Battery Management for Mobile Sensing Applications

A New Perspective of Battery Management for Mobile Sensing Applications

Undoubtedly, the energy is a very scarce resource in smartphones and embedded systems. While the energy issues have already been studied extensively in the research domain, they mostly focus on the system-driven battery management and energy optimization. However, the energy problem should not be understood solely with an energy-efficiency point of view. It should be actively managed by the stakeholders of mobile sensing applications, not only applications and systems, but also end users, application developers, and application market. Following this inspiration, I newly proposed a novel, comprehensive approach to address battery concerns of mobile sensing applications.

PowerForecaster: Enabling application markets to provide personalized power-impact of mobile sensing applications prior to installation

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Mobile application markets are important for users to select desirable applications to users’ needs. However, they still miss a key information, power consumption by an application. What if an application market provided a sensing application’s estimated power use? Users are relieved of exhaustive trial and errors to install an application.

To realize such a function, we developed PowerForecaster, a system to provide an instant, personalized power estimation of mobile sensing applications at pre-installation time. It adopts a novel power emulator that emulates the power use of a mobile sensing application while reproducing users’ physical activities and phone use patterns, achieving accurate, personalized power estimation.

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Beyond providing a single power value, PowerForecaster can also provide diverse power information with respect to user context, such as power hotspots of a mobile sensing application.

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PADA: a developer support tool for power-aware development of mobile sensing applications

In the development process, it is inevitable for developers to optimize power use of mobile sensing applications through iterations of power evaluation: measuring power, identifying power-intensive code blocks, and changing logic or tuning relevant parameters. We present PADA, a novel tool to assist developers with the power-aware development of mobile sensing applications. Its key idea is to equip developers with power emulation environments upon which they can instantaneously replay the codes.

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Sandra: Updating the existing battery models of end users and help them better manage the battery of mobile sensing applications

Mobile users have their own practices for battery management of smartphones, e.g., turning off Wi-Fi or dimming the screen. However, current practices are no longer effective for mobile sensing applications. This is mainly because the applications continuously consume in the background and more important, their energy use highly depends on user contexts. We address the new battery-impacting factors of mobile sensing applications which are prominent enough to outdate users’ existing battery model in real life. To this end, we develop Sandra, a novel mobility-aware smartphone battery information advisor.

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Current Practices for Battery Use and Management of Smartwatches

As an emerging wearable device, a number of commercial smartwatches have been released and widely used. While many people have concerns about the battery life of a smartwatch, there is no systematic study for the main usage of a smartwatch, its battery life, or battery discharging and recharging patterns of real smartwatch users. Accordingly, we know little about the current practices for battery use and management of smartwatches. To address this, we conduct an online survey to examine usage behaviors of 59 smartwatch users and an in-depth analysis on the battery usage data from 17 Android Wear smartwatch users. We investigate the unique characteristics of smartwatches’ battery usage, users’ satisfaction and concerns, and recharging patterns through an online survey and data analysis on battery usage.