• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.1-17
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• 2017

Mobile Cloud Computing has become a promising computing platform. It moves users' data to the centralized large data centers for users' mobile devices to conveniently access. Since the data storage service may not be fully trusted, many public verification algorithms are proposed to check the data integrity. However, these algorithms hardly consider the huge computational burden for the verifiers with resource-constrained mobile devices to execute the verification tasks. We propose an energy-efficient algorithm for assigning verification tasks (EEAVT) to optimize the energy consumption and assign the verification tasks by elastic and customizable ways. The algorithm prioritizes verification tasks according to the expected finish time of the verification, and assigns the number of checked blocks referring to devices' residual energy and available operation time. Theoretical analysis and experiment evaluation show that our algorithm not only shortens the verification finish time, but also decreases energy consumption, thus improving the efficiency and reliability of the verification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.429-430
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• 2018

The introduction of mobile devices increased the need to apply limitations such as mobile devices' memory, speed, energy, and bandwidth on the designs of searching images. There is a demand to reduce such limitations on searching images on the mobile phone. Hence, this paper proposes a design that adds tags on pictures to manage the images in mobile environment, allowing efficient searches and deletion of duplicate files based on the similarities of the images. The proposed method does not compromise its efficiency by increasing costs; it also reduces the volume of data needed for mobile devices.

• Smart Media Journal
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• v.12 no.3
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• pp.68-76
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• 2023

Emerging mobile edge computing (MEC) can be used in battery-constrained Internet of things (IoT). The execution latency of IoT applications can be improved by offloading computation-intensive tasks to an MEC server. Recently, the popularity of unmanned aerial vehicles (UAVs) has increased rapidly, and UAV-based MEC systems are receiving considerable attention. In this paper, we propose a dynamic computation offloading paradigm for UAV-based MEC systems, in which a UAV flies over an urban environment and provides edge services to IoT devices on the ground. Since most IoT devices are energy-constrained, we formulate our problem as a Markov decision process considering the energy level of the battery of each IoT device. We also use model-free Q-learning for time-critical tasks to maximize the system utility. According to our performance study, the proposed scheme can achieve desirable convergence properties and make intelligent offloading decisions.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.121-129
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• 2011

With advances in wireless networks and advent of powerful mobile devices such as smart phones, the demand for mobile IPTV services has been increasing. It is essential to minimize the energy consumption of mobile devices because their battery capacity is limited. In this paper, we therefore propose a new streaming structure in P2P-based mobile IPTV systems to minimize the energy consumption of mobile peers using agent peers. Agent peers can decrease the energy consumption of mobile peers significantly by performing streaming functionality and exchanging control messages for joining and leaving overlay networks in place of corresponding mobile peers. Finally, by simulation experiments using an energy model, we show that our proposed streaming structure can increase the lifetime of mobile peers using agent peers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5614-5633
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• 2018

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.427-440
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• 2003

Many mobile applications, such as games, security software and mathematical applications, use a random number generator(RNG). Since mobile devices operate under a limited battery capacity, the low energy consumption is one of key system requirements. For mobile applications based on an RNG, it is important to use low-power RNGs. In this article, we evaluate the energy efficiency of several well-known RNG algorithms and suggest guidelines for selecting RNGs suitable for mobile application.

• Journal of Software Engineering Society
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• v.26 no.3
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• pp.53-62
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• 2013

Along with increasing supplies of smart devices, a proliferation of context-aware applications is came. However, acquiring contexts through sensors requires considerable energy consumption. It has became big constraints on running many context-aware applications in mobile devices having limited battery capacity. Hence, energy-efficient methods for monitoring contexts are highly required. In this paper, we propose four context monitoring methods, analyse energy consumption in each method, and provide guidelines for applying the methods. It is effective to decrease energy consumption for monitoring contexts with applying the methods. To assess the proposed methods, we implement an application that is aware of a user's motion and show quantitative comparison between each of the methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1106-1112
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• 2017

As the Internet of Things (IOT) is evolving into an industry-wide service and expanded to the concept of Internet of Everything (IoE), services using IoT devices are easily accessible in everyday life. IoT requires more devices to collect information and is expected to increase the number of devices by 50 billion by 2020, and is about the number of devices currently available. Gradually, the number of mobile devices, smart devices, and Internet devices is increasing, and energy resources are required to operate such a large number of Internet devices, and the energy consumed by each device is small. In this paper, we consider the number of devices to be increased and generate a signal irrespective of transmission information so that power other than the energy required for signal transmission is consumed. When transmission information is generated and near to a receiver to receive information, The method to be used as an analysis is designed through experiments.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1219-1224
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• 2018

Nowaday, smart mobile devices on Internet of Things are required to process and deliver greate amount of data in real-time. Therefore, heterogeneous mult-core architecture such the big.LITTLE core architecture, which shows high energy conservation while guaranteeing high performance, are widely employed on up to date smart mobile devices. The LITTLE cores should be highly utilized to gain higher energy conservation because LITTLE cores have much higher energy efficiency than big cores. In this paper, we propose a core selection algorithm, which tries to firstly assign a real-time task on a LITTLE core rather a big core while the task can be finished within its own deadline. We also perform simulation as performance evaluation to show that our proposed algorithm shows higher energy conservation while guaranteeing the required performance.