Ahmad Rahmati

Context-for-wireless: context-sensitive energy-efficient wireless data transfer

Ubiquitous connectivity on mobile devices will enable numerous new applications in healthcare and multimedia. We set out to check how close we are towards ubiquitous connectivity in our daily life. The findings from our recent field-collected data from an urban university population show that while network availability is decent, the energy cost of network interfaces poses a great challenge. Based on our findings, we propose to leverage the complementary strength of Wi-Fi and cellular networks by choosing wireless interfaces for data transfers based on network condition estimation. We show that an ideal selection policy can more than double the battery lifetime of a commercial mobile phone, and the improvement varies with data transfer patterns and Wi-Fi availability. We formulate the selection of wireless interfaces as a statistical decision problem. The key to attaining the potential battery improvement is to accurately estimate Wi-Fi network conditions without powering up its network interface. We explore the use of different context information, including time, history, cellular network conditions, and device motion, for this purpose. We consequently devise algorithms that can effectively learn from context information and estimate the probability distribution of Wi-Fi network conditions. Simulations based on field-collected traces show that our algorithms can improve the average battery lifetime of a commercial mobile phone for a three-channel electrocardiogram (ECG) reporting application by 39%, very close to the theoretical upper bound of 42%. Finally, our field validation of our most simple algorithm demonstrates a 35% improvement in battery lifetime.

LiveLab: measuring wireless networks and smartphone users in the field

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.

Users and batteries: interactions and adaptive energy management in mobile systems

Battery lifetime has become one of the top usability concerns of mobile systems. While many endeavors have been devoted to improving battery lifetime, they have fallen short in understanding how users interact with batteries. In response, we have conducted a systematic user study on battery use and recharge behavior, an important aspect of user-battery interaction, on both laptop computers and mobile phones. Based on this study, we present three important findings: 1) most recharges happen when the battery has substantial energy left, 2) a considerable portion of the recharges are driven by context (location and time), and those driven by battery levels usually occur when the battery level is high, and 3) there is great variation among users and systems. These findings indicate that there is substantial opportunity to enhance existing energy management policies, which solely focus on extending battery lifetime and often lead to excess battery energy upon recharge, by adapting the aggressiveness of the policy to match the usage and recharge patterns of the device. We have designed, deployed, and evaluated a user- and statistics-driven energy management system, Llama, to exploit the battery energy in a user-adaptive and user-friendly fashion to better serve the user. We also conducted a user study after the deployment that shows Llama effectively harvests excess battery energy for a better user experience (brighter display) or higher quality of service (more application data) without a noticeable change in battery lifetime.

Understanding human-battery interaction on mobile phones

Mobile phone users have to deal with limited battery lifetime through a reciprocal process we call human-battery interaction (HBI). We conducted three user studies in order to understand HBI and discover the problems in existing mobile phone designs. The studies include a large-scale international survey, a one-month field data collection including quantitative battery logging and qualitative inquiries from ten mobile phone users, and structured interviews with twenty additional mobile phone users. We evaluated various aspects of HBI, including charging behavior, battery indicators, user interfaces for power-saving settings, user knowledge, and user reaction. We find that mobile phone users can be categorized into two types regarding HBI and often have inadequate knowledge regarding phone power characteristics. We provide qualitative and quantitative evidence that problems in state-of-the-art user interfaces has led to under-utilized power-saving settings, under-utilized battery energy, and dissatisfied users. Our findings provide insights into improving mobile phone design for users to effectively deal with the limited battery lifetime. Our work is the first to systematically address HBI on mobile phones and is complementary to the extensive research on energy-efficient design for a longer battery lifetime.

Studying Smartphone Usage: Lessons from a Four-Month Field Study

Many emerging mobile applications and services are based on smartphones. We have performed a four-month field study of the adoption and usage of smartphone-based services by 14 novice teenage users. From the field study, we present the application usage and usage characteristics of our participants. We show that their usage is highly mobile, location-dependent, and serves multiple social purposes. Furthermore, we report qualitative lessons regarding the evaluation of smartphone-based services. In particular, we highlight the cases that an accurate evaluation would require a long-term and/or field study instead of a short or lab-based study, and the cases where studying a particular application independently is insufficient and a holistic study, i.e., involving the whole device, is necessary. We further present guidelines on effectively shortening the length of a study. These lessons are supported in part by five identified contributing factors to usage evolution.

Characterizing web use on smartphones

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.

Fast track article: Human-battery interaction on mobile phones

Mobile phone users have to deal with limited battery lifetime through a reciprocal process we call human-battery interaction. We conducted three user studies in order to understand human-battery interaction and discover the problems in existing designs that prevent users from effectively dealing with the limited battery lifetime. The studies include a large-scale international survey, two long-term field trials including quantitative battery logging and qualitative inquiries, and structured interviews with twenty additional mobile phone users. We evaluated various aspects of human-battery interaction, including charging behavior, battery indicators, user interfaces for power-saving settings, user knowledge, and user reaction. We find that mobile phone users can be categorized into two types regarding human-battery interaction and often have inadequate knowledge regarding phone power characteristics. We provide qualitative and quantitative evidence that problems in state-of-the-art user interfaces have led to under-utilized power-saving settings, under-utilized battery energy, and dissatisfied users. Our findings provide insights into improving mobile phone design for users to effectively deal with the limited battery lifetime. Our work is the first to systematically address human-battery interaction on mobile phones and is complementary to the extensive research on energy-efficient design for a longer battery lifetime.

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

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.

xShare: supporting impromptu sharing of mobile phones

Loaded with personal data, e.g. photos, contacts, and call history, mobile phones are truly personal devices. Yet it is often necessary or desirable to share our phones with others. This is especially true as mobile phones are integrating features conventionally provided by other dedicated devices, from MP3 players to games consoles. Unfortunately, when we lend our phones to others, we give away complete access because existing phones assume a single user and provide little protection for private data and applications. In this work, we present xShare, a protection solution to address this problem. xShare allows phone owners to rapidly specify what they want to share and place the phone into a restricted mode where only the data and applications intended for sharing can be accessed. We first present findings from two motivational user studies based on which we provide the design requirements of xShare. We then present the design of xShare based on file-level access control. We describe the implementation of xShare on Windows Mobile and report a comprehensive usability evaluation of the implementation, including mea-surements and user studies. The evaluation demonstrates that our xShare implementation has negligible overhead for interactive phone usage, is extremely favored by mobile users, and provides robust protection against attacks by experienced Windows Mobile users and developers.

Context-Based Network Estimation for Energy-Efficient Ubiquitous Wireless Connectivity

Context information brings new opportunities for efficient and effective system resource management of mobile devices. In this work, we focus on the use of context information to achieve energy-efficient, ubiquitous wireless connectivity. Our field-collected data show that the energy cost of network interfaces poses a great challenge to ubiquitous connectivity, despite decent availability of cellular networks. We propose to leverage the complementary strengths of Wi-Fi and cellular interfaces by automatically selecting the most efficient one based on context information. We formulate the selection of wireless interfaces as a statistical decision problem. The challenge is to accurately estimate Wi-Fi network conditions without powering up the network interface. We explore the use of different context information, including time, history, cellular network conditions, and device motion, to statistically estimate Wi-Fi network conditions with negligible overhead. We evaluate several context-based algorithms for the estimation and prediction of current and future network conditions. Simulations using field-collected traces show that our network estimation algorithms can improve the average battery lifetime of a commercial mobile phone for an ECG reporting application by 40 percent, very close to the estimated theoretical upper bound of 42 percent. Furthermore, our most effective algorithm can predict Wi-Fi availability for one and ten hours into the future with 95 and 90 percent accuracy, respectively.