6,769 research outputs found
Power Consumption Analysis, Measurement, Management, and Issues:A State-of-the-Art Review of Smartphone Battery and Energy Usage
The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed
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
Google Play apps ERM: (energy rating model) multi-criteria evaluation model to generate tentative energy ratings for Google Play store apps
A common issue that is shared among Android smartphones users was and still related to saving their batteries power and to avoid the need of using any recharging resources. The tremendous increase in smartphone usage is clearly accompanied by an increase in the need for more energy. This preoperational relationship between modern technology and energy generates energy-greedy apps, and therefore power-hungry end users. With many apps falling under the same category in an app store, these apps usually share similar functionality. Because developers follow different design and development schools, each app has its own energy-consumption habits. Since apps share similar features, an end-user with limited access to recharging resources would prefer an energy-friendly app rather than a popular energy-greedy app. However, app stores give no indication about the energy behaviour of the apps they offer, which causes users to randomly choose apps without understanding their energy-consumption behaviour. Furthermore, with regard to the research questions about the fact that power saving application consumes a lot of electricity, past studies clearly indicate that there is a lot of battery depletion due to several factors. This problem has become a major concern for smartphone users and manufacturers. The main contribution of our research is to design a tool that can act as an effective decision support factor for end users to have an initial indication of the energy-consumption behaviour of an application before installing it. The core idea of the “before-installation” philosophy is simplified by the contradicting concept of installing the app and then having it monitored and optimized. Since processing requires power, avoiding the consumption of some power in order to conserve a larger amount of power should be our priority. So instead, we propose a preventive strategy that requires no processing on any layer of the smartphone. To address this issue, we propose a star-rating evaluation model (SREM), an approach that generates a tentative energy rating label for each app. To that end, SREM adapts current energy-aware refactoring tools to demonstrate the level of energy consumption of an app and presents it in a star-rating schema similar to the Ecolabels used on electrical home appliances.
The SREM will also inspire developers and app providers to come up with multiple energy-greedy versions of the same app in order to suit the needs of different categories of users and rate their own apps.
We proposed adding SREM to Google Play store in order to generate the energy-efficiency label for each app which will act as a guide for both end users and developers without running any processes on the end-users smartphone. Our research also reviews relevant existing literature specifically those covering various energy-saving techniques and tools proposed by various authors for Android smartphones. A secondary analysis has been done by evaluating the past research papers and surveys that has been done to assess the perception of the users regarding the phone power from their battery. In addition, the research highlights an issue that the notifications regarding the power saving shown on the screen seems to exploit a lot of battery. Therefore, this study has been done to reflect the ways that could help the users to save the phone battery without using any power from the same battery in an efficient manner. The research offers an insight into new ways that could be used to more effectively conserve smartphone energy, proposing a framework that involves end users on the process.Um problema comum entre utilizadores de smartphones Android tem sido a necessidade de economizar a energia das baterias, de modo a evitar a utilização de recursos de recarga. O aumento significativo no uso de smartphones tem sido acompanhado por um aumento, tambĂ©m significativo, na necessidade de mais energia. Esta relação operacional entre tecnologia moderna e energia gera aplicações muito exigentes no seu consumo de energia e, portanto, perfis de utilizadores que requerem nĂveis de energia crescentes. Com muitos das aplicações que se enquadram numa mesma categoria da loja de aplicações (Google Store), essas aplicações geralmente tambĂ©m partilham funcionalidades semelhantes. Como os criadores destas aplicações seguem abordagens diferentes de diversas escolas de design e desenvolvimento, cada aplicação possui as suas prĂłprias caraterĂsticas de consumo de energia. Como as aplicações partilham recursos semelhantes, um utilizador final com acesso limitado a recursos de recarga prefere uma aplicação que consome menos energia do que uma aplicação mais exigente em termos de consumo energĂ©tico, ainda que seja popular. No entanto, as lojas de aplicações nĂŁo fornecem uma indicação sobre o comportamento energĂ©tico das aplicações oferecidas, o que faz com que os utilizadores escolham aleatoriamente as suas aplicações sem entenderem o correspondente comportamento de consumo de energia. Adicionalmente, no que diz respeito Ă questĂŁo de investigação, a solução de uma aplicação de economia de energia consume muita eletricidade, o que a torna limitada; estudos anteriores indicam claramente que há muita perda de bateria devido a vários fatores, nĂŁo constituindo solução para muitos utilizadores e para os fabricantes de smartphones. A principal contribuição de nossa pesquisa Ă© projetar uma ferramenta que possa atuar como um fator de suporte Ă decisĂŁo eficaz para que os utilizadores finais tenham uma indicação inicial do comportamento de consumo de energia de uma aplicação, antes de a instalar. A ideia central da filosofia proposta Ă© a de atuar "antes da instalação", evitando assim a situação em se instala uma aplicação para perceber Ă posteriori o seu impacto no consumo energĂ©tico e depois ter que o monitorizar e otimizar (talvez ainda recorrendo a uma aplicação de monitorização do consumo da bateria, o que agrava ainda mais o consumo energĂ©tico). Assim, como o processamento requer energia, Ă© nossa prioridade evitar o consumo de alguma energia para conservar uma quantidade maior de energia. Portanto, Ă© proposta uma estratĂ©gia preventiva que nĂŁo requer processamento em nenhuma camada do smartphone.
Para resolver este problema, Ă© proposto um modelo de avaliação por classificação baseado em nĂveis e identificado por estrelas (SREM). Esta abordagem gera uma etiqueta de classificação energĂ©tica provisĂłria para cada aplicação. Para isso, o SREM adapta as atuais ferramentas de refatoração com reconhecimento de energia para demonstrar o nĂvel de consumo de energia de uma aplicação, apresentando o resultado num esquema de classificação por estrelas semelhante ao dos rĂłtulos ecolĂłgicos usados em eletrodomĂ©sticos. O SREM tambĂ©m se propõe influenciar quem desenvolve e produz as aplicações, a criarem diferentes versões destas, com diferentes perfis de consumo energĂ©tico, de modo a atender Ă s necessidades de diferentes categorias de utilizadores e assim classificar as suas prĂłprias aplicações. Para avaliar a eficiĂŞncia do modelo como um complemento Ă s aplicações da loja Google Play, que atuam como uma rotulagem para orientação dos utilizadores finais. A investigação tambĂ©m analisa a literatura existente relevante, especificamente a que abrange as várias tĂ©cnicas e ferramentas de economia de energia, propostas para smartphones Android. Uma análise secundária foi ainda realizada, focando nos trabalhos de pesquisa que avaliam a perceção dos utilizadores em relação Ă energia do dispositivo, a partir da bateria. Em complemento, a pesquisa destaca um problema de que as notificações sobre a economia de energia mostradas na tela parecem explorar muita bateria. Este estudo permitiu refletir sobre as formas que podem auxiliar os utilizadores a economizar a bateria do telefone sem usar energia da mesma bateria e, mesmo assim, o poderem fazer de maneira eficiente. A pesquisa oferece uma visĂŁo global das alternativas que podem ser usadas para conservar com mais eficiĂŞncia a energia do smartphone, propondo um modelo que envolve os utilizadores finais no processo.Un problème frĂ©quent rencontrĂ© par les utilisateurs de smartphones Android a Ă©tĂ©, tout en l’étant toujours, d’économiser leur batterie et d’éviter la nĂ©cessitĂ© d’utiliser des ressources de recharge. La croissance considĂ©rable de l’utilisation des smartphones s’accompagne clairement d’une augmentation des besoins en Ă©nergie. Cette relation prĂ©opĂ©rationnelle entre la technologie moderne et l’énergie gĂ©nère des applications gourmandes en Ă©nergie, et donc des utilisateurs finaux qui le sont tout autant. De nombreuses applications relevant de la mĂŞme catĂ©gorie dans une boutique partagent gĂ©nĂ©ralement des fonctionnalitĂ©s similaires. Étant donnĂ© que les dĂ©veloppeurs adoptent diffĂ©rentes approches de conception et de dĂ©veloppement, chaque application a ses propres caractĂ©ristiques de consommation d’énergie. Comme les applications partagent des fonctionnalitĂ©s similaires, un utilisateur final disposant d’un accès limitĂ© aux ressources de recharge prĂ©fĂ©rerait une application Ă©coĂ©nergĂ©tique plutĂ´t qu’une autre gourmande en Ă©nergie. Cependant, les boutiques d’applications ne donnent aucune indication sur le comportement Ă©nergĂ©tique des applications qu’elles proposent, ce qui incite les utilisateurs Ă choisir des applications au hasard sans comprendre leurs caractĂ©ristiques en ce domaine. En outre, en ce qui concerne les questions de recherche sur le fait que les applications d’économie d’énergie consomment beaucoup d’électricitĂ©, des Ă©tudes antĂ©rieures indiquent clairement que la dĂ©charge d’une batterie est due Ă plusieurs facteurs. Ce problème est devenu une prĂ©occupation majeure pour les utilisateurs et les fabricants de smartphones. La principale contribution de notre Ă©tude est de concevoir un outil qui peut agir comme un facteur d’aide efficace Ă la dĂ©cision pour que les utilisateurs finaux aient une indication initiale du comportement de consommation d’énergie d’une application avant de l’installer. L’idĂ©e de base de la philosophie « avant l’installation » est simplifiĂ©e par le concept contradictoire d’installer l’application pour ensuite la contrĂ´ler et l’optimiser. Puisque les opĂ©rations de traitement exigent de l’énergie, Ă©viter la consommation d’une partie d’entre elles pour l’économiser devrait ĂŞtre notre prioritĂ©. Nous proposons donc une stratĂ©gie prĂ©ventive qui ne nĂ©cessite aucun traitement sur une couche quelconque du smartphone. Pour rĂ©soudre ce problème, nous proposons un modèle d’évaluation au moyen d’étoiles (star-rating evaluation model ou SREM), une approche qui gĂ©nère une note Ă©nergĂ©tique indicative pour chaque application. Ă€ cette fin, le SREM adapte les outils actuels de refactoring sensibles Ă l’énergie pour dĂ©montrer le niveau de consommation d’énergie d’une application et la prĂ©sente dans un schĂ©ma de classement par Ă©toiles similaire aux labels Ă©cologiques utilisĂ©s sur les appareils Ă©lectromĂ©nagers. Le SREM incitera Ă©galement les dĂ©veloppeurs et les fournisseurs d’applications Ă mettre au point plusieurs versions avides d’énergie d’une mĂŞme application afin de rĂ©pondre aux besoins des diffĂ©rentes catĂ©gories d’utilisateurs et d’évaluer leurs propres applications. Nous avons proposĂ© d’ajouter le SREM au Google Play Store afin de gĂ©nĂ©rer le label d’efficacitĂ© Ă©nergĂ©tique pour chaque application. Celui-ci servira de guide Ă la fois pour les utilisateurs finaux et les dĂ©veloppeurs sans exĂ©cuter de processus sur le smartphone des utilisateurs finaux. Notre recherche passe Ă©galement en revue la littĂ©rature existante pertinente, en particulier celle qui couvre divers outils et techniques d’économie d’énergie proposĂ©s par divers auteurs pour les smartphones Android. Une analyse secondaire a Ă©tĂ© effectuĂ©e en Ă©valuant les documents de recherche et les enquĂŞtes antĂ©rieurs qui ont Ă©tĂ© rĂ©alisĂ©s pour Ă©valuer la perception des utilisateurs concernant l’alimentation tĂ©lĂ©phonique depuis leur batterie. En outre, l’étude met en Ă©vidence un problème selon lequel les notifications concernant les Ă©conomies d’énergie affichĂ©es Ă l’écran semblent elles-mĂŞmes soumettre les batteries Ă une forte utilisation. Par consĂ©quent, cette Ă©tude a Ă©tĂ© entreprise pour reflĂ©ter les façons qui pourraient aider les utilisateurs Ă Ă©conomiser efficacement la batterie de leur tĂ©lĂ©phone sans pour autant la dĂ©charger. L’étude offre un bon aperçu des nouvelles façons d’économiser plus efficacement l’énergie des smartphones, en proposant un cadre qui implique les utilisateurs finaux dans le processus
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iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings
Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs
The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System
Combining low cost wireless EEG sensors with smartphones offers novel
opportunities for mobile brain imaging in an everyday context. We present a
framework for building multi-platform, portable EEG applications with real-time
3D source reconstruction. The system - Smartphone Brain Scanner - combines an
off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such
represents the first fully mobile system for real-time 3D EEG imaging. We
discuss the benefits and challenges of a fully portable system, including
technical limitations as well as real-time reconstruction of 3D images of brain
activity. We present examples of the brain activity captured in a simple
experiment involving imagined finger tapping, showing that the acquired signal
in a relevant brain region is similar to that obtained with standard EEG lab
equipment. Although the quality of the signal in a mobile solution using a
off-the-shelf consumer neuroheadset is lower compared to that obtained using
high density standard EEG equipment, we propose that mobile application
development may offset the disadvantages and provide completely new
opportunities for neuroimaging in natural settings
Energy Efficiency Analysis And Optimization For Mobile Platforms
The introduction of mobile devices changed the landscape of computing. Gradually, these devices are replacing traditional personal computer (PCs) to become the devices of choice for entertainment, connectivity, and productivity. There are currently at least 45.5 million people in the United States who own a mobile device, and that number is expected to increase to 1.5 billion by 2015.
Users of mobile devices expect and mandate that their mobile devices have maximized performance while consuming minimal possible power. However, due to the battery size constraints, the amount of energy stored in these devices is limited and is only growing by 5% annually. As a result, we focused in this dissertation on energy efficiency analysis and optimization for mobile platforms. We specifically developed SoftPowerMon, a tool that can power profile Android platforms in order to expose the power consumption behavior of the CPU. We also performed an extensive set of case studies in order to determine energy inefficiencies of mobile applications. Through our case studies, we were able to propose optimization techniques in order to increase the energy efficiency of mobile devices and proposed guidelines for energy-efficient application development. In addition, we developed BatteryExtender, an adaptive user-guided tool for power management of mobile devices. The tool enables users to extend battery life on demand for a specific duration until a particular task is completed. Moreover, we examined the power consumption of System-on-Chips (SoCs) and observed the impact on the energy efficiency in the event of offloading tasks from the CPU to the specialized custom engines. Based on our case studies, we were able to demonstrate that current software-based power profiling techniques for SoCs can have an error rate close to 12%, which needs to be addressed in order to be able to optimize the energy consumption of the SoC. Finally, we summarize our contributions and outline possible direction for future research in this field
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
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