Software for Wearable Devices: Challenges and Opportunities

Wearable devices are a new form of mobile computer system that provides exclusive and user-personalized services. Wearable devices bring new issues and challenges to computer science and technology. This paper summarizes the development process and the categories of wearable devices. In addition, we present new key issues arising in aspects of wearable devices, including operating systems, database management system, network communication protocol, application development platform, privacy and security, energy consumption, human-computer interaction, software engineering, and big data.Comment: 6 pages, 1 figure, for Compsac 201

Towards energy-aware coding practices for Android

This paper studies how the use of different coding practices when developing Android applications influence energy consumption. We consider two common Java/Android programming practices, namely string operations and (non) cached image loading, and we show the energy profile of different coding practices for doing them. With string operations, we compare the performance of the usage of the standard String class to the usage of the StringBuilder class, while with our second practice we evaluate the benefits of image caching with asynchronous loading. We externally measure energy consumption of the example applications using the Trepn profiler application by Qualcomm. Our preliminary results show that selected coding practices do significantly affect energy consumption, in the particular cases of our practice selection, this difference varies between 20% and 50%.This work is funded by the Slovak Research and Development Agency under the contract No. SK-PT2015-0037 and by the Portugal-Slovakia Cooperation FCT Project (Ref. 441), and by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT – Fundacão para a Ciência e a Tecnologia within project POCI-01-0145- FEDER-016718

An Energy-efficient Location Provider for Daily Trips

It is well known that smartphones are energy greedy and their batteries do not last more than a few days. Since mobile phones consumption increased faster than batteries capacities it becomes important to find new ways to reduce their energy consumption. We claim that people using GPS location service everyday should not pay a full energy-cost since most of the time their are taking the same path to commute. Based on these observations, we developed a new implementation of the location API built from Android Location API, which is capable of learning and predict user location on time with an average accuracy of 50 meters while saving energy.Il est généralement admis que les smartphones sont des appareils qui consomment énormément d'énergie et qui nécessitent d'être rechargés régulièrement. Cet état de fait est en partie lié à l'évolution de la consommation de ces derniers qui augmente plus rapidement que celle de la capacité des batteries. Aussi, nous observons qu'une grande partie de la population utilise la localisation par GPS quotidiennement et en parcourant généralement les mêmes routes afin de, par exemple, connaître les conditions de circulation ou du temps restant. Á partir de ces observations, nous proposons une nouvelle mise en {\oe}uvre de l'API de localisation, basée sur celle d'Android, capable d'apprendre et de prédire la position des utilisateurs avec une précision moyenne de cinquante mètres tout en diminuant sa consommation au fil du temps

Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

Evaluating the impact of caching on the energy consumption and performance of progressive web apps

Context. Since today mobile devices have limited battery life, the energy consumption of the software running on them can play a strong role with respect to the success of mobile-based businesses. Progressive Web Applications (PWAs) are built using common web technologies like HTML, CSS, and JavaScript and are commonly used for providing a better user experience to mobile users. Caching is the main technique used by PWA developers for optimizing network usage and for providing a meaningful experience even when the user's device is offline. Goal. This paper aims at assessing the impact of caching on both the energy consumption and performance of PWAs. Method. We conducted an empirical experiment targeting 9 real PWAs developed by third-party developers. The experiment is designed as a 1 factor-2 treatments study, with the usage of caching as the single factor and the status of the cache as treatments (empty vs populated cache). The response variables of the experiment are (i) the energy consumption of the mobile device and (ii) the page load time of the PWAs. The experiment is executed on a real Android device running the Mozilla Firefox browser. Results. Our results show that PWAs do not consume significantly different amounts of energy when loaded either with an empty or populated cache. However, the page load time of PWAs is significantly lower when the cache is already populated, with a medium effect size. Conclusions. This study confirms that PWAs are promising in terms of energy consumption and provides evidence that caching can be safely exploited by PWA developers concerned with energy consumption. The study provides also empirical evidence that caching is an effective technique for improving the user experience in terms of page loading time of PWAs

PADA: Power-aware development assistant for mobile sensing applications

� 2016 ACM. We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.N

Data-Oriented Characterization of Application-Level Energy Optimization

Abstract. Empowering application programmers to make energy-aware decisions is a critical dimension of energy optimization for computer systems. In this paper, we study the energy impact of alternative data management choices by programmers, such as data access patterns, data precision choices, and data organization. Second, we attempt to build a bridge between application-level energy management and hardware-level energy management, by elucidating how various application-level data management features respond to Dynamic Voltage and Frequency Scal-ing (DVFS). Finally, we apply our findings to real-world applications, demonstrating their potential for guiding application-level energy opti-mization. The empirical study is particularly relevant in the Big Data era, where data-intensive applications are large energy consumers, and their energy efficiency is strongly correlated to how data are maintained and handled in programs

Dos and Don'ts in Mobile Phone Sensing Middleware: Learning from a Large-Scale Experiment

International audienceMobile phone sensing contributes to changing the way we approach science: massive amount of data is being contributed across places and time, and paves the way for advanced analyses of numerous phenomena at an unprecedented scale. Still, despite the extensive research work on enabling resource-efficient mobile phone sensing with a very-large crowd, key challenges remain. One challenge is facing the introduction of a new heterogeneity dimension in the traditional middleware research landscape. The middleware must deal with the heterogeneity of the contributing crowd in addition to the system's technical heterogeneities. In order to tackle these two heterogeneity dimensions together, we have been conducting a large-scale empirical study in cooperation with the city of Paris. Our experiment revolves around the public release of a mobile app for urban pollution monitoring that builds upon a dedicated mobile crowd-sensing middleware. In this paper, we report on the empirical analysis of the resulting mobile phone sensing efficiency from both technical and social perspectives, in face of a large and highly heterogeneous population of participants. We concentrate on the data originating from the 20 most popular phone models of our user base, which represent contributions from over 2,000 users with 23 million observations collected over 10 months. Following our analysis, we introduce a few recommendations to overcome-technical and crowd-heterogeneities in the implementation of mobile phone sensing applications and supporting middleware