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Exploring Variety Seeking Behavior
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How the Availability of Wi-Fi Connections Influences the Use of Mobile Devices
"... Abstract Several aspects might influence the way users operate their mobile devices in a mobile context. In this work, we show how the presence or absence of a Wi-Fi connection influences the amount of data traffic generated by mobile devices. Our results show that the probability of users to gener ..."
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Abstract Several aspects might influence the way users operate their mobile devices in a mobile context. In this work, we show how the presence or absence of a Wi-Fi connection influences the amount of data traffic generated by mobile devices. Our results show that the probability of users to generate data traffic while connected to Wi-Fi is twice as high as when a cellular connection only is available. Furthermore, we observe that an almost constant amount of data traffic is generated over a day, although it slightly increases in the late afternoon. Last but not least, we observe that fair-use policies do not seem to influence the behavior of mobile users with respect to the amount of traffic they generate over different weeks of a month. We ground our analysis on the Device Analyzers data set, which contains detailed records of mobile phone usage of more than 17,000 users from all over the world. Building upon these preliminary quantitative results, we outline how the availability of data for mobile users can be improved by combining mobility and phone usage prediction with knowledge about the temporal and spatial availability of Wi-Fi connections.
The Device Analyzer Competition
"... Abstract Device Analyzer is an Android app designed to collect user interaction data from smartphones. Over 18,000 study participants from around the world have run the app and provided us with usage statistics. Many study participants have allowed us to share their data with other researchers. Thi ..."
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Abstract Device Analyzer is an Android app designed to collect user interaction data from smartphones. Over 18,000 study participants from around the world have run the app and provided us with usage statistics. Many study participants have allowed us to share their data with other researchers. This allows our community to research hypotheses or validate findings from other studies. We ran the 2014 Ubicomp Programming Competition to encourage the use of Device Analyzer data, and over 170 researchers around the world entered. The following papers were selected to be presented at the competition workshop. In this paper we provide background information for readers of these papers and share our experiences with others who might be considering sharing research datasets with the community. We describe how we ran the contest, provide a general overview of the responses and discuss the challenges that we encountered.
Social Context Discovery from Temporal App Use Patterns
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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.
Connect the Dots by Understanding User Status and Transitions
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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request
Is This You? Identifying a Mobile User Using Only Diagnostic Features
"... Mobile smart phones capture a great amount of informa-tion about a user across a variety of different data domains. This information can be sensitive and allow for identifying a user profile, thus causing potential threats to a user’s pri-vacy. Our work shows that diagnostic information that is not ..."
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Mobile smart phones capture a great amount of informa-tion about a user across a variety of different data domains. This information can be sensitive and allow for identifying a user profile, thus causing potential threats to a user’s pri-vacy. Our work shows that diagnostic information that is not considered sensitive, could be used to identify a user af-ter just three consecutive days of monitoring. We have used the Device Analyzer dataset to determine what features of a mobile device are important in identifying a user. Many mobile games and applications collect diagnostic data as a means of identifying or resolving issues. Diagnostic data is commonly accepted as less sensitive information. Our experimental results demonstrate that using only diagnostic features like hardware statistics and system settings, a user’s device can be identified at an accuracy of 94 % with a Naive Bayes classifier.
A Case for Battery Charging-Aware Power Management and Deferrable Task Scheduling in Smartphones
"... Prior battery-aware systems research has focused on dis-charge power management in order to maximize the us-able battery lifetime of a device. In order to achieve the vision of perpetual mobile device operation, we pro-pose that software also needs to carefully consider the process of battery chargi ..."
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Prior battery-aware systems research has focused on dis-charge power management in order to maximize the us-able battery lifetime of a device. In order to achieve the vision of perpetual mobile device operation, we pro-pose that software also needs to carefully consider the process of battery charging. This is because the power consumed by the system when plugged in can influence the rate of battery charging, and hence, the availability of the system to the user. We characterize the charging process of a Nexus 4 smartphone and analyze the char-ging behaviors of anonymous Nexus 4 users using the Device Analyzer dataset. We find that there is potential for software schedulers to increase device availability by distributing tasks across the charging period. We estim-ate that approximately 53 % of the users we examined could benefit from up to 18.9 % improvement in net en-ergy gained by the battery while charging. Accordingly, we propose new threads of research in charging-aware power management and deferrable task scheduling that could improve overall availability for a significant por-tion of smartphone users. 1
