• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.174-190
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• 2010

In ubiquitous computing, invisible devices and software are connected to one another to provide convenient services to users [1][2]. Users hope to obtain a personalized service which is composed of customized devices among sharable devices in a ubiquitous smart space (which is called USS in this paper). However, the situations of each user are different and user preferences also are various. Although users request the same service in the same USS, the most suitable devices for composing the service are different for each user. For these user requirements, this paper proposes a device recommender system which infers and recommends customized devices for composing a user required service. The objective of this paper is the development of the systems for recommending devices through context-aware inference in peer-to-peer environments. For this goal, this paper considers the context and user preference. Also I implement a prototype system and test performance on the real ubiquitous mobile object (UMO).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4948-4967
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• 2017

On mobile devices, image sequences are widely used for multimedia applications such as computer vision, video enhancement, and augmented reality. However, the real-time processing of mobile devices is still a challenge because of constraints and demands for higher resolution images. Recently, heterogeneous computing methods that utilize both a central processing unit (CPU) and a graphics processing unit (GPU) have been researched to accelerate the image sequence processing. This paper deals with various optimizing techniques such as parallel processing by the CPU and GPU, distributed processing on the CPU, frame buffer object, and double buffering for parallel and/or distributed tasks. Using the optimizing techniques both individually and combined, several heterogeneous computing structures were implemented and their effectiveness were analyzed. The experimental results show that the heterogeneous computing facilitates executions up to 3.5 times faster than CPU-only processing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.836-841
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• 2008

Ubiquitous computing represents the most explicit attempt yet to move computing technology beyond the confines of tool usage towards a pervasive penetration of everyday life. In this report, as a general introduction of Ubiquitous computing, a trend of Ubiquitous computing devices is proposed for the applied technology fields and our everyday life. We outline a broad analysis of this technology based on a close examination of the researches advancing it. After introducing a framework for understanding modem device technology, we develop an interpretation of ubiquitous computing concentrating on its guiding principles, technological infrastructure, and trends.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.1-9
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• 2010

Today's growth of the mobile communication infrastructure made mobile computing systems like cellular phones came next to or surpassed the desktop PCs in popularity due to their mobility. Although the performance of mobile devices is now being improved continuously, it is a current common sense that compute intensive large-scale applications can hardly run on any kind of mobile handset devices. To clear up this problem, we decided to exploit the mobile cluster computing system and surveyed the existing ones first. We found out, however, that most of them are not the actual implementations but a mobile cluster infrastructure proposal or idea suggestions for reliable mobile clustering. To make cell phones participated in cluster computing nodes, in this paper, we propose a redesigned JPVM cluster computing engine and a set of WIPI mobile runtime functions interfacing with it. And we also show the performance evaluation results of real parallel applications running on our Mobile-JPVM cluster computing systems. We find out by the performance evaluation that large-scale applications can sufficiently run on mobile devices such as cellular phones when using our mobile cluster computing engine.

• Journal of Information Technology Services
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• v.10 no.3
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• pp.213-236
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• 2011

Mobile devices are widely accepted as a convenient machine which provides computing capability as well we cell phone capability. Because of limited resources on mobile devices, complex applications could not be deployed on the devices. Service-based mobile applications (SMAs) can provide a solution to overcome the limitation by subscribing cloud services. Since SMAs have complex structures than standalone applications, it is challenging to develop high quality SMAs, to manage both services and mobile applications, and to implement automated billing for subscribed services. Therefore, there is a great demand for a platform for super mobile computing, which supports all key activities in managing life cycle of SMAs. In this paper, we present technical aspects of a platform which is under development; Super Mobile Autonomous Reliable plaTform (SMART). We believe that it provides a number of practical features which are essential in supporting life-cycle of SMAs; development, deployment, management, and operation.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.4
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• pp.294-301
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• 2016

In this paper, we present methods to efficiently parallelize iterative 3D image reconstruction by exploiting trigeneous devices (three different types of device) at the same time: a CPU, an integrated GPU, and a discrete GPU. We first present a technique that exploits single instruction multiple data (SIMD) architectures in GPUs. Then, we propose a performance estimation model, based on which we can easily find the optimal data partitioning on trigeneous devices. We found that the performance significantly varies by up to 6.23 times, depending on how SIMD units in GPUs are accessed. Then, by using trigeneous devices and the proposed estimation models, we achieve optimal partitioning and throughput, which corresponds to a 9.4% further improvement, compared to discrete GPU-only execution.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.330-334
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• 2009

Most security solutions and middleware on home network consider internet users as approaching subject. It is unrealistic where the most subjects are mobile users who want to control home network devices. Therefore minor and fast certification structures are needed to control other devices with mobile device that has lower computing capacity. To solve the above problems, this paper wants to build safe certification frame work for internet and mobile users to control household devices safely. New certification structure is proposed to get out of heavy certification structure like PKI and to minimize encrypting and decrypting operation by compounding session key and public key.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.79-82
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• 2017

IoT devices are interconnected in global network with different functionalities and manage the data transfer in cloud computing. IoT devices can be used anytime, anywhere with any device with different applications and protocols. Same devices but different applications according to end user requirements such as sensors and Wi-Fi devices, reusability of these applications can enhance the development process. However, large number of variations in cloud computing make it difficult the features selection in application because of compatibility issues of devices. In this paper we have proposed multi-Software Product Lines (multi-SPLs) approach to manage the variabilities and commonalities of IoT applications and protocols. Feature modeling is used to manage the commonalities and variabilities of SPL. We proposed that multi-SPLs feature model is more appropriate for modeling of IoT applications and protocols.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.71-85
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• 2014

This study proposes the future strategy of semiconductor companies corresponding to the growth of cloud computing. Cloud computing is the delivery of IT resources such as hardware and software as a service rather than a product, and it is expected to significantly change the IT market. By employing the scenario planning method, this study develops a total of eight scenario cases, and presents the three possible scenarios including the best market, the worst market, and the neutral market scenario. This study suggests the future strategy of semiconductor companies based on the best market scenario (increasing firms' IT expenditure, increasing the complexity and performance of devices, the frequent replacement of devices). The suggested future strategy of semiconductor includes that the semiconductor companies need to strengthen their price competitiveness, secure the next generation technologies, and develop the better capability for market prediction with the growth of cloud computing. This study will help semiconductor companies set up the strategy direction of technology development, and understand the connections between cloud computing and the memory semiconductor industry. This study has practical implications for semiconductor industry to prepare for the future of cloud computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1002-1015
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• 2018

The advent of Internet of Things (IoT) and the evident inadequacy of Cloud networks concerning management of numerous end nodes have brought about a shift of paradigm giving birth to Fog computing. Fog computing is an extension of Cloud computing that extends Cloud resources at the edge of the network, closer to the user. Cloud computing has become one of the essential needs of people over the Internet but with the emerging concept of IoT, traditional Clouds seem inadequate. IoT entails extremely low latency and for that, the Cloud servers that are distant and unknown to the user appear to be unsuitable. With the help of Fog computing, the Fog devices installed would be closer to the user that will provide an immediate storage for the frequently needed data. This paper discusses data migration between different storage types especially between Cloud devices and then presents a mechanism to migrate data between Cloud and Fog Layer. We call this mechanism Adaptive Deadline-Aware Scheme (ADAS) for Data migration between Cloud and Fog. We will demonstrate that we can access and process latency sensitive "hot" data through the proposed ADAS more efficiently than with a traditional Cloud setup.