With the recent advent of 4G LTE networks, there has been increasing interest to better understand the performance and power characteristics, compared with 3G/WiFi networks. In this paper, we take one of the first steps in this direction.Using a publicly deployed tool we designed for Android called 4GTest attracting more than 3000 users within 2 months and extensive local experiments, we study the network performance of LTE networks and compare with other types of mobile networks. We observe LTE generally has significantly higher downlink and uplink throughput than 3G and even WiFi, with a median value of 13Mbps and 6Mbps, respectively. We develop the first empirically derived comprehensive power model of a commercial LTE network with less than 6% error rate and state transitions matching the specifications. Using a comprehensive data set consisting of 5-month traces of 20 smartphone users, we carefully investigate the energy usage in 3G, LTE, and WiFi networks and evaluate the impact of configuring LTE-related parameters. Despite several new power saving improvements, we find that LTE is as much as 23 times less power efficient compared with WiFi, and even less power efficient than 3G, based on the user traces and the long high power tail is found to be a key contributor. In addition, we perform case studies of several popular applications on Android in LTE and identify that the performance bottleneck for web-based applications lies less in the network, compared to our previous study in 3G [24]. Instead, the device's processing power, despite the significant improvement compared to our analysis two years ago, becomes more of a bottleneck.

/pdf/a-close-examination-of-performance-and-power-characteristics-2g7pjkqkb1.pdf

A close examination of performance and power characteristics of 4G LTE networks

Statistics: Methods and Applications.

With the advent of instant mobile messaging applications, traditional SMS is in danger of loosing it's reign as the king of mobile messaging. Applications like WhatsApp allow mobile users to send real-time text messages to individuals or groups of friends at no cost. While there is a vast body of research on traditional text messaging practices, little is understood about how and why people have adopted and appropriated instant mobile messaging applications. The goal of this work is to provide a deeper understanding of the motives and perceptions of a popular mobile messaging application called WhatsApp and to learn more about what this service offers above and beyond traditional SMS. To this end, we present insights from two studies an interview study and a large-scale survey highlighting that while WhatsApp offers benefits such as cost, sense of community and immediacy, SMS is still considered a more reliable, privacy preserving technology for mobile communication.

/pdf/what-s-up-with-whatsapp-comparing-mobile-instant-messaging-1cyynt0kji.pdf

What's up with whatsapp?: comparing mobile instant messaging behaviors with traditional SMS

By 2025, when most of today's psychology undergraduates will be in their mid-30s, more than 5 billion people on our planet will be using ultra-broadband, sensor-rich smartphones far beyond the abilities of today's iPhones, Androids, and Blackberries. Although smartphones were not designed for psychological research, they can collect vast amounts of ecologically valid data, easily and quickly, from large global samples. If participants download the right "psych apps," smartphones can record where they are, what they are doing, and what they can see and hear and can run interactive surveys, tests, and experiments through touch screens and wireless connections to nearby screens, headsets, biosensors, and other peripherals. This article reviews previous behavioral research using mobile electronic devices, outlines what smartphones can do now and will be able to do in the near future, explains how a smartphone study could work practically given current technology (e.g., in studying ovulatory cycle effects on women's sexuality), discusses some limitations and challenges of smartphone research, and compares smartphones to other research methods. Smartphone research will require new skills in app development and data analysis and will raise tough new ethical issues, but smartphones could transform psychology even more profoundly than PCs and brain imaging did.

The Smartphone Psychology Manifesto

I am a researcher and graduate student of American Indian descent (Stockbridge-Munsee/Mohican). I have always been very aware of the absence of other American Indian students in postsecondary education programs; have noticed the shortage of American Indians employed as educators, professors, or administrators; and am acutely aware of the number of American Indian populations excluded from qualitative or quantitative data sets as part of mainstream research agendas that are published on the state or national level. This is the beginning of my dissertation research journey, and the data that I have studied thus far demonstrates a ten-year failure of the traditional, Western, and public educational system in Wisconsin to nurture, graduate, and utilize Native Americans from birth through adulthood. This historical failure in the Wisconsin educational systems and organizations has left the First Children and the First People of this country far behind as compared to their other minority and white counterparts (Bowman ). The extent of how far and why they are behind is what I am determined to discover. Anecdotally speaking, I knew in my heart that the few Indians who did survive school accomplished this despite many barriers or whatever politically correct legislation was the popular rhetoric at the time. But in my head I needed to ascertain, scientifically document, and understand the factors that were responsible for this shameful record regarding the education of Native American students and their underrepresentation as professionals within the educational community. In a time of attempts to abolish affirmative action and hearing the promises of leaving no children behind, I have diligently tried to set aside time to conduct research Cultural Differences of Teaching and Learning

Cultural differences in teaching and learning

We present LiveLab, a methodology to measure real-world smartphone usage and wireless networks with a reprogrammable indevice logger designed for long-term user studies. We discuss the challenges of privacy protection and power impact in LiveLab and offer our solutions. We present an iPhone 3GS based deployment of LiveLab with 25 users intended for one year. Early results from the data collection so far highlight the unique strengths and potential of LiveLab. We have two objectives in this position paper. First, we demonstrate the feasibility and capability of LiveLab. By sharing our experience, we seek to advocate LiveLab as a network and user measurement methodology. Second, we present our preliminary findings, and seek feedback from the community regarding what data to collect.

/pdf/livelab-measuring-wireless-networks-and-smartphone-users-in-2uhrq9lrb4.pdf

LiveLab: measuring wireless networks and smartphone users in the field

http://clay.rice.edu/pubs/Tossell-Emoticon-CHB12.pdf

A longitudinal study of emoticon use in text messaging from smartphones

Smartphone technology is penetrating world markets and becoming ubiquitous in most college settings. This study takes a naturalistic approach to explore the use of these devices to support student learning. Students that had never used a smartphone were recruited to participate and reported on their expectations of the value of smartphones to achieve their educational goals. Instrumented iPhones that logged device usage were then distributed to these students to use freely over the course of 1 year. After the study, students again reported on the actual value of their smartphones to support their educational goals. We found that students' reports changed substantially before and after the study; specifically, the utility of the smartphone to help with education was perceived as favorable prior to use, and then, by the end of the study, they viewed their phones as detrimental to their educational goals. Although students used their mobile device for informal learning and access to school resources according to the logged data, they perceived their iPhones as a distraction and a competitor to requisite learning for classroom performance. [ABSTRACT FROM AUTHOR]

You can lead a horse to water but you cannot make him learn: Smartphone use in higher education

The current paper establishes empirical patterns associated with mobile internet use on smartphones and explores user differences in these behaviors. We apply a naturalistic and longitudinal logs-based approach to collect real usage data from 24 iPhone users in the wild. These data are used to describe smartphone usage and analyze revisitation patterns of web browsers, native applications, and physical locations where phones are used. Among our findings are that web page revisitation through browsers occurred very infrequently (approximately 25% of URLs are revisited by each user), bookmarks were used sparingly, physical traversing patterns mirrored virtual (internet) traversing patterns and users systematically differed in their web use. We characterize these differences and suggest ways to support users with enhanced design of smartphone technologies and content.

http://clay.rice.edu/pubs/Tossell-CHI12.pdf

Characterizing web use on smartphones

Previous studies have found that smartphone users differ by orders of magnitude. We explore this variability to understand how users install and use native applications in ecologically-valid environments. A quasi-experimental approach is applied to compare how users in different socio-economic status (SES) groups adopt new smartphone technology along with how applications are installed and used. We present a longitudinal study of 34 iPhone 3GS users. 24 of these participants were chosen from two carefully selected SES groups who were otherwise similar and balanced. Usage data collected through an in-device programmable logger, as well as several structured interviews, identify similarities, differences, and trends, and highlight systematic differences in smartphone usage. A group of 10 lower SES participants were later recruited and confirm the influence of SES diversity on device usage. Among our findings are that a large number of applications were uninstalled, lower SES groups spent more money on applications and installed more applications overall, and the lowest SES group perceived the usability of their iPhones poorly in comparison to the other groups. We further discuss the primary reasons behind this low score, and suggest design implications to better support users across SES brackets.

Chad C. Tossell

Papers

LiveLab: measuring wireless networks and smartphone users in the field

A longitudinal study of emoticon use in text messaging from smartphones

You can lead a horse to water but you cannot make him learn: Smartphone use in higher education

Characterizing web use on smartphones

Exploring iPhone usage: the influence of socioeconomic differences on smartphone adoption, usage and usability