Context information brings new opportunities for efficient and effective applications and services on mobile devices. A wide range of research has exploited context dependency, i.e. the relations between context(s) and the outcome, to achieve significant, quantified, performance gains for a variety of applications and services. These works typically have to deal with the challenges of multiple context sources leading to a sparse training dataset, and the challenges of energy hungry context sensors. Often, they address these challenges in an application specific and ad-hoc manner. We liberate mobile application designers and researchers from these burdens by providing a methodical approach to these challenges. In particular, we 1) define and measure the context-dependency of three principal types of mobile usage (visited websites, phone calls, and app usage) in an application agnostic yet practical manner, providing insight into the performance of potential application. 2) Address the challenge of data sparseness when dealing with multiple context sources in a systematic manner. 3) Present SmartContext to address the energy challenge by automatically selecting among context sources while ensuring a minimum accuracy for each estimation. Our analysis and findings are based on one year of usage and context traces collected in real-life settings from 24 iPhone users. We present findings regarding the context dependency of three types of mobile usage from 24 users, yet our methodology and the lessons we learn can be readily extended to other types of usage as well as system resources. Our findings guide the development of context aware systems, and highlight the challenges and expectations regarding the context dependency of mobile usage.

Is it possible to migrate TCP/IP flows between different networks on modern mobile devices without infrastructure support or protocol changes? To answer this question, we make three research contributions: 1) We report a comprehensive characterization of IP traffic on 27 iPhone 3GS users for three months. 2) Driven by these findings, we devise two simple, effective, and easily deployable system mechanisms to support seamless flow migration without network support, and extensively evaluate their effectiveness using our field collected traces of real-life usage. Wait-n-Migrate leverages the fact that most flows are short lived. It establishes new flows on newly available networks but allows preexisting flows on the old network to terminate naturally. Resumption Agent takes advantage of the resumption functionality of modern protocols to securely resume flows without application intervention. Combined, they provide an unprecedented opportunity to immediately deploy policies that leverage multiple networks to improve the performance, efficiency, and connectivity of mobile devices. 3) We report an iPhone-based implementation of these system mechanisms and demonstrate their overhead to be negligible. Furthermore, we employ a sample switching policy, AutoSwitch, to demonstrate their performance. Through traces and field measurements, we show that AutoSwitch reduces user disruptions by an order of magnitude.

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.

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.

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.

The present report is an empirical analysis of smartphone personalisation. We collected data from two groups of users to measure how they adapt the content, interface and physical appearance of their devices. This user-driven personalisation is measured with a simple heuristic approach to quantify the behaviour. Using these scores, we explore how users differ from each other in how they personalise their smartphones with a focus on gender differences, usability and device usage in the wild. Among our findings are that not all users personalise their smartphones, females and males personalise their iPhones differently, and those who personalised their phones more tended to rate it as more usable. The users who personalised more also used their device for greater periods of time on a broader range of applications. For instance, individuals who adapted their iPhones to a greater degree also accessed the Web more often and spent more time browsing once it was accessed. We conclude with a discussion of possible factors underlying the large user diversity of smartphone personalisation found in this research.

In this work, we offer a novel system, MicroStation (μStation) that allows ubiquitous data broadcasting applications using the FM radio on mobile devices such as smartphones. μStation includes two key modules to enable data broadcasting based on existing mobile FM radio hardware. Channel Selector assigns different FM channels to neighboring μStation broadcasters to avoid collision and guides μStation listeners to find their broadcasting of interest. Data Codec realizes bit-level communication between mobile devices through existing FM radio hardware. We describe an implementation of μStation on the Nokia N900 smartphone, and provide low-level APIs and services to support application development. We also demonstrate two representative applications: Facebook-FM and Sync-Flash. These applications demonstrate the capability of μStation to readily enable a new class of ubiquitous data broadcasting applications on mobile devices.

In this paper, we present a comprehensive study on sensor-assisted video encoding (SaVE) schemes for video capturing on mobile devices in real-world environments. Our purpose is to reduce the computational complexity of video encoding by leveraging sensors that are increasingly available on mobile devices, e.g., accelerometers and digital compasses. Motion estimation is a key component of video encoding. In this paper, SaVE calculates the rotational movement of a camera (on mobile devices) and then infers the global motion in the camera imager. SaVE subsequently employs the estimated global motion as predictors to simplify motion estimation algorithms for state-of-the-art H.264/AVC video coding. We have constructed a prototype of SaVE and evaluated its performance with a pair of accelerometers, a digital compass, and their combination. Our experimental results show that SaVE can significantly reduce the computations of motion estimation while achieving equal or better video quality. Our results also show that SaVE has a strong noise-resistant capability. Therefore, it can be practically employed in real-world environments.

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.

Mobile phones are truly personal devices loaded with personal data such as photos, contacts, and call history. 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. Yet existing phones assume a single user and provide little protection for private data and applications when a phone is shared. That is, when we lend our phones to others, we give away complete access. 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 two formative user studies and derive the design requirements of xShare. We then offer the design of xShare based on file-level access control. We describe the implementation of xShare on Windows Mobile and report a comprehensive evaluation, including performance measurements, usability, and a one-month field trial.

Motion estimation is a key component of modern video encoding and is very compute-intensive. We present a novel Sensor-assisted Video Encoding (SaVE) method to reduce the computational complexity of motion estimation in H.264/AVC encoders, leveraging accelerometers and digital compasses that are increasingly available on mobile devices. Using these sensors, SaVE calculates the rotational movement of a camera and then infers the global motion in the camera image sensor; it subsequently employs the estimated global motion to simplify the state-of-the-art motion estimation algorithms, UMHS and EPZS used in H.264/AVC encoders. We have constructed a prototype of SaVE and report extensive evaluation of it. Our experimental results show that SaVE can reduce the computations of UMHS and EPZS algorithms by up to 27% and 18%, respectively, while achieving the same or better video quality.

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.

Consumer electronics and mobile devices intended for pervasive applications are often subject to shaking that makes their screen difficult to read. To address this usability challenge, we present NoShake, a system for screen content stabilization. NoShake utilizes a single accelerometer, now present in numerous consumer electronics and mobile devices. The core of NoShake is a physics inspired model that dynamically compensates for the device shaking by shifting the screen content opposite the direction of the shake. The model is efficient, parametric, and can be fine tuned based on shaking pattern detection. We implement a prototype of NoShake on an Apple iPhone and conduct user studies in a number of scenarios, which highlight the strengths as well as limitations of NoShake in coping with shaking devices.

Accelerometers have appeared on many camcorders, cameras and mobile phones. We present algorithms that estimate camera movement from accelerometer readings and apply the estimation to significantly improve the compute-intense motion estimation in video encoding. We have implemented a working prototype that simultaneously captures video and three-axis acceleration data. The video is then compressed with a reference MPEG-2 encoder, modified to incorporate the accelerometer readings to assist motion estimation. Our experimental data shows a two to three times speed improvement for the entire encoding process, in comparison with full search.

Radio Frequency Identification (RFID) technology has the potential to dramatically improve numerous industrial practices. However, it still faces many challenges, including security and reliability, which may limit its use in many application scenarios. While security has received considerable attention, reliability has escaped much of the research scrutiny. In this work, we investigate the reliability challenges in RFID-based tracking applications, where objects (e.g., pallets, packages, and people) tagged with low-cost passive RFID tags pass by the RFID reader's read zone. Our experiments show that the reliability of tag identification is affected by several factors, including the inter-tag distance, the distance between the tag and antenna, the orientation of the tag with respect to the antenna, and the location of the tag on the object. We demonstrate that RFID system reliability can be significantly improved with the application of simple redundancy techniques.

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.

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.