• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2008년도 제38차 하계학술발표논문집 16권1호
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• pp.163-167
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• 2008

본 논문에서는 광범위 Community Computing 환경에서의 Community Computing Network의 에너지 효율과 신뢰성을 높이기 위한 수정된 SPaC(Simple Packet Combining)를 제안한다. 제안하는 수정된 SPaC는 같은 패킷을 두 개 이상의 오류가 있는 패킷을 이용하여 에러를 복구하는 기존의 SPaC를 수정하여 특정 threshold 값을 사용하여 감청 시 CPU의 처리량을 줄이고 패리티 패킷을 이용하여 높은 신뢰성과 보다 향상된 에너지 효율을 가진다.

• 대한임베디드공학회논문지
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• 제15권6호
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• pp.299-305
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• 2020

We have developed a scheduler that additionally consider network performance by extending the Kubernetes developed to manage lots of containers in cloud computing nodes. The network delay adapt characteristics of the compute nodes were learned during server operation and the learned results were utilized to develop placement algorithm by considering the existing measurement units, CPU, memory, and volume together, and it was confirmed that the low delay network service was provided through placement algorithm.

• 디지털산업정보학회논문지
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• 제8권3호
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• pp.75-85
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• 2012

In general, cellular network is deployed as distributed network architecture, so network equipment is spread out all over the coverage area. This case causes high cost. Recently, centralized network architecture has been deployable at cellular network since base station was able to be virtualized due to advance in computing power. The centralized network architecture in cellular network adjusts its cell radius dynamically and minimizes power consumption of the network. This paper introduces a new centralized deployment way of cellular network using SDR and cloud computing technology. Then, advantage and feasibility of the new network will be reviewed by implementing this novel network.

• 디지털산업정보학회논문지
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• 제9권1호
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• pp.43-52
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• 2013

Pervasive computing and social network are good resources in recommendation method. Collaborative filtering is one of the most popular recommendation methods, but it has some limitations such as rating sparsity. Moreover, it does not consider social network in pervasive computing environment. We propose an effective proactive friend recommendation method using social network and contexts in pervasive computing environment. In collaborative filtering method, users need to rate sufficient number of items. However, many users don't rate items sufficiently, because the rating information must be manually input into system. We solve the rating sparsity problem in the collaboration filtering method by using contexts. Our method considers both a static and a dynamic friendship using contexts and social network. It makes more effective recommendation. This paper describes a new friend recommendation method and then presents a music friend scenario. Our work will help e-commerce recommendation system using collaborative filtering and friend recommendation applications in social network services.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.107-114
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• 2023

The study provides the identification of vulnerabilities in the security issues by Wireless Network. To achieve it the research focus on packet flow analysis, end to end data communication, and the security challenges (Cybercrime, insider threat, attackers, hactivist, malware and Ransomware). To solve this I have used the systematic literature review mechanisms and demonstrative tool namely Wireshark network analyzer. The practical demonstration identifies the packet flow, packet length time, data flow statistics, end- to- end packet flow, reached and lost packets in the network and input/output packet statics graphs. Then, I have developed the proposed model that used to secure the Wireless network solution and prevention vulnerabilities of the network security challenges. And applying the model that used to investigate the security challenges and vulnerabilities of cloud computing services is used to fulfill the network security goals in Wireless network. Finally the research provides the model that investigate the security challenges and vulnerabilities of cloud computing services in wireless networks

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.89-100
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• 2022

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

• KNOM Review
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• 제22권3호
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• pp.1-12
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• 2019

최근 빅데이터와 AI를 이용한 연구들이 ICT 분야에서 주요 이슈로 부상하고 있다. 하지만 연구를 위한 빅데이터의 크기가 기하급수적으로 증가하면서 기존 네트워크 방식의 데이터 전송에 대해 사용자들은 빅데이터를 송수신하는데 걸리는 시간은 하드디스크를 복사하여 보내는 시간보다 느리다는 문제를 제기한다. 이에 따라 연구자들은데이터를 고속으로 전송하고, 다양한 네트워크의 구조를 수용할 수 있는 동적이고 유연한 네트워크 기술을 요구한다. SDN/NFV 기술은 네트워크를 프로그래밍하여 사용자들의 요구에 적절한 네트워크를 제공할 수 있는 기술로써, 네트워크의 유연성 및 보안성 문제를 해결할 수 있다. 또한 AI를 수행하는데 있어 문제가 되는 중앙집중적 방식의데이터 처리는 실시간성을 보장할 수 없고, 트래픽이 증가하는 경우 네트워크 지연이 발생한다. 이를 해결하기 위해 중앙집중적 방식을 탈피한 Edge-Computing 기술을 이용하여 해결할 수 있다. 본 논문에서는 SDN, NFV, Edge-Computing 기술에 대한 개념 및 연구 동향에 대해 알아보고, 세 가지 기술을 접목시켜 사용되는 데이터 중심 네트워크 기술 동향에 대해 분석한다.

• 통상정보연구
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• 제13권3호
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• pp.55-74
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• 2011

최근 전 세계 곳곳에서 폭발적으로 확산되고 있는 소셜컴퓨팅(Social Computing)은 정치, 경제, 사회, 문화 동 우리의 삶의 모든 영역에서 실로 지대한 영향을 미치고 있다. 웹 2.0을 기반으로 소셜협업, 소셜 퍼블리싱, 소셜 피드백 등 다양한 유형의 서비스가 제공되고 있으며, 향후 소셜 커넥션이나, 증강 현실, 위치기반서비스 등 더욱 발전된 형태의 진화된 소셜 컴퓨팅 서비스가 이루어질 것으로 기대된다. 그러나 흔히 SNS(Social Network Service), 또는 소셜 미디어 등으로 표현되는 네트워크를 이용한 이와 같은 서비스는 아직 그 정확한 개념과 특징이 구체적으로 정의된 것은 아니며, 이와 관련된 체계적인 연구도 부족한 형편이다. 특히 소셜 컴퓨팅의 비즈니스 활용, 특히 국제무역에서의 수용가능성에 대한 연구는 거의 이루어지고 있지 않은 형편이다. 본 연구는 이러한 문제 인식 하에 흔히 SNS, 소셜 미디어 등으로 표현되는 소셜 컴퓨팅(Social Computing)의 개념과 특징, 그 종류를 정리하고, 비즈니스, 특히 국제무역에서 이를 활용하는 수용 가능성에 대한 고찰을 목적으로 하고 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권11호
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• pp.2764-2783
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• 2012

Network virtualization supports future Internet environments and cloud computing. Virtualization can mitigate many hardware restrictions and provide variable network topologies to support variable cloud services. Owing to several advantages such as low cost, high flexibility, and better manageability, virtualization has been widely adopted for use in network virtualization platforms. Among the many issues related to cloud computing, to achieve a suitable cloud service quality we specifically focus on network and performance isolation schemes, which ensure the integrity and QoS of each virtual cloud network. In this study, we suggest a virtual network platform that uses Xen-based virtualization, and implement multiple virtualized networks to provide variable cloud services on a physical network. In addition, we describe the isolation of virtual networks by assigning a different virtualized network ID (VLAN ID) to each network to ensure the integrity of the service contents. We also provide a method for efficiently isolating the performance of each virtual network in terms of network bandwidth. Our performance isolation method supports multiple virtual networks with different levels of service quality.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.140-146
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• 2023

Resource allocation is one of the top challenges in Internet of Things (IoT) networks. This is due to the scarcity of computing, energy and communication resources in IoT devices. As a result, IoT devices that are not using efficient algorithms for resource allocation may cause applications to fail and devices to get shut down. Owing to this challenge, this paper proposes a novel algorithm for managing computing resources in IoT network. The fog computing devices are placed near the network edge and IoT devices send their large tasks to them for computing. The goal of the algorithm is to conserve energy of both IoT nodes and the fog nodes such that all tasks are computed within a deadline. A bi-partite graph-based algorithm is proposed for stable matching of tasks and fog node computing units. The output of the algorithm is a stable mapping between the IoT tasks and fog computing units. Simulation results are conducted to evaluate the performance of the proposed algorithm which proves the improvement in terms of energy efficiency and task delay.