Managing the power consumption of display-related components in mobile devices is difficult because of performance degradation. Therefore, eliminating hidden workloads, such as redundant frames, is preferable, as it directly reduces power without affecting the user experience. Our preliminary study shows that the default launcher of the Android Open Source Project (AOSP) and popular applications, such as Instagram and Pinterest, generate redundant frames. In this paper, we propose a scheme to optimize the power consumption of the smartphone's display-related components by preventing redundant frames generation. By analyzing the frame-generation process, we observe that redundant frame generation is possible in the current Android framework. We then propose a scheme that recognizes and prevents redundant frame generation before actual frame generation (i.e., frame rendering in the GPU). The proposed scheme utilizes a display list, which was introduced in recent Android smartphones for efficient frame generation. We implemented the proposed scheme on Nexus smartphones. On the Nexus 5, the proposed solution reduced the energy of the AOSP default launcher, Instagram, and Pinterest by 40, 35.4, and 39.6 percent, respectively. Furthermore, the experimental results with a general usage scenario showed that our scheme prevented about 35 percent of redundant frame generation with a false-positive rate of 1.8 percent.

Advertisement-embedded mobile applications have been reported to consume a non-trivial amount of energy. Although a few studies have focused on the energy consumption of mobile advertisements (ads), no previous work has addressed the mobile ad ecosystem, which consists of users, application developers, and ad providers. In this paper, we define the advertisement energy information (AEI) required for the mobile ad ecosystem, and we propose a set of application programming interfaces (APIs) to provide AEI that considers various requirements of the underlying ecosystem. To realize the APIs, we developed a system service in Android to collect the AEI accurately and with low overhead. The experiment results show the validity of the proposed scheme, and the case studies demonstrate the usefulness of the proposed APIs.

Mobile users are always demanding extended availability in their battery use. Together with enlarged battery capacity, fast charging is one approach that provides an improved user experience in battery use. Recently, device manufacturers have been developing a variety of fast charging techniques for mobile devices. However, the existing techniques severely reduce the charging power when the device is in use while charging. We experimentally demonstrate that the primary cause of the reduction in the charge speed during device use is to cope with the performance degradation incurred by heat generation. We then propose an adaptive charging scheme, called Always-on Quick Charging, which enables fast charging especially when the device is in use. The key idea of our approach is to adjust the charging power while ensuring that the heat generated by the charging does not affect the performance. The proposed scheme is implemented in Google's Pixel 2XL smartphone. The experiment with a real-world usage scenario shows that the charging speed of the proposed scheme is up to 2.4 times faster than the default scheme, while preserving device performance.

Predicting a click sequence inmobile applications improves the user experience in various ways. By predicting which button will be clicked next, one can predict how the application will work and how the device will operate. However, predicting the click sequence is difficult because of the problems involved in collecting click sequences in real application usage. More importantly, accurate predictions are extremely challenging. In this paper, we address these issues. We propose PathFinder, a scheme for collecting click events and based on them predicting the next click in the application. The clicks are collected with the Android Accessibility Service and the next click is predicted via long short-term memory (LSTM). For the prediction, the base click sequence model is first generated from all users' data; then, a personalized model is trained with an individual click sequence. As training data considerably influences the performance of LSTM, several techniques are developed to enhance the quality of the training data. The experimental results for 100 popular applications showed that the coverage and accuracy of click sequence tracing were 95% and 96%, respectively. Furthermore, PathFinder predicted the top three buttons that would be clicked next with a 0.76 F-measure for 1 775 043 real click data.

Smartphones' surface temperature, also called skin temperature, can rapidly heat up in certain cases, and this causes a variety of safety problems. Therefore, the thermal management of smartphones should consider the skin temperature, and its accurate prediction is important. However, due to the complicated relationship among the many exothermic components in the device, predicting skin temperature is extremely difficult. In this paper, we develop a thermal prediction model that accurately predicts the skin temperature of a mobile device. In an experiment with smartphones, we show that the proposed model achieves an accuracy of 98%, with a ±0.4 °C margin of error. To the best of our knowledge, our work is the first to reveal the complex relationship between the various components inside of a smartphone and its skin temperature.

The thermal issue for mobile devices becomes critical as the devices' performance increases to handle complicated applications. Conventional thermal management limits the performance of the entire device, degrading the quality of both foreground and background applications. This is not desirable because the quality of the foreground application, i.e., the frames per second (FPS), is directly affected, whereas users are generally not aware of the performance of background applications. In this paper, we propose an app-oriented thermal management scheme that specifically restricts background applications to preserve the FPS of foreground applications. For efficient thermal management, we developed a model that predicts the heat contribution of individual applications based on hardware utilization. The proposed system gradually limits system resources for each background application according to its heat contribution. The scheme was implemented on a Galaxy S8+ smartphone, and its usefulness was validated with a thorough evaluation.

Although Wi-Fi fingerprinting is a promising solution for indoor localization, its widespread use is limited due to the necessity of time-consuming site surveys. Recently, active research has been conducted to reduce site-survey costs with participatory sensing. While previous work focused on the expansion of radio map coverage, in this paper, we deal with the issues on the scalability and consistency of radio map. In participatory sensing, radio map construction should be able to handle massive data collected from many people over a long period with limited storage capacity. The radio map should also guarantee consistency, which means consistent accuracy regardless of the RSS variances caused by environmental dynamics. This paper proposes a scalable and consistent radio map management scheme. Using multiple fingerprints per location, we minimize accuracy degradation caused by the RSS variance problem. To overcome the scalability issue, we control the number of fingerprints by a two-phase fingerprints selection algorithm. For each location, the proposed scheme first clusters the collected fingerprints and removes all fingerprints except for the centroids. Then, an optimal set of fingerprints is found by comparing the fingerprints in neighboring locations. We validate the efficiency of the proposed scheme with real experiments in various environments.

The power modeling of mobile application processors (APs) is a challenging task due to their complexity. The existing power models and their associated devices have mostly been made obsolete by recent hardware developments. In this paper, we propose an enhanced power model used in modern mobile devices. The model accurately estimates the power consumption of AP component and utilizes the runtime usage information of each hardware component. We evaluated the model accuracy using various benchmarks, as well as popular smartphone applications with multiple devices that employ different APs. The evaluation shows that our model achieves the mean absolute percentage error (MAPE) of 5.1%.

Extending battery lifetime is an important issue for mobile devices. While extensive attempts have been made at the software level, optimization often risks hampering user experience. One fundamental method to increase battery lifetime is to improve the efficiency of the battery itself. We argue that the multi-cell battery system, which is widely used for enhancing battery efficiency in the electric vehicle (EV) field, can solve this issue. However, due to the hardware constraints and device usage characteristics, battery advancements in the EV field are not directly applicable to mobile devices. In this paper, we propose BattMan, a multi-cell battery management system for mobile devices, for the enhancement of battery efficiency. We develop an accurate battery cell model to estimate the expected battery lifetime considering the recovery effect, the rate capacity effect, and battery aging. We also propose a multi-cell scheduling algorithm to maximize the overall battery lifetime. We implemented BattMan on recent smartphones and evaluated its impact on battery lifetime. The experimental results show that a two-cell configuration of the proposed system increases battery lifetime by an average of between 14-19%, depending on cell aging, in real usage scenarios over a single-cell battery of the same overall capacity. We hope the proposed multi-cell battery scheme opens up a new direction towards battery lifetime improvement in mobile devices.

For the tuning and optimization of mobile applications, user Quality of Experience (QoE) should be closely considered as a key development metric. While previous research has approached application tuning from different perspectives, there has rarely been any sophisticated analysis of QoE at the user interface level. In this paper, we propose QX-probe, a comparative and quantitative QoE analysis tool for application tuning. We define latency, energy, and UI (User Interface) usage information as critical factors for QoE analysis. QX-probe measures these factors at the UI level and provides a range of information for QoE analysis by means of a web-based tool. The usefulness of QX-probe is validated through a number of case studies using real application. Developers should be able to use this tool to identify application tuning points.

Monitoring various hardware and software events for energy consumption is essential for energy management in mobile devices. However, current mobile operating systems (OS) lack monitoring functionality and do not provide sufficient information of this kind. In this paper, we propose PEMOS (Power Events Monitor for Mobile Operating Systems), a framework for power event APIs for mobile devices, that provides a wide spectrum of energy-related information, enabling in-depth analysis of energy problems. PEMOS provides a set of well-defined APIs as a mobile OS facility, defining various energy-related system events as power events. These are classified into system events and application events, encompassing extensive and fine-grained power-related events. Benefits of PEMOS include extensive coverage of power events, high portability across various platforms, and efficient API implementation. The framework structure is portable across multiple devices, and the standard ioctl-based API implementation enables the same operations on different devices without system modification. We implemented PEMOS on the Android platform to evaluate its efficacy and usefulness. The experimental results and case studies confirm that PEMOS is effective and useful for a range of energy management systems, with minimal overhead.

Current mobile devices use a touch boosting scheme to handle operations caused by user interactions with the touchscreen. The current scheme uses a predetermined DVFS step for touch boosting, regardless of user texting speed or related workloads, causing power waste due to unnecessarily high CPU frequency. In particular, the current mechanism is not optimized for power usage when the soft keyboard is used as an input mechanism. In this paper, we propose a scheme called Tbooster, which adaptively adjusts touch boosting level. The scheme reflects texting interval and texting latency, minimizing power consumption while maintaining the user's quality of experience. The scheme was implemented in Android devices and evaluated using a variety of texting applications. Our evaluation results show that the proposed technique reduces the device's overall power consumption by 4.6--13.1%, depending on texting interval and application type.

Mobile data offloading through WiFi is an essential requirement to reduce cellular network traffic. While extensive attempts have been made at mobile data offloading, previous studies have rarely addressed practical issues, such as dealing with diverse user contexts. In this paper, we propose a personalized data offloading scheme to provide maximum throughput within the cellular budget in daily life. We propose an adaptive policy that considers a user's mobility patterns, cellular budget, and network usage for applications. The proposed system employs an adaptive model to predict the throughput of WiFi APs and the network usage of smartphones. Among the three types of predictor model (i.e., spatial, temporal, and spatio-temporal), the system automatically chooses the optimal model for each mobile user without user intervention. The experimental results from 10 mobile users show that the proposed system provides 29% higher throughput than previous systems and minimizes extra data charges.

Energy accounting is an essential requirement for optimizing energy consumption on mobile devices. State-of-the-art approaches consider application processes and threads as the sole components of energy consumption. In this framework, the energy consumption of system services is unclear and has not been comprehensively studied. In this paper, we suggest that the energy consumption of system services should be investigated to understand the behavior of applications. We propose a fine-grained energy tracing scheme, EnTrack, to enhance the accuracy of energy tracing by identifying and incorporating the energy portions consumed by system services. We implemented EnTrack on the Android platform and validated its functionality and usefulness. In addition, practical usage cases of EnTrack, which uses it as an energy behavior analysis tool, were introduced. The case studies demonstrated that EnTrack enables an understanding of fine-grained energy consumption, especially in system services, which have previously been concealed.

Quality improvement in mobile applications should be based on the consideration of several factors, such as users' diversity in spatio-temporal usage, as well as the device's resource usage, including battery life. Although application tuning should consider this practical issue, it is difficult to ensure the success of this process during the development stage due to the lack of information about application usage. This paper proposes a user interaction-based profiling system to overcome the limitations of development-level application debugging. In our system, the analysis of both device behavior and energy consumption is possible with fine-grained process-level application monitoring. By providing fine-grained information, including user interaction, system behavior, and power consumption, our system provides meaningful analysis for application tuning. The proposed method does not require the source code of the application and uses a web-based framework so that users can easily provide their usage data. Our case study with a few popular applications demonstrates that the proposed system is practical and useful for application tuning.

Pearson's correlation coefficient and vector similarity are generally applied to The users' similarity weight of user based recommender system. This study is needed to find that the correlation coefficient of similarity weight is effected by the number of pair response and significance probability. From the classified correlation coefficient by the significance probability test on the correlation coefficient and pair of response, the change of MAE is studied by comparing the predicted precision of the two. The results are experimentally related with the change of MAE from the significant correlation coefficient and the number of pair response.

In this study the author had attempted to establish a correlation among alcohol use and MMPI and lunar cycle. MMPI was carried on 2, April 1992(the last day of the second lunar month) and 15, June 1992(the fifteenth day of the fifth lunar month). These experiments took male and female students who were in WON KWANG University as subjects. The results of it suggested the followings. 1) Drinkers and normal subjects average of T-score in each scales were in a normal scores. 2) In every scales except L,K,Si scales drinkers showed higher scores than normal subjects. There was a significant difference on L, F, Pd, Pa, Sc, Ma scales. 3) On the fifteenth day of the month, drinkers showed Higher T-Scores than normal subjects in every scales excepts L,K,Si scales. There was a significant difference in Ma scale. 4) On the last day of the month, drinkers showed higher T-scores than normal subjects in every scales except L,K,Mf,Si scales. There was a significant difference on L, F, Pd, Sc, Ma scales. 5) Drinkers showed higher than normal subjects in symptom of neurosis and psychosis. As the results of above, human character changes to some extend according to alcohol use. There is a tendency that repeated drinking of college students prone to change into habitual drinking or alcoholic impairment. So controlled drinking among the college students is highly recommended.