Energy consumption in mobile devices is an important issue for both system developers and users. Users are aware of the battery-related information of their mobile devices and tend to take appropriate actions to increase the battery life. In this article, we propose a framework that accurately estimates the remaining battery time of applications at runtime. The framework profiles the power behavior of applications tied with activated hardware components and estimates the remaining battery budget utilizing the battery-related data provided by the device. The experiments validate that our method predicts the remaining battery time for applications with approximately 93% of accuracy.

Energy accounting is an essential requirement for optimizing energy consumption on mobile devices. State-of-the-art approaches consider application processes and threads as the sole components of energy consumption. In this framework, the energy consumption of system services is unclear and has not been comprehensively studied. In this paper, we suggest that the energy consumption of system services should be investigated to understand the behavior of applications. We propose a fine-grained energy tracing scheme, EnTrack, to enhance the accuracy of energy tracing by identifying and incorporating the energy portions consumed by system services. We implemented EnTrack on the Android platform and validated its functionality and usefulness. In addition, practical usage cases of EnTrack, which uses it as an energy behavior analysis tool, were introduced. The case studies demonstrated that EnTrack enables an understanding of fine-grained energy consumption, especially in system services, which have previously been concealed.

A smartphone battery interface should provide energy-related information efficiently with high accuracy because it is the basis for user actions on battery consumption. Previous studies have mainly focused on battery information per se, but the effectiveness of user interaction with the interface has barely been studied. In this paper, we first discuss the results of a survey on different types of energy-related information and features to investigate what users want in terms of battery consumption for smartphones. We then introduce Powerlet, which is a new battery interface that attempts to actively interact with users to provide battery usage information. We validated the efficiency of Powerlet with real users' experiences derived over a seven-week study period. We found that the users actively managed and changed their phone usage based on the energy-related information provided by Powerlet. With the Powerlet active battery interface, participants reduced daily energy consumption by 8.2% on average.

Modeling and estimating power consumption of OLED displays are necessary to understand the energy behavior of emerging mobile devices. Although previous study exists to model and estimate the power consumption of stationary display images, to the best of our knowledge, no prior work is found to deal with runtime power behavior of OLED display running real applications. This paper proposes a runtime power estimation scheme for OLED displays that involves monitoring kernel activities that capture the screen change events of running applications. The experiment results show that the proposed scheme estimates the display energy consumption of running applications with reasonable accuracy.

To prolong the battery lifetime of modern mobile devices, the energy management policy should be developed in a personalized way, adequately reflecting user context or the energy behavior of the user. The first step toward this personalization is to collect the relevant information, accurately and efficiently, from the device. This paper presents a fine-grained and low-overhead framework, called UserScope, which is designed to collect energy-related user contexts in Android smartphones. We classified energy-related smart phone usage and designed an appropriate set of monitoring parameters to collect from the system. The UserScope core is then implemented as a kernel module to collect all the necessary information in an event-driven manner. This kernel-level implementation ensures monitoring accuracy and low system overhead. UserScope also provides a data-sharing mechanism with which other software components in the system can easily interface. Our experiments show that User Scope accurately extracts energy related system information with 0.8% CPU overhead. The practicality of UserScope is also validated with real deployment and subsequent analysis of the collected data.

This paper presents experiment results showing the relationship between operating temperature and the lifetime of mobile devices. Conventional knowledge is that energy-efficiency is higher at lower operating temperatures. However, we show that this assumption is not always true, because the energy-efficiency of mobile device is governed not only by static leakage power but also by the chemical characteristics of the battery. We believe that future research on thermal management of mobile devices should consider the results of these observations.

This paper presents a run-time monitoring framework to detect end-to-end timing constraint violations of event flows in distributed real-time systems. The framework analyzes every event on possible event flow paths and automatically inserts timing fault checks for run-time detection. When the framework detects a timing violation, it provides users with the event flow's run-time path and the time consumption of each participating software module. In addition, it invokes a timing fault handler according to the timing fault specification, which allows our approach to aid the monitoring and management of the deployed systems. The experimental results show that the framework correctly detects timing constraint with insignificant overhead and provides related diagnostic information.