Application-level power management in pervasive computing systems

Negri, Luca; Barretta, Domenico; Fornaciari, William

doi:10.1145/977091.977104

Cited by 5 publications

(4 citation statements)

References 14 publications

(8 reference statements)

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“…Many approaches [43], [44], [45] have also been proposed to model the power consumption of other components in mobile devices. Several approaches [10], [46], [47] acquired power models automatically based on the battery behavior of mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Detecting Display Energy Hotspots in Android Apps

Wan

Jin

et al. 2015

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

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Energy consumption of mobile apps has become an important consideration as the underlying devices are constrained by battery capacity. Display represents a significant portion of an app's energy consumption. However, developers lack techniques to identify the user interfaces in their apps for which energy needs to be improved. In this paper, we present a technique for detecting display energy hotspots -user interfaces of a mobile app whose energy consumption is greater than optimal. Our technique leverages display power modeling and automated display transformation techniques to detect these hotspots and prioritize them for developers. In an evaluation on a set of popular Android apps, our technique was very accurate in both predicting energy consumption and ranking the display energy hotspots. Our approach was also able to detect display energy hotspots in 398 Android market apps, showing its effectiveness and the pervasiveness of the problem. These results indicate that our approach represents a potentially useful technique for helping developers to detect energy related problems and reduce the energy consumption of their mobile apps.

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Section: Related Workmentioning

confidence: 99%

Detecting Display Energy Hotspots in Android Apps

Wan

Jin

et al. 2015

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

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“…Shye and colleagues obtained power models for all components of an Android G1 based on measurements of a logger they developed. Negri and colleagues treated applications as Finite‐state Machines (FSMs) and built power models through measurement of selected states. Several approaches acquired power models automatically based on the battery behavior of mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Detecting display energy hotspots in Android apps

Wan

Jin

et al. 2017

Software Testing Verif & Rel

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Summary The energy consumption of mobile apps has become an important consideration for developers as the underlying mobile devices are constrained by battery capacity. Display represents a significant portion of an app's energy consumption—up to 60% of an app's total energy consumption. However, developers lack techniques to identify the user interfaces in their apps for which energy needs to be improved. This paper presents a technique for detecting display energy hotspots—user interfaces of a mobile app whose energy consumption is greater than optimal. The technique leverages display power modeling and automated display transformation techniques to detect these hotspots and prioritize them for developers. The evaluation of the technique shows that it can predict display energy consumption to within 14% of the ground truth and accurately rank display energy hotspots. Furthermore, the approach found 398 display energy hotspots in a set of 962 popular Android apps, showing the pervasiveness of this problem. For these detected hotspots, the average power savings that could be realized through better user interface design was 30%. Taken together, these results indicate that the approach represents a potentially impactful technique for helping developers to detect energy related problems and reduce the energy consumption of their mobile apps.

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“…Situation based tough control actions are also needed in some cases, e.g. dropping of the video contents and delivering only audio contents in the adverse conditions [13,14,15].…”

Section: Energy Aware Wireless Network Protocolsmentioning

confidence: 99%