• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.31-50
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• 2009

As the collected information which is static or dynamic is infinite in ubiquitous computing environments, information overload and invasion of privacy have been pressing issues in the recommendation service. In this study, we propose a recommendation service procedure through P2P, The P2P helps customer to obtain effective and secure product information because of communication among customers who have the similar preference about the products without connection to server. To evaluate the performance of the proposed recommendation service, we utilized real transaction and product data of the Korean mobile company which service character images. We developed a prototype recommender system and demonstrated that the proposed recommendation service makes an effect on recommending product in the ubiquitous environments. We expect that the information overload and invasion of privacy will be solved by the proposed recommendation procedure in ubiquitous environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2153-2166
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• 2017

Web apps have a problem that they cause same data to be repetitively retrieved, processed, and displayed when web browsers load different web pages. To resolve the problem, this paper presents and evaluates a new method for restructuring of Java web apps. This approach dynamically analyzes Java web apps from the MVC (Model-View-Controller) architecture point of view and identifies redundant data by using the composite view pattern. Then the input apps are restructured in order not to load the redundant data when users make requests for new pages. This restructuring generates new web apps that conform to the MVC architecture and improve the performance of input web apps. The experimental results showed that when compared to legacy web apps, the restructured apps' response time was reduced on desktop PCs and mobile devices by 38% and 55%, respectively. In addition, case studies using open-source web apps showed the applicability of the proposed approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.707-718
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• 2008

The methodology of classifying traffics is changing from payload based or port based to machine learning based in order to overcome the dynamic changes of application's characteristics. However, current state of traffic classification using machine learning (ML) algorithms is ongoing under the offline environment. Specifically, most of the current works provide results of traffic classification using cross validation as a test method. Also, they show classification results based on traffic flows. However, these traffic classification results are not useful for practical environments of the network traffic monitoring. This paper compares the classification results using cross validation with those of using split validation as the test method. Also, this paper compares the classification results based on flow to those based on bytes. We classify network traffics by using various feature sets and machine learning algorithms such as J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, and NaiveBayes. In this paper, we find the best feature sets and the best ML algorithm for classifying traffics using the split validation.

• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1282-1291
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• 2005

This paper presents the EVM(Embedded Virtual Machine) which enables the execution of dynamic applications loaded in the embedded systems such as Mobile Devices(mobile phone, PDA), Set-Top Box, and Digital TV using downloading techniques. To accomplish this goal, we defined a SIL-(Standard Intermediate Language) code, and implemented a Bytecode-to-SIL translator which enables the execution of programs written in java language in the EVM platform without JVM, and a MSIL--to-SIL- translator which enables for programs written in .NET language to be executed in the EVM platform without .NET platform. Also, we developed a EFF(Executable File Format) builder as an assembler which translates SIL codes into an executable file, *.evm, and implemented the EVM which reads the *.evm file and executes it. The virtual machine for embedded systems developed in this paper is the software technologies that enable the execution of applications or contents without changes to when the platforms change. In fact, the virtual machine suggested here is not only usable as a standard model for existing virtual machines but also aid in more efficient execution of applications loaded in the embedded systems such as Mobile Devices, Digital TV, and Set-Top Box.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2A
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• pp.55-62
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• 2003

This paper proposes an efficient and differentiated MAC scheduling algorithm for Bluetooth systems. The proposed algorithm guarantees QoS (Quality of Service) requirement of each master-slave pair or application. Conventional MAC scheduling algorithms for Bluetooth take priority of each pair into consideration and demonstrate relatively reasonable performance. However, their performances may depend on traffic characteristic, or they are limited by overheads for signaling. In this paper, we propose a new MAC scheduling algorithm superior to the conventional algorithms from the viewpoints of throughput, delay, and supporting QoS. Our proposed algorithm is evaluated via computer simulations under various environments and compared with the conventional scheduling algorithms. Simulation results indicate that the proposed algorithm shows better performance than the existing algorithms, and can support the QoS demand of each pair.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1081-1094
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• 2016

KREONET is a principal national R&E network running by KISTI in Korea. It uniquely provides production research network services for around 200 non-profit research and educational organizations, based on hybrid (IP and non-IP) network infrastructure. However, KREONET is limited to meet various needs of new network services for advanced Science & Technology (S&T) users because its infrastructure is inherently derived form classical hardware-based, fixed and closed environments. So, KREONET-S is designed to provide advanced S&T services to catch up with time-to-research and time-to-collaboration. In this paper, we present a system architecture of KREONET-S based on network infrastructure that consists of data and control planes separately. Furthermore, we propose and describe VDN service which is capable of building a virtual dedicate & bandwidth-guaranteed network for S&T group dynamically. we implement VDN application on KREONET-S and then perform performance analysis for proving that KREONET-S system and VDN application can be a good solutions to cope with new network paradigms for various advanced S&T applications and users.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.73-85
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• 2013

As a way of augmenting constrained resources of mobile devices such as CPU and memory, many works on mobile cloud computing (MCC), where mobile devices utilize remote resources of cloud services or PCs, /have been proposed. A typical approach to resolving resource problems of mobile nodes in MCC is to offload functional components to other resource-rich nodes. However, most of the current woks do not consider a characteristic of dynamically changed MCC environment and propose offloading mechanisms in a conceptual level. In this paper, in order to ensure performance of highly complex mobile applications, we propose four different types of offloading mechanisms which can be applied to diverse situations of MCC. And, the proposed offloading mechanisms are practically designed so that they can be implemented with current technologies. Moreover, we define cost models to derive the most sutilable situation of applying each offloading mechanism and prove the performance enhancement through offloadings in a quantitative manner.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.11
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• pp.831-835
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• 2009

Volunteer computing is a computing paradigm in which the operations of a large-scale application are processed using idle resources of Internet-connected computers. The redundant-work distribution techniques used in the volunteer computing environment cause some problems like delay of the work completion time or inefficiency of the work execution due to continuous requests of the work redistribution because the techniques do not consider the characteristics of works and dynamic resources. To cope with such problems this paper suggests a P2P based regional matchmaking technique which can redistribute works in consideration of the characteristics of the works and the participant resources. The techniques consist of the profiles for works/resources, the module for monitoring the available resources spaces, the protocols for the matching request/reply, the module for match-ratio calculation, and so on.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.61-67
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• 2023

The rise of 5G and the proliferation of smart devices have underscored the significance of multi-access edge computing (MEC). Amidst this trend, interest in effectively processing computation-intensive and latency-sensitive applications has increased. This study investigated a novel task offloading strategy considering the probabilistic MEC environment to address these challenges. Initially, we considered the frequency of dynamic task requests and the unstable conditions of wireless channels to propose a method for minimizing vehicle power consumption and latency. Subsequently, our research delved into a deep reinforcement learning (DRL) based offloading technique, offering a way to achieve equilibrium between local computation and offloading transmission power. We analyzed the power consumption and queuing latency of vehicles using the deep deterministic policy gradient (DDPG) and deep Q-network (DQN) techniques. Finally, we derived and validated the optimal performance enhancement strategy in a vehicle based MEC environment.