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

• 대한간호학회지
• /
• 제34권2호
• /
• pp.290-296
• /
• 2004

Purpose: The purpose of this study was to develop a home care nursing network system for operating home care effectively and efficiently by utilizing a wire-wireless network and mobile computing in order to record and send patients' data in real time, and by combining the headquarter office and the local offices with home care nurses over the Internet. It complements the preceding research from 1999 by adding home care nursing standard guidelines and upgrading the PDA program. Method: Method/l and Prototyping were adopted to develop the main network system. Result: The detailed research process is as follows: 1 )home care nursing standard guidelines for Diabetes, cancer and peritoneal-dialysis were added in 12 domains of nursing problem fields with nursing assessment/intervention algorithms. 2) complementing the PDA program was done by omitting and integrating the home care nursing algorhythm path which is unnecessary and duplicated. Also, upgrading the PDA system was done by utilizing the machinery and tools where the PDA and the data transmission modem are integrated, CDMX-1X base construction, in order to reduce a transmission error or transmission failure.

• International Journal of Internet, Broadcasting and Communication
• /
• 제10권2호
• /
• pp.25-30
• /
• 2018

This paper is about wine quality classification with multilayer perceptron using the deep neural network. Wine complexity is an issue when predicting the quality. And the deep neural network is considered when using complex dataset. Wine Producers always aim high to get the highest possible quality. They are working on how to achieve the best results with minimum cost and efforts. Deep learning is the possible solution for them. It can help them to understand the pattern and predictions. Although there have been past researchers, which shows how artificial neural network or data mining can be used with different techniques, in this paper, rather not focusing on various techniques, we evaluate how a deep learning model predicts for the quality using two different activation functions. It will help wine producers to decide, how to lead their business with deep learning. Prediction performance could change tremendously with different models and techniques used. There are many factors, which, impact the quality of the wine. Therefore, it is a good idea to use best features for prediction. However, it could also be a good idea to test this dataset without separating these features. It means we use all features so that the system can consider all the feature. In the experiment, due to the limited data set and limited features provided, it was not possible for a system to choose the effective features.

• 한국정보통신학회논문지
• /
• 제10권2호
• /
• pp.315-320
• /
• 2006

분산 컴퓨터 네트워크는 단일 시스템의 동작 정지로 인한 전체 시스템에 미치는 영향을 적 게 함으로써 신뢰도를 높일 수 있고, 한 개의 대형 시스템을 활용하는 것 보다 저렴한 비용으로 보다 나은 성능을 얻을 수 있는 장점이 있다. 또한 시스템이 확장 및 재구성이 용이하다[1]. 그러나, 분산 컴퓨팅 환경에서 네트워크를 통한 데이터의 공유는 실생활이 되고 있는 반면 네트워크 환경에서 데이터의 무결성과 보안에 대한 위험성은 증가하고 있다[2],[3]. 따라서 본 논문에서는 운영적 요소와 기술적 요소에 대한 분석을 통해 이러한 요소들을 결합시키기 위한 네트워크 암호화 데이터베이스 보안시스템 모델을 제시한다. 제시한 모델에 운영적 요소와 기술적 요소를 체계적으로 결합시킨다면 분산 컴퓨팅 환경에서 허가받지 않은 사용자로부터 데이터를 안전하게 보호할 암호화 데이터베이스 보안 시스템을 구축할 수 있을 것이다.

• 한국정보통신학회논문지
• /
• 제12권6호
• /
• pp.1128-1133
• /
• 2008

상황인식(Context-aware)은 인간-컴퓨터 상호작용의 단점을 극복하기 위한 방법으로써 많은 주목을 받고 있다. 본 논문에서는 UoC(Ubiquitous system on Chip)로 구현될 수 있는 상황인식 시스템 구조를 제안한다. 본 논문은 유비쿼터스 컴퓨팅 시스템을 구현하기 위해 CRS(Context Recognition Switch)와 DOS(Dynamic and Optimal Standard)의 개념을 포함한 Pre-processor, HPSP(High Performance Signal Processor), Network Topology Processor의 부분으로 구성된 UoC Architecture를 제안한다. 또한, IEEE 802.15.4 WPAN(Wireless Personal Area Network) Standard에 의해 구현된 UoC를 보여준다. 제안된 상황인식 기반의 UoC Architecture는 주거 환경에서 컨텍스트를 인식하여 사용자를 지원하는 지능형 이동 로봇 등에 적용될 수 있을 것이다.

• 한국컴퓨터정보학회논문지
• /
• 제11권4호
• /
• pp.119-125
• /
• 2006

네트워크를 통하여 서로 연결된 컴퓨팅 자원들의 집합을 분산 시스템이라고 정의할 수 있다. 최적화 문제 영역에서 가장 중요한 해결 기법 중에 하나인 병렬 유전자 알고리즘은 분산 시스템을 기반으로 하고 있다. 인터넷과 이동 컴퓨팅과 같은 동적 네트워크 환경 하에서 네트워크의 상태는 가변적으로 변할 수 있어 기존의 병렬 유전자 알고리즘을 분산 시스템에서 최적화 문제를 해결하기 위하여 그대로 사용하기에는 비효율적이다. 본 논문에서는 동적 네트워크 환경 하에서 분산 에이전트를 사용하여 병렬 유전자 알고리즘을 효율적으로 사용할 수 있는 기법을 제시한다.

• 정보보호학회논문지
• /
• 제9권2호
• /
• pp.73-82
• /
• 1999

이동하는 모빌노드(mobile node)의 인증 기법 중에, 홈 에이전트(home agent)와 모빌노드, 외부 에이전트(foreign agent)를 거치는 triangle 인증 기법이 있다. 이 기법의 문제점은, 모빌노드의 이동이 빈번하게 발생되면 인증 절차 또한 비례적으로 이루어져야 하므로 무선통신 환경상의 통신 오버헤드가 증가하게 된다. 이러한 문제점을 개선하기 위해서 본 논문에서는, 고차잉여류의 개념에 근거한 다중서명방식을 이용하여 모빌노드가 이동할 때마다 필요했던 홈 에이전트와 외부 에이전트간의 인증을 생략한 자체 인증 방식을 제안하고자 한다. In an open network computing environment a host cannot to identity its users correctly to network services. In order to prevent this thing we present the design of a authentication scheme 솟 using the notion of rth -residuosity problem and discrete logarithm problem which is proposed by S. J. Park et al. The proposed scheme described here is efficient method for mutual authentication without leakage of users identity in mobile communication system that ensure user anonymity and untraceability.

• International Journal of Computer Science & Network Security
• /
• 제21권4호
• /
• pp.65-74
• /
• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권3호
• /
• pp.874-890
• /
• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.

• International Journal of Computer Science & Network Security
• /
• 제23권10호
• /
• pp.89-96
• /
• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.