• 방송공학회논문지
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• 제19권1호
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• pp.14-23
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• 2014

본 논문은 최근 이동통신 네트워크에서 폭발적으로 증가하고 있는 비디오 트래픽이 야기한 문제와 요구사항의 해결책으로써, SDN(Software Defined Network)을 기반으로 하는 다중 무선 접속 기술(Multiple Radio Access Technology)의 제어 기법을 제안하고 그 성능을 자체 구축한 테스트베드를 통하여 평가한다. 이를 위하여, 먼저 사업자 입장에서 3rd-party의 비디오 트래픽을 사업자망으로부터 우회(off-loading)시키는 방안의 필요성과, 사용자에게 저비용으로 고속의 대용량 비디오 콘텐츠 서비스를 제공하는 방안에 대하여 논의한다. 또한 성능평가를 위한 테스트베드는 OpenStack 클라우드 및 SDN 기반으로 구축 하였다. 이를 통해, OpenFlow와 Open Switch를 이용하여 2개의 2.4GHz 무선 랜 링크와 3개의 5GHz 무선 랜 링크가 동시에 하나의 서비스를 지원하도록 함으로서 820 Mbps 급의 초고속 클라우드 비디오 서비스를 위한 전송 속도를 실현하였다.

• ETRI Journal
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• 제37권2호
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• pp.348-358
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• 2015

Mobile devices have been widespread and become very popular with connectivity to the Internet, and a lot of desktop PC applications are now aggressively ported to them. Unfortunately, mobile devices are often vulnerable to malicious attacks due to their common usage and connectivity to the Internet. Therefore, the demands on the development of mobile security systems increase in accordance with advances in mobile computing. However, it is very hard to run a security program on a mobile device all of the time due the device's limited computational power and battery life. To overcome these problems, we propose a novel mobile security scheme that migrates heavy computations on mobile devices to cloud servers. An efficient data transmission scheme for reducing data traffic between devices and servers over networks is introduced. We have evaluated the proposed scheme with a mobile device in a cloud environment, whereby it achieved a maximum speedup of 13.4 compared to a traditional algorithm.

• 전자통신동향분석
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• 제33권5호
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• pp.64-75
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• 2018

People usually check their social media, e-mail, and online news on their smartphones using their carrier network. During this process, much of the data traffic is offloaded to ubiquitous Wi-Fi networks. Such offloading will continue to increase rapidly because of flourishing public Wi-Fi networks located around the world. In this technical report, we first investigate domestic public Wi-Fi projects, and followed by foreign projects. In addition, we investigate the Wi-Fi technology evolution that has been standardized in IEEE 802. More and more people are tending to use Wi-Fi, not only at home or work, but also on public transport such as buses and trains. Hence, it is important to come up with ideas that can realize Wi-Fi onboard. The key technologies needed here are related to a mobile wireless backhaul between trains and trackside radio equipment, or between buses and roadside units. Thus, we also investigate the mobile wireless backhaul technologies and their trends.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1884-1903
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• 2019

Device-to-Device (D2D) communications can provide proximity based services in the future 5G cellular networks. It allows short range communication in a limited area with the advantages of power saving, high data rate and traffic offloading. However, D2D communications may reuse the licensed channels with cellular communications and potentially result in critical interferences to nearby devices. To control the interference and improve network throughput in overlaid D2D cellular networks, a novel channel assignment approach is proposed in this paper. First, we characterize the performance of devices by using Poisson point process model. Then, we convert the throughput maximization problem into an optimal spectrum allocation problem with signal to interference plus noise ratio constraints and solve it, i.e., assigning appropriate fractions of channels to cellular communications and D2D communications. In order to mitigate the interferences between D2D devices, a cluster-based multi-channel assignment algorithm is proposed. The algorithm first cluster D2D communications into clusters to reduce the problem scale. After that, a multi-channel assignment algorithm is proposed to mitigate critical interferences among nearby devices for each D2D cluster individually. The simulation analysis conforms that the proposed algorithm can greatly increase system throughput.

• 인터넷정보학회논문지
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• 제23권2호
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• 한국통신학회논문지
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• 제41권7호
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• pp.789-798
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• 2016

본 논문에서는 home 소형셀 대비 넓은 커버리지를 갖고 많은 사용자를 서비스 하는 enterprise/urban 소형셀 환경에서 적용할 수 있는 사용자 컨텍스트 기반 캐시 알고리즘을 제안한다. 소형셀 캐시 기법은 소형셀 사용자의 웹 트래픽을 소형셀 내부에 위치한 저장 공간에 저장하는 방법으로 코어망 트래픽을 감소시키는 효과가 있다. 본 논문에서는 기존의 알고리즘과 달리 Mobile Edge Computing(MEC)의 개념을 적용하여 소형셀 내부가 아닌 edge server에 사용자 트래픽을 캐시하며 사용자 특성을 반영하기 위해 사용자를 그룹화한다. 또한, 그룹별 저장 공간의 크기를 달리하고, 캐시 업데이트 주기를 캐시 적중률에 따라 변경하여 코어망으로부터 제공받는 트래픽을 감소하고자 하였다. 성능 분석 결과 기존 알고리즘 대비 캐시 적중률 측면에서 약 11%, cache efficiency 측면에서 약 5.5%의 성능 향상을 확인할 수 있었다.