Mirroring Mobile Phone in the Clouds

This paper presents a framework of Mirroring Mobile Phone in the Clouds (MMPC) to speed up data/computing intensive applications on a mobile phone by taking full advantage of the super computing power of the clouds. An application on the mobile phone is dynamically partitioned in such a way that the heavy-weighted part is always running on a mirrored server in the clouds while the light-weighted part remains on the mobile phone. A performance improvement (an energy consumption reduction of 70% and a speed-up of 15x) is achieved at the cost of the communication overhead between the mobile phone and the clouds (to transfer the application codes and intermediate results) of a desired application. Our original contributions include a dynamic profiler and a dynamic partitioning algorithm compared with traditional approaches of either statically partitioning a mobile application or modifying a mobile application to support the required partitioning

Profiling Power Consumption on Mobile Devices

The proliferation of mobile devices, and the migration of the information access paradigm to mobile platforms, motivate studies of power consumption behaviors with the purpose of increasing the device battery life. The aim of this work is to profile the power consumption of a Samsung Galaxy I7500 and a Samsung Nexus S, in order to understand how such feature has evolved over the years. We performed two experiments: the first one measures consumption for a set of usage scenarios, which represent common daily user activities, while the second one analyzes a context-aware application with a known source code. The first experiment shows that the most recent device in terms of OS and hardware components shows significantly lower consumption than the least recent one. The second experiment shows that the impact of different configurations of the same application causes a different power consumption behavior on both smartphones. Our results show that hardware improvements and energy-aware software applications greatly impact the energy efficiency of mobile device

Monitoring Energy Consumption of Smartphones

With the rapid development of new and innovative applications for mobile devices like smartphones, advances in battery technology have not kept pace with rapidly growing energy demands. Thus energy consumption has become a more and more important issue of mobile devices. To meet the requirements of saving energy, it is critical to monitor and analyze the energy consumption of applications on smartphones. For this purpose, we develop a smart energy monitoring system called SEMO for smartphones using Android operating system. It can profile mobile applications with battery usage information, which is vital for both developers and users.Comment: The 1st International Workshop on Sensing, Networking, and Computing with Smartphones (PhoneCom), IEEE, Dalian, China, Oct 19-22, 201

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

OpenCV Based Real-Time Video Processing Using Android Smartphone

as the smarphone industry grows rapidly, the smartphone application needs to be faster and consumes lower power because the smartphone is only powered by a battery. In this paper, two Android applications based on video processing method are introduced; one by using OpenCV library, the other one is using Android library with self-implemented algorithm called CamTest. Eight image processing methods are applied to each frame of the video captured from the Android smartphone. The smartphone used in this study is the Samsung Galaxy S, with Android 2.3 Gingerbread Operating System. The efficiencies and power consumptions of the two applications are compared by observing their frame processing rate and power consumption. The experimental results show that out of the eight image processing methods, six methods that executed using OpenCV library are faster than that of CamTest with a total average ratio of 0.41. For the power consumption per frame test, six methods that executed using OpenCV library consume less power than that of CamTest with a total average ratio of 0.39