Estimation of Power Consumption of Each Application Considering Software Dependency in Android

Kurihara, Shun; Fukuda, Shinji; Kamiyama, Takeshi; Fukuda, Akira; Oguchi, Masato; Yamaguchi, Saneyasu

doi:10.2197/ipsjjip.27.221

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…In contrast, E-GreenDroid simulates the paused and killed states. Kurihara et al [31] monitored the usage of GPS and wake-lock usage to forecast energy consumption. They built their own benchmark apps to measure energy consumption.…”

Section: B Dynamic Analysismentioning

confidence: 99%

Detecting Wake Lock Leaks in Android Apps Using Machine Learning

et al. 2021

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The popularity of Android devices has increased exponentially with an increase in the number of mobile devices. Millions of online apps are used in these devices. Energy consumption of a device is a major concern for end-users, who want a long usage time on a single battery charge. The energy consumed by the app must be optimized by developers, and the available APIs must be used carefully. A wake-lock is used in apps to control the power state of the Android device and often leads to energy leakage. In this study, we detected wake-lock leaks in Android apps using machine learning. We pre-processed apps by extracting wake-lock related APIs to obtain the structural information of wake-lock usage and oversampled the data using the synthetic minority oversampling technique (SMOTE) to balance the dataset. The machine learning algorithms used to detect wake-lock leaks were first optimized using grid search to determine the best parameters. These parameters were then used in training to detect wake-lock leaks in these apps. We employed various machine learning algorithms and divided them into simple and ensemble algorithms to evaluate their efficacy. The support vector machine (SVM) and stochastic gradient boosting (SGB) were the most effective, producing 97 % and 98 % accuracy, respectively.

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Section: B Dynamic Analysismentioning

confidence: 99%

Detecting Wake Lock Leaks in Android Apps Using Machine Learning

et al. 2021

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“…It is an extension of E-GreenDroid [24] and does not address unnecessary wake-up patterns. Kurihara et al [61] monitored the start and end of wake lock and GPS usage to estimate power consumption. They created their own benchmark apps to measure power consumption; however, no experiments were conducted using this tool while handling apps from the app store.…”

Section: Dynamic Analysismentioning

confidence: 99%

Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron

Khan

Lee

et al. 2021

Electronics

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With the proliferation of mobile devices, the popularity of Android applications (apps) has increased exponentially. Efficient power consumption in a device is essential from the perspective of the user because users want their devices to work all day. Developers must properly utilize the application programming interfaces (APIs) provided by Android software development kit to optimize the power consumption of their app. Occasionally, developers fail to relinquish the resources required by their app, resulting in a resource leak. Wake lock APIs are used in apps to manage the power state of the Android smartphone, and they frequently consume more power than necessary if not used appropriately (also called energy leak). In this study, we use a multi-layer perceptron (MLP) to detect wake lock leaks in Android apps because the MLP can solve complex problems and determine similarities in graphs. To detect wake lock leaks, we extract the call graph as features from the APK and embed the instruction and neighbor information in the node’s label of the call graph. Then, the encoded data are input to an MLP model for training and testing. We demonstrate that our model can identify wake lock leaks in apps with 99% accuracy.

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“…Kurihara et al [289] tried to monitor software dependencies on energy consumption of smartphones. The method can estimate GPS and wake-lock time more accurately than that of the standard system of the Android OS.…”

Section: ) Monitoring Software Dependency On Power Consumptionmentioning

confidence: 99%

Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

et al. 2019

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The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed.

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Estimation of Power Consumption of Each Application Considering Software Dependency in Android

Cited by 5 publications

References 21 publications

Detecting Wake Lock Leaks in Android Apps Using Machine Learning

Detecting Wake Lock Leaks in Android Apps Using Machine Learning

Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron

Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

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