• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.536-545
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• 2020

When general statistical regression methods are applied to predict the battery remaining time of a mobile smart device, they yielded the poor accuracy of estimating battery remaining time as the deviations of battery usage time per battery level became larger. In order to improve the estimation accuracy of general statistical regression methods, a preprocessing task is required to refine the measured raw data with large deviations of battery usage time per battery level. In this paper, we propose a data preprocessing framework that preprocesses raw measured battery consumption data and converts them into refined battery consumption data. The numerical results obtained by experimenting the proposed data preprocessing framework confirmed that it yielded good performance in terms of accuracy of estimating battery remaining time under general statistical regression methods for given refined battery consumption data.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.6
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• pp.33-38
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• 2011

In recent years, mobile devices and high-hearth because of the multi-functional, battery usage is increasing. But compared to the required computing power increases the battery's energy capacity of the research is going slowly. In this paper we use the battery discharge characteristics, can be used in battery research and to increase the effective capacity, wireless transmission of power from the system just by turning off the technology to extend battery life is explained. Experimental transmission of images through the standard battery drain intervals according to measuring battery life, and applications used in these experiments and heuristic to optimize battery run time was achieved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1078-1090
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• 2022

The distribution patterns of battery usage time data per battery level are able to affect the performance of estimating battery remaining time in mobile devices. Outliers may mainly affect the estimation performance of statistical regression methods. In this paper, we propose a software framework that detects and processes outliers to improve the estimation performance of statistical regression methods. The proposed framework first detects outliers that degrade the estimation performance. The proposed framework replaces outliers with smoothed data. The difference between an outlier and its replaced data will be properly distributed into individual data. Finally, individual data are reinforced to improve the estimation performance. The numerical results obtained by experimenting the proposed framework confirmed that it yielded good performance of estimating battery remaining time.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.23-32
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• 2013

The purpose of this study is to make effective mobile grid considered general environment, which can be summarized as irregular mobility, service exploration, data sharing, variety of machines, limit to the battery duration, etc. The data was extracted from the Dartmouth College. We analysed mobile use pattern of a specific group and applied pattern using hybrid method. As a result, we could adjust infra usage effectively and appropriately and cost cutting and increase satisfaction of user. In this study, by applying weighting method based on access time interval, we analysed use pattern added time variation with association rule during users in mobile grid environment. We proposed more stable way to manage patterns in a mobile grid environment that is being used as a hybrid form to process the data value received from the server in real time. Further studies are needed to get appropriate use pattern by group using use patterns of various groups.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1201-1211
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• 2015

WSN acts as an effective tool for tracking the large scale environments. In such environment, the battery life of the sensor networks is limited due to collection of the data, usage of sensing, computation and communication. To resolve this, a mobile robot is presented to identify the data present in the partitioned sensor networks and passed onto the sink. In novel data collection algorithm, the performance of the data collecting operation is reduced because mobile robot can be used only within the limited range. To enhance the data collection in a changing environment, Non Linear Error Identifier (NLEI) algorithm has been developed and presented in this paper to configure the robot by means of error models which are non-linear. Experimental evaluation has been conducted to estimate the performance of the proposed NLEI and it has been observed that the proposed NLEI algorithm increases the error correction rate upto 42% and efficiency upto 60%.

• ETRI Journal
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• v.37 no.2
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• pp.348-358
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• 2015

Mobile devices have been widespread and become very popular with connectivity to the Internet, and a lot of desktop PC applications are now aggressively ported to them. Unfortunately, mobile devices are often vulnerable to malicious attacks due to their common usage and connectivity to the Internet. Therefore, the demands on the development of mobile security systems increase in accordance with advances in mobile computing. However, it is very hard to run a security program on a mobile device all of the time due the device's limited computational power and battery life. To overcome these problems, we propose a novel mobile security scheme that migrates heavy computations on mobile devices to cloud servers. An efficient data transmission scheme for reducing data traffic between devices and servers over networks is introduced. We have evaluated the proposed scheme with a mobile device in a cloud environment, whereby it achieved a maximum speedup of 13.4 compared to a traditional algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.127-135
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• 2015

Context-aware mobile cloud computing is a new promising paradigm that allows to improve user experience by analyzing contextual information such as user location, time of the day, neighboring devices and current activity. In this paper we provide performance study of context-aware mobile cloud computing system with Volare middleware. Volare monitors the resources and context of the device. and dynamically adapts cloud service requests accordingly, at discovery time or at runtime. This approach allows for more resource-efficient and reliable cloud service discovery, as well as significant cost savings at runtime. We also have studied the performance of context-aware mobile cloud computing for different quality of service (QoS) adaptation policies. Our simulations results show that when battery level is low and CPU usage is high and user cannot maintain the initial QoS, service cost is decreased according to current adaptation policy. In conclusion, the current adaptation policy suggested in this paper may improve user experience by providing a dynamically adapted service cost according to a situation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12A
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• pp.959-965
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• 2011

As the usage of mobile stations increases thanks to various services, power saving mechanisms of mobile station operated by battery power have been gained much attention. This paper presents a power saving mechanism(PSM) of IEEE 802.16m, called Mobile WiMAX, for 4G mobile technology. This paper first presents different points of the PSM of IEEE 802.16m from IEEE 802.16e, which is a basis of IEEE 802.16m. This paper proposes a dynamic sleep cycle adjustment algorithm for improving the performance of IEEE 802.16m PSM by considering the arrival pattern of incoming packets from base station. Performance of the algorithm is analyzed in terms of energy consumption as well as waiting time of packets.