• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.57-69
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• 2020

In general, many IoT devices, including smart phones, use LTE, Wi-Fi, and Bluetooth, and these communication modules generate a lot of energy consumption during periodic data transmission. This paper proposes a method of the data transmission mode change for improving energy efficiency in various communication environments that mobile devices may encounter. We propose an algorithm for setting the mode considering energy efficiency, data transmission performance and cost when the mobile device transmits data, and transmitting the data in an optimized manner according to the state of the mobile device. The proposed algorithm is implemented through experiments on energy efficiency for each communication module, and the scenario is used to verify how efficiently the proposed algorithm uses energy.

• Journal of the Semiconductor & Display Technology
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• v.19 no.1
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• pp.23-28
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• 2020

In Smart mobile devices embedding big.LITTLE architectures, the conventional multi-core assignment scheme for user applications may incur wasteful energy consumption and long response time. In this paper, we propose a user experience assisted energy-efficient multicore assignment scheme. Our simulation results show that the proposed scheme achieves at 40% less energy consumption and at 20% less response time comparing to the legacy scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4044-4062
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• 2016

We investigate the optimization of energy consumption in Mobile Cloud environment in this paper. In order to optimize the energy consumed by the CPUs in mobile devices, we put forward using the asymptotic time complexity (ATC) method to distinguish the computational complexities of the applications when they are executed in mobile devices. We propose a multi-scale scheme to quantize the channel gain and provide an improved dynamic transmission scheduling algorithm when offloading the applications to the cloud center, which has been proved to be helpful for reducing the mobile devices energy consumption. We give the energy estimation methods in both mobile execution model and cloud execution model. The numerical results suggest that energy consumed by the mobile devices can be remarkably saved with our proposed multi-scale scheme. Moreover, the results can be used as a guideline for the mobile devices to choose whether executing the application locally or offloading it to the cloud center.

• ETRI Journal
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• v.44 no.5
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• pp.746-758
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• 2022

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6152-6168
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• 2017

Recently, the biometric-based authentication systems such as FIDO (Fast Identity Online) are increased in mobile computing environments. The biometric-based authentication systems are performed on the mobile devices with the battery, the improving energy efficiency is important issue. In the case, the size of images (i.e., face, fingerprint, iris, and etc.) affects both recognition accuracy and energy consumption, and hence the tradeoff analysis between the both recognition accuracy and energy consumption is necessary. In this paper, we propose an energy-efficient way to authenticate based on biometric information with tradeoff analysis between the both recognition accuracy and energy consumption in multimedia IoT (Internet of Things) transmission environments. We select the facial information among biometric information, and especially consider the multicore-based mobile devices. Based on our experimental results, we prove that the proposed approach can enhance the energy efficiency of GABOR+LBP+GRAY VALUE, GABOR+LBP, GABOR, and LBP by factors of 6.8, 3.6, 3.6, and 2.4 over the baseline, respectively, while satisfying user's face recognition accuracy.

• Journal of KIISE:Information Networking
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• v.33 no.5
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• pp.369-379
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• 2006

In recent years, there have been many researches about Ad-hoc Networks which is available to communicate freely between mobile devices by using multi-hop without any support of relay base or access point. TCP that used the most widely transport protocol in the Internet repeats packet loss and retransmission because it increases congestion window size by using reactive congestion control until packet loss occurs. As a result of this, energy of mobile device is wasted unnecessarily. In this paper, we propose TCP-New Veno in order to improve the energy efficiency of mobile device. According to the state of network, the scheme adjusts appropriate size of congestion window. Therefore, the energy efficiency of mobile device and utilization of bandwidth are improved by the scheme. From the simulation by using ns-2, we could see more improved energy efficiency with TCP-New Veno than those with TCP in Ad-hoc Networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1587-1605
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• 2015

Mobile cloud computing (MCC) combines mobile Internet and cloud computing to improve the performance of applications. In MCC, the data processing and storage for mobile devices (MDs) is provided on the remote cloud. However, MCC faces the problem of energy efficiency caused by randomly varying channels. In this paper, by introducing the Lyapunov optimization method, we propose a combined service subscription and delivery (CSSD) algorithm that can guide the users to subscribe to services reasonably. This algorithm can also determine whether to deliver the data and to whom data is sent in the current time unit based on the queue backlog and the channel state. Numerical results validate the correctness and effectiveness of our proposed CSSD algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2144-2159
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• 2015

CISCO VNI predicted an average annual growth rate of 66% for mobile video traffic between 2014 and 2019 and accordingly much academic research related to video streaming has been initiated. In video streaming, Adaptive Bitrate (ABR) is a streaming technique in which a source video is stored on a server at variable encoding rates and each streaming user requests the most appropriate video encoding rate considering their channel capacity. However, these days, ABR related studies are only focusing on real-time rate adaptation omitting energy efficiency though it is one of the most important requirement for mobile devices, which may cause dissatisfaction for streaming users. In this paper, we propose an energy efficient prefetching based dynamic adaptive streaming technique by considering the limited characteristics of the batteries used in mobile devices, in order to reduce the energy waste and provide a similar level of service in terms of the average video rate compared to the latest ABR streaming technique which does not consider the energy consumption. The simulation results is showing that our proposed scheme saves 65~68% of energy at the average global mobile download speed compared to the latest high performance ABR algorithm while providing similar rate adaptation performance.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.209-220
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• 2016

While the services of mobile devices like smart phone, tablet, and smart watch have been increased and varied, the software embedded into such devices has been also increased in size and functional complexity. Therefore, increasing operation time of mobile devices for serviceability became an important issue due to the limitation of battery power. Recent studies focus on the software development having efficient behavioral patterns because the energy consumption of mobile devices is caused by software behaviors which control the hardware operations. However, it is often difficult to develop the embedded software with considering energy-efficiency and behavior optimization due to the short development cycle of the mobile services in many cases. Therefore, this paper proposes the refactoring techniques for reducing energy consumption, and enables to fulfill the energy requirements during software development and maintenance. We defined energy bad smells with the code patterns that can excessively consume the energy, and our refactoring techniques are to remove these bad smells. We performed some case studies to verify the usefulness of our refactoring techniques.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.57-63
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• 2007

Providing the sufficient energy to the mobile device is essential to process the job in distributed wireless network. To solve the restrained conditions of energy problems of mobile devices, this paper suggests the efficient method of processing the distributed job using mobility management in wireless network. Energy consumption can be analyzed using the statistical model and required energy of processing the distributed job in mobile device can be predicted using the mobility management. Therefore, this paper suggests the reliable algorithm to process distributed job through the mobile devices with regular mobility and shows the efficiency of the suggested algorithm.