Fully automated OLED display power modeling for mobile devices

Choi, Yonghun; Ha, Rhan; Cha, Hojung

doi:10.1016/j.pmcj.2018.07.006

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…The adaptation rules specify that the application reconfigure its functionality in order to use an algorithm with a higher compression ratio (around 75% of video compression) in two cases: (1) the battery level of the mobile is lower than 25%, the device is not charging and the user shares a video; (2) the user shares a video while the device is connected to a low-band network. In addition, the user interface is adapted by providing darker colors [8] when the device battery level is lower than 25% and the device is not connected to power.…”

Section: Rq1 Benefits Of Our Adaptation Enginesmentioning

confidence: 99%

“…These configurations are pre-planned to be energy efficient for a generic user behavior and, normally, they are not prepared for changes that may occur at runtime [7] . Therefore, when the user behavior changes, the energy efficiency of the application decreases [8] . In contrast, self-adaptive applications are able to self-adapt their behavior or structure at runtime in response to user behavior [9] .…”

Section: Introductionmentioning

confidence: 99%

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Energy efficient adaptation engines for android applications

Cañete

Horcas

Ayala

et al. 2020

Information and Software Technology

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ContextThe energy consumption of mobile devices is increasing due to the improvement in their components (e.g., better processors, larger screens). Although the hardware consumes the energy, the software is responsible for managing hardware resources such as the camera software and its functionality, and therefore, affects the energy consumption. Energy consumption not only depends on the installed code, but also on the execution context (environment, devices status) and how the user interacts with the application.Objective In order to reduce the energy consumption based on user behavior, it is necessary to dynamically adapt the application. However, the adaptation mechanism also consumes a certain amount of energy in itself, which may lead to an important increase in the energy expenditure of the application in comparison with the benefits of the adaptation. Therefore, this footprint must be measured and compared with the benefit obtained.Method In this paper, we (1) determine the benefits, in terms of energy consumption, of dynamically adapting mobile applications, based on user behavior; and (2) advocate the most energy-efficient adaptation mechanism. We provide four different implementations of a proposed adaptation model and measure their energy consumption. ResultsThe proposed adaptation engines do not increase the energy consumption when compared to the benefits of the adaptation, which can reduce the energy consumption by up to 20%. ConclusionThe adaptation engines proposed in this paper can decrease the energy consumption of the mobile devices based on user behavior. The overhead introduced by the adaptation engines is negligible in comparison with the benefits obtained by the adaptation.

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Section: Rq1 Benefits Of Our Adaptation Enginesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy efficient adaptation engines for android applications

Cañete

Horcas

Ayala

et al. 2020

Information and Software Technology

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“…Among the common wireless communication protocols used in IoT devices, Bluetooth has the lowest power, at 10 mW, while Wi-Fi has the highest power, at over 500 mW [19]. Moreover, the displays of smartphones and smartwatches consume power [20,21]. Consequently, several heat dissipation methods for smartphones have been proposed [22,23].…”

Section: Introductionmentioning

confidence: 99%

Receiver coil built into belt for heat dissipation of watch-type smart devices

Kashiwazaki

Hachiya

Kanamoto

et al. 2022

IEICE Electron. Express

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This letter proposes a novel method to promote the heat dissipation of watch-type smart devices by using receiver coils for wireless charging. It is achieved by embedding a spiral coil inside the belt. The heat dissipation effects of the proposed method on temperatures at key hot spots are clarified. The power transfer efficiency is also clarified. In watch-type smart devices, the most critical part for temperature is the underside surface of the belt, since this is the part that touches the skin for a long time. The analysis results show that the proposed method can reduce the increase in ambient temperature at the belt's underside surface by 59.7% and that its power transfer efficiency is more than 90%.

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“…For example, Gómez-Bombarelli et al integrated a machine learning model with computational quantum chemistry to screen over a million candidates in the search for an efficient blue-emitting OLED (Figure a–f) . Choi et al employed supervised learning techniques to model the power consumption through the RGB value of each pixel of the OLED display in mobile devices . Janai et al took a random forest algorithm to extract the complex correlation between blue OLED device efficiency and device parameters such as triplet energy, frontier molecular orbital energy levels, and layer thickness, finding that the triplet energy of the electron transport layer is critical to OLED efficiency .…”

mentioning

confidence: 99%

“…47 Choi et al employed supervised learning techniques to model the power consumption through the RGB value of each pixel of the OLED display in mobile devices. 48 Janai et al took a random forest algorithm to extract the complex correlation between blue OLED device efficiency and device parameters such as triplet energy, frontier molecular orbital energy levels, and layer thickness, finding that the triplet energy of the electron transport layer is critical to OLED efficiency. 49 There is clearly ample opportunity for applying data-driven methods to develop OLED materials.…”

mentioning

confidence: 99%