. As dual leaders, service and content providers share the profit in a cooperative manner, and mobile users react individually to the leaders in a competitive manner. By employing different game methodologies, we investigate the mutual relationship between the network entities and aim to balance the user’s payoff and the system revenue. To validate the proposed approach, an analytic simulation model and numerical results are provided to demonstrate the efficiency and feasibility of the proposed approach under the 5G network infrastructure. Finally, we provide further challenges and various future opportunities in this research area.

An empirical study on the cellular subscribers churn, selection factors and satisfaction with the services

With mobile phones becoming smarter, almost all operating systems are downloading and using data. Cellular service providers are concerned with decline in revenue due to constant decline in voice call and text messages and consequently more options of moving to competitor. This leads to building of a model that describes the critical factor explaining the switching of subscriber, to understand the factors for customer churn and identify the relationships between these three constructs with respect to higher mobile data usage. In order to validate the proposed relationship, Confirmatory Factor Analysis using Structure Equation Modelling is used. Findings indicate that one of the important construct of selecting a service provider is customer's satisfactory experience with the service provider. Also, it has been found that the significance of data services is constantly increasing for the subscribers. These findings will be useful to both service providers and researchers to comprehend the service quality parameters considering mobile subscribers view point. This can help to re-look the existing benchmarks and increase customer satisfaction.

An empirical study on the cellular subscribers churn, selection factors and satisfaction with the services

With mobile phones becoming smarter, almost all operating systems are downloading and using data. Cellular service providers are concerned with decline in revenue due to constant decline in voice call and text messages and consequently more options of moving to competitor. This leads to building of a model that describes the critical factor explaining the switching of subscriber, to understand the factors for customer churn and identify the relationships between these three constructs with respect to higher mobile data usage. In order to validate the proposed relationship, Confirmatory Factor Analysis using Structure Equation Modelling is used. Findings indicate that one of the important construct of selecting a service provider is customer's satisfactory experience with the service provider. Also, it has been found that the significance of data services is constantly increasing for the subscribers. These findings will be useful to both service providers and researchers to comprehend the service quality parameters considering mobile subscribers view point. This can help to re-look the existing benchmarks and increase customer satisfaction.

An Efficient Incentive Mechanism for Device-to-Device Multicast Communication in Cellular Networks

With a growing demand for mobile data usage, cellular networks are facing the challenge of severe traffic overload. Device-to-Device (D2D) multicast communication, a proximity communication technique that leverages the spatial–temporal locality of mobile data usage to achieve one-to-many simultaneous transmission, provides an efficient solution to offloading heavy traffic. However, D2D multicast communication relies on users’ sharing behavior, and multicasting data incur costs such as energy, which prevents the popularity of such user-driven technique. Thus, in this paper, we study the problem of incentive design for promoting D2D multicast communication in cellular networks. Specifically, we propose a contract-based incentive mechanism to optimize the operator’s expected profit from motivating D2D multicast communication for content sharing with guaranteed service quality. We consider both complete and incomplete information scenarios. The proposed mechanism can provide efficient incentives under information asymmetry by delivering contracts, which satisfy nice properties such as individual rationality and incentive compatibility . Greedy algorithms with low complexity are developed based on local optimization to obtain fast solutions for contract design. A Lagrange multiplier method based iterative algorithm that can be proved to obtain optimal contracts under information asymmetry is also proposed. Numerical results show that the proposed mechanism can handle information asymmetry better and has a better performance than linear and step pricing schemes, increasing the expected profit by up to 2.49 times and 1.8 times, respectively.

A proactive defense mechanism for mobile communication user data

A proactive defense mechanism for mobile communication user data

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.

Determinants of Customers’ Brand Choice and Continuance Intentions with Mobile Data Service Provider: The Role of Past Experience

The prevalence of mobile data services has intensified competition and structured the market for mobile telecommunication services, resulting in decreased revenue particularly from voices services and high churn rate. Mobile service providers therefore seek ways to increase revenue by attracting and retaining mobile data users. This study investigates the determinants of customers’ brand choice and continuance intentions with mobile service providers in the context of mobile data service. This study also captures the impact of past experience on behavioural outcomes. Data were collected from 304 customers of mobile data service firms and analysed using regression analyses. Results indicate that mobile service quality, pricing structure and promotion, but not brand image, affect brand choice, whereas continuance intentions are affected by the mobile service quality, brand image and price. Customers’ past experience relates significantly and negatively to brand choice but not continuance intention. The contribution and implication of the study are discussed.

Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data Mining

The mobility patterns and trip behavior of people are usually extracted from data collected by traditional survey methods. However, these methods are generally costly and difficult to implement, especially in developing cities with limited resources. The massive amounts of call detail record (CDR) data passively generated by ubiquitous mobile phone usage provide researchers with the opportunity to innovate alternative methods that are inexpensive and easier and faster to implement than traditional methods. This paper proposes a method based on proven techniques to extract the origin–destination (OD) trips from the raw CDR data of mobile phone users and process the data to capture the mobility of those users. The proposed method was applied to 3.4 million mobile phone users over a 12-day period in Mozambique, and the data processed to capture the mobility of people living in the Greater Maputo metropolitan area in different time frames (weekdays and weekends). Subsequently, trip generation maps, attraction maps, and the OD matrix of the study area, which are all practically usable for urban and transportation planning, were generated. Furthermore, spatiotemporal interpolation was applied to all OD trips to reconstruct the population distribution in the study area on an average weekday and weekend. Comparison of the results obtained with actual survey results from the Japan International Cooperation Agency (JICA) indicate that the proposed method achieves acceptable accuracy. The proposed method and study demonstrate the efficacy of mining big data sources, particularly mobile phone CDR data, to infer the spatiotemporal human mobility of people in a city and understand their flow pattern, which is valuable information for city planning.

Exploring the Relationships between the Non-Work Trip Frequency and Accessibility Based on Mobile Phone Data

Non-work trips have increased remarkably over recent years, even exceeding the amount of daily work-related trips. It is therefore important to understand the relationship between the non-work trip frequency and accessibility of the travelers. However, the conclusions of previous studies were inconsistent. The causes of this inconsistency have been speculated to be the limitations of sample size, aggregated accessibility, and home-based trips. To explore the actual relationships between the non-work trip frequency and accessibility, this study employs a more reliable method based on mobile phone data. First, positioning data of mobile phone users was used to identify activity points, home and workplace locations, and non-work trips of the users. Furthermore, the socioeconomic attributes and preferences of users were extracted from mobile phone user feature data. Second, accessibility was measured at the individual level based on a real-time travel-cost estimation method. Subsequently, the negative binomial regression model was employed to study the effects of accessibility, socioeconomic attributes, and preferences of users on their travel frequencies. A case study of the central business district of Lujiazui in Shanghai was conducted to prove the feasibility of the proposed method. The results show that the non-work trip frequency increases with accessibility. Moreover, users with higher consumption levels are more sensitive to accessibility when they decide the frequency of the non-work trips. However, the influence of accessibility on non-work trip frequency tends to weaken with the decrease of accessibility. These findings contribute to a more reliable transportation policy design and planning.

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.