Power side channel is a very important category of side channels, which can be exploited to steal confidential information from a computing system by analyzing its power consumption. In this paper, we demonstrate the existence of various power side channels on popular mobile devices such as smartphones. Based on unprivileged power consumption traces, we present a list of real-world attacks that can be initiated to identify running apps, infer sensitive UIs, guess password lengths, and estimate geo-locations. These attack examples demonstrate that power consumption traces can be used as a practical side channel to gain various confidential information of mobile apps running on smartphones. Based on these power side channels, we discuss possible exploitations and present a general approach to exploit a power side channel on an Android smartphone, which demonstrates that power side channels pose imminent threats to the security and privacy of mobile users. We also discuss possible countermeasures to mitigate the threats of power side channels.
Most smartphones are power-hungry devices, while many batteries could last only less than a day in typical usage. The most power consuming components are CPU, network, screen and various sensors such as GPS and camera @cite_26 . For most Android smartphones, the screen consumes a majority of the total power. The screen power could be affected significantly due to its brightness and color of pixels @cite_18 , thus it is able to reduce the power consumption of an app by adjusting its color schemes @cite_45 .
@cite_18 @cite_26 @cite_45
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Mobile consumer-electronics devices, especially phones, are powered from batteries which are limited in size and therefore capacity. This implies that managing energy well is paramount in such devices. Good energy management requires a good understanding of where and how the energy is used. To this end we present a detailed analysis of the power consumption of a recent mobile phone, the Openmoko Neo Freerunner. We measure not only overall system power, but the exact breakdown of power consumption by the device's main hardware components. We present this power breakdown for micro-benchmarks as well as for a number of realistic usage scenarios. These results are validated by overall power measurements of two other devices: the HTC Dream and Google Nexus One. We develop a power model of the Freerunner device and analyse the energy usage and battery lifetime under a number of usage patterns. We discuss the significance of the power drawn by various components, and identify the most promising areas to focus on for further improvements of power management. We also analyse the energy impact of dynamic voltage and frequency scaling of the device's application processor. Smartphones and smartphone apps have undergone an explosive growth in the past decade. However, smartphone battery technology hasn't been able to keep pace with the rapid growth of the capacity and the functionality of smartphones and apps. As a result, battery has always been a bottleneck of a user's daily experience of smartphones. An accurate estimation of the remaining battery life could tremendously help the user to schedule their activities and use their smartphones more efficiently. Existing studies on battery life prediction have been primitive due to the lack of real-world smartphone usage data at scale. This paper presents a novel method that uses the state-of-the-art machine learning models for battery life prediction, based on comprehensive and real-time usage traces collected from smartphones. The proposed method is the first that identifies and addresses the severe data missing problem in this context, using a principled statistical metric called the concordance index. The method is evaluated using a dataset collected from 51 users for 21 months, which covers comprehensive and fine-grained smartphone usage traces including system status, sensor indicators, system events, and app status. We find that the remaining battery life of a smartphone can be accurately predicted based on how the user uses the device at the real-time, in the current session, and in history. The machine learning models successfully identify predictive features for battery life and their applicable scenarios. In 3G cellular networks, the release of radio resources is controlled by inactivity timers. However, the timeout value itself, also known as the tail time, can last up to 15 seconds due to the necessity of trading off resource utilization efficiency for low management overhead and good stability, thus wasting considerable amount of radio resources and battery energy at user handsets. In this paper, we propose Tail Optimization Protocol (TOP), which enables cooperation between the phone and the radio access network to eliminate the tail whenever possible. Intuitively, applications can often accurately predict a long idle time. Therefore the phone can notify the cellular network on such an imminent tail, allowing the latter to immediately release radio resources. To realize TOP, we utilize a recent proposal of 3GPP specification called fast dormancy, a mechanism for a handset to notify the cellular network for immediate radio resource release. TOP thus requires no change to the cellular infrastructure and only minimal changes to smartphone applications. Our experimental results based on real traces show that with a reasonable prediction accuracy, TOP saves the overall radio energy (up to 17 ) and radio resources (up to 14 ) by reducing tail times by up to 60 . For applications such as multimedia streaming, TOP can achieve even more significant savings of radio energy (up to 60 ) and radio resources (up to 50 ). This paper presents a large, 4-week study of more than 4000 people to assess their smartphone charging habits to identify timeslots suitable for opportunistic data uploading and power intensive operations on such devices, as well as opportunities to provide interventions to support better charging behavior. The paper provides an overview of our study and how it was conducted using an online appstore as a software deployment mechanism, and what battery information was collected. We then describe how people charge their smartphones, the implications on battery life and energy usage, and discuss how to improve users' experience with battery life.