• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• 정보처리학회논문지A
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• 제17A권3호
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• pp.113-120
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• 2010

이븐 연결망은 고장허용 다중컴퓨터에 대한 하나의 모형으로 제안된 연결망으로, 간단한 라우팅 알고리즘, 최대고장허용도, 노드 중복 없는 경로와 같은 여러 가지 유용한 성질과 알고리즘들이 분석되었다. 기존에 발표된 라우팅 알고리즘과 노드 중복 없는 경로를 구성하는 알고리즘은 최적임이 증명되었다. 하지만 아직까지 이븐 연결망에서 에지 중복 없는 스패닝 트리를 구성하는 기법은 소개되지 않았다. 에지 중복 없는 스패닝 트리는 상호연결망의 고장허용도의 성능 향상과 효율적인 방송 기법을 분석하기 위해서 사용되는 매우 유용한 기법이다. 기존에 발표된 라우팅 알고리즘 또는 노드 중복 없는 경로를 구성하는 알고리즘은 라우팅 또는 노드 중복 없는 경로를 위한 알고리즘으로 에지 중복 없는 스패닝 트리를 구성하기 위해 적용될 수 없는 알고리즘이다. 본 논문에서는 이븐 연결망 $E_d$에서 에지 중복 없는 스패닝 트리를 구성하는 알고리즘을 제안한다.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.896-914
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• 2020

With the rapid growth of network traffic, a large number of connected devices, and higher application services, the traditional network is facing several challenges. In addition to improving the current network architecture and hardware specifications, effective resource management means the development trend of 5G. Although many existing potential technologies have been proposed to solve the some of 5G challenges, such as multiple-input multiple-output (MIMO), software-defined networking (SDN), network functions virtualization (NFV), edge computing, millimeter-wave, etc., research studies in 5G continue to enrich its function and move toward B5G mobile networks. In this paper, focusing on the resource allocation issues of 5G core networks and radio access networks, we address the latest technological developments and discuss the current challenges for resource management in 5G.

• 한국정보통신학회논문지
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• 제14권12호
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• pp.2593-2600
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• 2010

최근 무선이동 통신기술 및 임베디드 시스템 기술의 발달과 더불어 그 적용성이 크게 확대되고 있는 MANET을 연구함에 있어서, 에지코스트(Edge Cost)에 기반한 MANET의 특징을 표현할 수 있는 모델링기법을 도입하여 네트워크의 상태변화에 따른 전달성능의 변화를 관찰한 연구결과를 제시하였다. 에지코스트 기반의 모델링 방법론은 4 가지의 에지상태를 통해 어느 한 시점에서의 네트워크의 상태를 표현하는 방법이다. 이러한 에지코스트 기반의 모델링에 Real Edge/Infinity Edge 개념 도입과 네트워크 내에 서로 다른 종류의 전달 프로토콜간 연동기능 도입을 가정하여 10개 시나리오의 대상 네트워크를 구분하여 DEVSim++ 엔진을 통해 시뮬레이션하였다. 그 결과 서로 다른 전달 프로토콜간 연동기능은, 네트워크에 포함된 전달 프로토콜의 종류와 연동기능 노드의 수가 많을수록 전달성능 향상 기여도가 높은 결과를 보였다.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• 한국정보과학회논문지:정보통신
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• 제36권6호
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• pp.514-527
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• 2009

무선 센서 네트워크는 군사적, 환경적 목적으로 다방면에서 활용될 수 있는데, 최근 유독가스, 산불, 지진과 같은 연속적인 성격을 가진 물체의 확산 경로를 추적하는 연구가 새롭게 진행되고 있다. 기존 연구에서는 연속적인 물체의 경계를 지역적으로 측정하기 위해 1-홉 이웃 노드들과의 통신을 통한 방식을 제시하였으나, 이러한 방식은 불필요하게 많은 노드들이 경계 노드로 선택되어 물체의 경계를 정확히 측정할 수 없는 문제를 안고 있다. 본 논문에서는 최소한의 경계 노드를 선별하기 위해 지역적인 드로네 삼각기법을 이용한 방법을 제안하고, 연속적인 물체를 에너지 효율적으로 추적하기 위한 센서의 동작 규칙을 규정한다. 모의실험 결과, 본 논문에서 제안한 방법이 기존의 1-홉 경계 설정과 비교해 경계 노드의 선택 정확도는 평균 29.95% 개선되면서도 경계 노드의 수는 평균 54.43% 감소하며, 통신 메시지 수와 에너지 소모량은 각각 평균 79.36%, 72.34% 향상됨을 보였다. 또한, MICAz mote를 이용한 현장실험을 통해 평균 48.38% 경계 노드 수가 감소함을 보였다.

• Journal of electromagnetic engineering and science
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• 제15권1호
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• pp.14-19
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• 2015

In multicellular wireless networks, intercell interference limits system performance, especially cell edge user performance. One promising approach to solve this problem is the intercell interference coordination (ICIC) scheme. In this paper, we propose a new ICIC scheme based on a resource partitioning approach to enhance cell edge user performance in a wireless multicellular system. The most important feature of the proposed scheme is that the algorithm is performed at each base station in a distributed manner and therefore minimizes the required information exchange between neighboring base stations. The proposed scheme has benefits in a practical environment where the traffic load distribution is not uniform among base stations and the backhaul capacity between the base stations is limited.

• 센서학회지
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• 제30권5호
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• pp.286-289
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• 2021

Image classification is one of the fundamental applications of computer vision. It enables a system to identify an object in an image. Recently, image classification applications have broadened their scope from computer applications to edge devices. The convolutional neural network (CNN) is the main class of deep learning neural networks that are widely used in computer tasks, and it delivers high accuracy. However, CNN algorithms use a large number of parameters and incur high computational costs, which hinder their implementation in edge hardware devices. To address this issue, this paper proposes a lightweight image classifier that provides good accuracy while using fewer parameters. The proposed image classifier diverts the input into three paths and utilizes different scales of receptive fields to extract more feature maps while using fewer parameters at the time of training. This results in the development of a model of small size. This model is tested on the CIFAR-10 dataset and achieves an accuracy of 90% using .26M parameters. This is better than the state-of-the-art models, and it can be implemented on edge devices.

• ETRI Journal
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• 제44권4호
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• pp.588-598
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• 2022

Since multi-access edge computing (MEC) was established as a key enabler of 5G, MEC based on 5G networks (5G MEC) has been perceived as a new business opportunity for many industry players, including telecom operators. Numerous 5G MEC cooperation announcements among companies playing their respective roles in the MEC ecosystem have been recently released. However, because of cooperative and competitive relationships among key players in the MEC ecosystem and the uncertainty of 5G MEC, the announcement of 5G MEC cooperation can negatively affect the telecom operators' firm value. This study investigates the market reaction to announcements of 5G MEC cooperation for telecom operators using an event study methodology. The empirical results show that announcements of 5G MEC cooperation have a negative impact on the telecom operators' firm value. The results also show that the early deployment of 5G networks may reduce the negative impact of 5G MEC cooperation announcements by reducing uncertainty.