1,503 research outputs found
Measuring Power Consumption for Image Processing on Android Smartphone
The energy consumption of smartphones can be undertaken in multiple levels of hardware and software. Generally, there are two approaches in measuring power consumption of a smartphone application which are the measurement-based and estimation-based methods. The goal of this study is to compare the two power consumption measuring approaches in quantifying the power consumed by image processing applications in Android smartphone. For measurement-based approach, a simple wattmeter is designed whereas for the estimation-based approach, an Android application called the PowerTutor will be utilized. The wattmeter and PowerTutor will measure the power consumption of eight image processing methods running on modified Android library with self implemented algorithm called the CamTest. According to t-test analysis that has been conducted, the p values of all of the image processing methods show that there are no significant differences between the wattmeter and the PowerTutor application (p>0.01). Even though measurement based method is more accurate than estimation-based method in term of measuring power consumption, PowerTutor application proved it provides accurate, real-time power consumption estimation for Android platform smartphones.
Application developers still can use PowerTutor as an option to determine the impact of software design on power consumption
Detecting anomalous energy consumption in android applications
The use of powerful mobile devices, like smartphones, tablets and laptops, are changing the way programmers develop software. While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. This paper presents a technique and a tool to detect anomalous energy
consumption in Android applications, and to relate it directly with the source code of the application. We propose a dynamically calibrated model for energy consumption for the Android ecosystem, and that supports different devices. The model is then used as an API to monitor the application execution: first, we instrument the application source code so that we can relate energy consumption to the application source code; second, we use a statistical approach, based on fault-localization techniques, to localize abnormal energy consumption in the source code
Mapping System Level Behaviors with Android APIs via System Call Dependence Graphs
Due to Android's open source feature and low barriers to entry for
developers, millions of developers and third-party organizations have been
attracted into the Android ecosystem. However, over 90 percent of mobile
malware are found targeted on Android. Though Android provides multiple
security features and layers to protect user data and system resources, there
are still some over-privileged applications in Google Play Store or third-party
Android app stores at wild. In this paper, we proposed an approach to map
system level behavior and Android APIs, based on the observation that system
level behaviors cannot be avoided but sensitive Android APIs could be evaded.
To the best of our knowledge, our approach provides the first work to map
system level behavior and Android APIs through System Call Dependence Graphs.
The study also shows that our approach can effectively identify potential
permission abusing, with almost negligible performance impact.Comment: 14 pages, 6 figure
Data Science and Ebola
Data Science---Today, everybody and everything produces data. People produce
large amounts of data in social networks and in commercial transactions.
Medical, corporate, and government databases continue to grow. Sensors continue
to get cheaper and are increasingly connected, creating an Internet of Things,
and generating even more data. In every discipline, large, diverse, and rich
data sets are emerging, from astrophysics, to the life sciences, to the
behavioral sciences, to finance and commerce, to the humanities and to the
arts. In every discipline people want to organize, analyze, optimize and
understand their data to answer questions and to deepen insights. The science
that is transforming this ocean of data into a sea of knowledge is called data
science. This lecture will discuss how data science has changed the way in
which one of the most visible challenges to public health is handled, the 2014
Ebola outbreak in West Africa.Comment: Inaugural lecture Leiden Universit
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
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