EMERGING TOPICS IN COMPUTING Characterizing User Behavior in Mobile Internet (original) (raw)
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Understanding Data Usage Patterns of Geographically Diverse Mobile Users
IEEE Transactions on Network and Service Management, 2020
The increasing trend of the traffic demand from mobile users and the presence of limited resources creates a challenge for network resource management. Understanding the data usage pattern and traffic demand of mobile users is a way forward to enable data-driven network resource management. However, due to the complex nature of mobile networks, understanding and characterizing data usage pattern of mobile users is a daunting task. In this work, we investigate and characterize data usage patterns and behavior of users in mobile networks. We leverage a dataset (∼340 M records) collected through a crowd-based mobile network measurement platform-Netradar-across six countries. We elucidate different network factors and study how they affect the data usage patterns by taking mobile users in Finland as a use case. We perform a comparison on data usage patterns of mobile users across six countries by considering total data consumption, network access, the number of sessions created per user, throughput, and user satisfaction level on services. We show that data usage behavior of users over a mobile network is primarily driven by user mobility, the type of data subscription plan marketed by Mobile Network Operators (MNOs), network congestion, and network coverage. Besides, the data usage patterns over different network technologies (e.g., preferring cellular over WiFi) and the percentage of users accessing congested networks vary by country; mostly due to the market pricing strategy and radio coverage. However, the overall data consumption (cellular and WiFi) is comparatively similar in most of the countries we studied.
Characterizing data usage patterns in a large cellular network
Proceedings of the 2012 ACM SIGCOMM workshop on Cellular networks: operations, challenges, and future design - CellNet '12, 2012
Using heterogeneous data sources collected from one of the largest 3G cellular networks in the US over three months, in this paper we investigate the usage patterns of mobile data users. We observe that data usage across mobile users are highly uneven. Most of the users access data services occasionally, while a small number of heavy users contribute to a majority of data usage in the network. We apply statistical tools, such as Markov model and tri-nonnegative matrix factorization, to characterize data users. We find that the intensive usage from heavy users can be attributed to a small number of applications, mostly video/audio streaming, data-intensive mobile apps, and popular social media sites. Our analysis provides a fine-grained categorization of data users based on their usage patterns and sheds light on the potential impact of different users on the cellular data network.
Comparison of User Traffic Characteristics on Mobile-Access versus Fixed-Access Networks
Lecture Notes in Computer Science, 2012
We compare Web traffic characteristics of mobile-versus fixed-access end-hosts, where herein the term "mobile" refers to access via cell towers, using for example the 3G/UMTS standard, and the term "fixed" includes Wi-Fi access. It is well-known that connection speeds are in general slower over mobile-access networks, and also that often there is higher packet loss. We were curious whether this leads mobile-access users to have smaller connections. We examined the distribution of the number of bytesper-connection, and packet loss from a sampling of logs from servers of Akamai Technologies. We obtained 149 million connections, across 57 countries. The mean bytes-per-connection was typically larger for fixed-access: for two-thirds of the countries, it was at least onethird larger. Regarding distributions, we found that the difference between the bytes-per-connection for mobileversus fixed-access, as well as the packet loss, was statistically significant for each of the countries; however the visual difference in plots is typically small. For some countries, mobile-access had the larger connections. As expected, mobile-access often had higher loss than fixed-access, but the reverse pertained for some countries. Typically packet loss increased during the busy period of the day, when mobile-access had a larger increase. Comparing our results from 2010 to those from 2009 of the same time period, we found that connections have become a bit smaller.
Clustering and predicting the data usage patterns of geographically diverse mobile users
Computer Networks
Mobile users demand more and more data traffic, yet network resources are limited. This creates a challenge for network resource management. One way of addressing this challenge is by understanding the data usage patterns of mobile users so that resources can be optimally allocated based on user traffic demand and data usage behavior. However, understanding and characterizing the data usage patterns of mobile users is a complex task. In this work, we investigate and characterize users' data usage patterns and behavior in mobile networks. We leverage a dataset (∼113 million records) collected through a crowd-based mobile network measurement platform-Netradar-across five countries. Data usage behavior of users over a cellular network is primarily driven by user mobility, the type of subscription plan marketed by Mobile Network Operators (MNOs), network congestion, and network coverage. We apply an unsupervised machine learning approach to cluster mobile user types by considering different factors such as data consumption, network access type, the number of sessions created per user, throughput, and mobility. By defining data usage pattern of mobile users, we develop a user clustering model and identify three different mobile user groups (clusters). Our clustering model shows that the data usage patterns are unevenly distributed across the five countries studied, characterized by a small number of heavy users consuming the highest volume of data. We show how the types of applications installed by users correlate with data consumption patterns in some countries. Heavy users tend to install more traffic-demanding apps than users from the other two groups-regular and light users. Finally, we trained a classification model using the labeled dataset produced by our aforementioned user clustering method. The model helps classifying mobile users according to their usage patterns (i.e., heavy, regular, and light) with an accuracy of ∼80% in the test dataset.
Relevant Affect Factors of Smartphone Mobile Data Traffic
affect the realization of smartphone MDT. The results of the research clarify the ways which influence the amount of MDT generated by a smartphone. This paper increases the awareness of the users of the methods of generating smartphone MDT. The research also allows users to specify parameters that affect the prediction of generated MDT of a smartphone.
Proceedings of the 8th …, 2010
Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -interactions with the device and the applications used -and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.
An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet
Management Science, 2011
We quantify how user mobile Internet usage relates to unique characteristics of the mobile Internet. In particular, we focus on examining how the mobile-phone based content generation behavior of users relates to content usage behavior. The key objective is to analyze whether there is a positive or negative interdependence between the two activities. We use a unique panel dataset that consists of individual-level mobile Internet usage data that encompasses individual multimedia content generation and usage behavior. We combine this knowledge with data on user calling patterns, such as duration, frequency, and locations from where calls are placed to construct their social network and to compute their geographical mobility. We build an individual-level simultaneous equation panel data model that controls for the different sources of endogeneity of the social network. We find that there is a negative and statistically significant temporal interdependence between content generation and usage. This finding implies that an increase in content usage in the previous period has a negative impact on content generation in the current period and vice versa. The marginal effect of this interdependence is stronger on content usage (up to 8.7%) than on content generation (up to 4.3%). The extent of geographical mobility of users has a positive effect on their mobile Internet activities. Users more frequently engage in content usage compared to content generation when they are traveling. In addition, the variance of user mobility has a stronger impact on their mobile Internet activities than does the mean. We also find that the social network has a strong positive effect on user behavior in the mobile Internet. These analyses unpack the mechanisms that stimulate user behavior on the mobile Internet. Implications for shaping user mobile Internet usage behavior are discussed.
AN ANALYTICAL STUDY ON MOBILE INTERNET USAGE ON CELL PHONES IN NAGPUR
The advent of smart phones has revolutionized the internet accessibility and its usage. Data packs are available on mobile phones to facilitate use of internet on the move and these data packs are available in prepaid and post paid forms. Mobile internet is consumed in different pattern by each individual using cell phone & time spent on different applications also varies from person to person. This paper aims to analyse the attitude of mobile internet users, taking into account the use of mobile internet by pre paid or post paid customers. The analysis was centred around the idea that different applications are used by different users based on their own needs and convenience. Usage pattern & frequency of use is a matter of personal choice. The paper highlights applications that are most frequently used by the customers, thereby resulting in consuming majority of the data chunk and rating the importance of these applications based on their data usability. The attitudes, usage patterns and behaviour governing data pack users are, hence, of much importance to handset manufacturers and also to cellular service providers. Sample was drawn from 436 respondents of Nagpur and the respondent profile was based on age, gender and profession. A structured questionnaire was administered to record and study the perceptions of respondents on data pack usage, the different applications they use and the duration of usage of such applications. Data was analysed using simple tools like percentages, means, etc and are depicted in the form of charts and graphs. Major findings include difference in data usage among genders, variation in use of applications across different age groups, time spent by various groups of respondents on different applications through mobile internet data and many more.
Traffic Patterns on Different Internet Access Technologies
The incentives for traffic measurements and traffic pattern analysis are increasing. New technologies for accessing the Internet like high speed packet data services in mobile networks are deployed, which imply in many cases that users share transfer channel resources. Applications and user behaviour from a wired environment may have a negative impact on these resources. This paper discusses the need for deeper knowledge of application and user behaviour in new access technologies and also suggests measuring and analysis methods.
Understanding Usage Pattern of Korean Smartphone Users
In this paper, using a comprehensive smartphone usage logging system (a client app and a server), various statistical analysis results from more than 800 man-days usage logs are presented. The analysis shows 1) significant difference in usage frequency and time statistics among the applications, 2) strong popularity in social network application category and 3) little usage variation between weekdays and weekends, but less frequent usage in night time.