3,494 research outputs found

    Towards integrating mobile devices into dew computing: A model for hour-wise prediction of energy availability

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    With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing/storage features and pervasiveness, but also due to their capability to render services for several hours, even days,without being plugged to the electricity grid. Nonetheless,misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew computing environments. Arguably, having accurate estimations of remaining battery would help to take better advantage of a device's computing capabilities. In this paper, we propose a model to estimate mobile devices battery availability by inspecting traces of real mobile device owner's activity and relevant device state variables. Themodel includes a feature extraction approach to obtain representative features/variables, and a prediction approach, based on regression models and machine learning classifiers. On average, the accuracy of our approach, measured with the mean squared error metric, overpasses the one obtained by a relatedwork. Prediction experiments at five hours ahead are performed over activity logs of 23 mobile users across several months.Fil: Longo, Mathias. University of Southern California; Estados UnidosFil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

    Power Consumption Analysis, Measurement, Management, and Issues:A State-of-the-Art Review of Smartphone Battery and Energy Usage

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    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

    Mobile Big Data Analytics in Healthcare

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    Mobile and ubiquitous devices are everywhere around us generating considerable amount of data. The concept of mobile computing and analytics is expanding due to the fact that we are using mobile devices day in and out without even realizing it. These mobile devices use Wi-Fi, Bluetooth or mobile data to be intermittently connected to the world, generating, sending and receiving data on the move. Latest mobile applications incorporating graphics, video and audio are main causes of loading the mobile devices by consuming battery, memory and processing power. Mobile Big data analytics includes for instance, big health data, big location data, big social media data, and big heterogeneous data. Healthcare is undoubtedly one of the most data-intensive industries nowadays and the challenge is not only in acquiring, storing, processing and accessing data, but also in engendering useful insights out of it. These insights generated from health data may reduce health monitoring cost, enrich disease diagnosis, therapy, and care and even lead to human lives saving. The challenge in mobile data and Big data analytics is how to meet the growing performance demands of these activities while minimizing mobile resource consumption. This thesis proposes a scalable architecture for mobile big data analytics implementing three new algorithms (i.e. Mobile resources optimization, Mobile analytics customization and Mobile offloading), for the effective usage of resources in performing mobile data analytics. Mobile resources optimization algorithm monitors the resources and switches off unused network connections and application services whenever resources are limited. However, analytics customization algorithm attempts to save energy by customizing the analytics process while implementing some data-aware techniques. Finally, mobile offloading algorithm decides on the fly whether to process data locally or delegate it to a Cloud back-end server. The ultimate goal of this research is to provide healthcare decision makers with the advancements in mobile Big data analytics and support them in handling large and heterogeneous health datasets effectively on the move

    SLS: Smart localization service: human mobility models and machine learning enhancements for mobile phone’s localization

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    In recent years we are witnessing a noticeable increment in the usage of new generation smartphones, as well as the growth of mobile application development. Today, there is an app for almost everything we need. We are surrounded by a huge number of proactive applications, which automatically provide relevant information and services when and where we need them. This switch from the previous generation of passive applications to the new one of proactive applications has been enabled by the exploitation of context information. One of the most important and most widely used pieces of context information is location data. For this reason, new generation devices include a localization engine that exploits various embedded technologies (e.g., GPS, WiFi, GSM) to retrieve location information. Consequently, the key issue in localization is now the efficient use of the mobile localization engine, where efficient means lightweight on device resource consumption, responsive, accurate and safe in terms of privacy. In fact, since the device resources are limited, all the services running on it have to manage their trade-off between consumption and reliability to prevent a premature depletion of the phone’s battery. In turn, localization is one of the most demanding services in terms of resource consumption. In this dissertation I present an efficient localization solution that includes, in addition to the standard location tracking techniques, the support of other technologies already available on smartphones (e.g., embedded sensors), as well as the integration of both Human Mobility Modelling (HMM) and Machine Learning (ML) techniques. The main goal of the proposed solution is the provision of a continuous tracking service while achieving a sizeable reduction of the energy impact of the localization with respect to standard solutions, as well as the preservation of user privacy by avoiding the use of a back-end server. This results in a Smart Localization Service (SLS), which outperforms current solutions implemented on smartphones in terms of energy consumption (and, therefore, mobile device lifetime), availability of location information, and network traffic volume

    Energy-Aware Mobile Learning:Opportunities and Challenges

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    Activity forecasting for the Android smartphones using Gaussian processes

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    Masteroppgave i informasjons- og kommunikasjonsteknologi IKT590 2012 – Universitetet i Agder, GrimstadSmartphones have become amazingly popular the last few years, most likely due to the operating system revolution that has taken place, in particular when it comes to the introduction of sophisticated applications. The Android operating system is nowadays known by the most famous and used one thanks to the new and multiple functionalities it offers. Despite the advantages of this revolution, it has come with a high cost: The level of sophistication and the multiple functions now offered typically rely on high quality screen, intensive CPU usage, and extensive networking. As a result, power consumption increases persistently, hindering further advances and lead to a short battery life time. So, techniques for predicting the future behavior of the Android phone are needed and can serve as a basis for power management. The present Master’s thesis seeks to develop a better understanding of how machine learning can be efficient and useful to predict the future behavior of the smartphone. Two learning approaches KNN Regression and Gaussian Process Regression are used to do the forecast of the CPU usage, the screen utilization and the network activity based on historical collected measurements of these three components. The results reveal that the Gaussian Process Regression model is most efficient and accurate than the KNN regression model with a prediction error rate between 1% and 7%. By using the Gaussian Process Regression model, it becomes possible to apply power management techniques to smartphones. In fact, by predicting the future inactivity periods and the idle states of the phone, components of the smartphone can be turned off in the right time and then avoid extensive switching between on- and off-modus, which is expensive in itself and consumes too much power; or by reducing the CPU frequency, disconnecting the phone from the network and adjusting the screen quality to be less bright in idle mode, battery power consumption can be significantly reduced

    Application of data analytics and machine learning on data collected by smartphones to understand human behavioural patterns

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    A growing number of health studies seek to leverage smartphone-based recording to continuously monitor consenting participants’ health behaviours, including those related to mental health, mobility, and activity. So as to better understand health risks and the influence of the environment on human physical and mental health conditions, such studies commonly use smartphones to collect health behaviour relevant metrics such as screen state, app usage, location, activity level, browsing behaviour, etc. They also typically use survey instruments incorporating questionnaires, voice recordings, photos, multi-media content on which the user is asked to provide feedback, etc. When the data volume and variety grow substantially --- such as is common with sensed data --- then challenges associated with data quantity, quality, diversity, trustworthiness, etc. also increase significantly. Because most health scientists are unfamiliar with tools and concepts required for effective analysis of such high-volume and high-velocity data, it is challenging for health scientists alone to perform the computationally intensive analyses needed to secure certain types of insight from the collected data. The primary objective of this thesis is to provide computational mechanisms to support research teams associated with 3 distinct case studies utilizing smartphone-based data, so as to help obtain insights accessible to team health scientists. The data sets for these three studies were collected from participants using a pre-existing smartphone based application named Ethica. Such data was accumulated over a period ranging from 2 weeks to 6 months – with the study period differing across the three studies – through a set of surveys and mobile sensors such as those for the battery, screen state, GPS, etc. This thesis addresses three significant challenges associated with the extraction and processing of smartphone data. The first is the computational burden and intricacies associated with data extraction, preprocessing and analytic steps. The second consists of a need for handling omitted and missing data points with the help of machine learning and statistical methods. The final challenge covered here is to secure valuable findings from these data sets through exploratory analysis following examination of participant adherence patterns and evaluation of the quantity and quality of the data collected. The methods applied in this thesis are useful for other studies using the Ethica platform because of the shared structure of Ethica datasets and the capacity of the code to be reused and readily adapted for other such datasets
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