• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.13-24
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• 2013

IEEE 802.15.4 network and WiFi network are installed to overlap each other and configured to use adjacent frequency bands in which case the communication service required by applications can not be guaranteed because of randomly increased frame transmission delay and frequent frame transmission failures at nodes in IEEE 802.15.4 network. In this paper, transmission delay model at IEEE 802.15.4 nodes and an experimental system to evaluate the interference from WiFi traffic are described, then elements for the evaluation of interference are measured with the analysis of their characteristics. A sequential method of using medium access layer and physical layer elements of IEEE 802.15.4 protocols is proposed to decide interference from WiFi network. With the proposed method, if an evaluation function having frame transmission failures and transmission delay as variables returns a value greater than a threshold, intensive measurements of wireless channel power are carried out subsequently and the final decision of interference is made by the calculated average channel power. Experimental results of the method show that the decision time is reduced with increased frequency of decision in comparison to an other similar method.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.1_2
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• pp.11-19
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• 2001

근래에 들어, 백플레인 버스를 기반으로 하는 멀티프로세서 시스템의 프로세서간 통신에도 TCP/IP와 같은 표준 네트워크 프로토콜을 이용하여 표준 MAC 계층을 구현하는 것이 일반적이다. 본 논문은 이러한 MAC 에뮬레이션 기반 버스 네트워크상에서 내장형 실시간 은용을 지원하기 위한 최악 응답 시간 분석법을 제시한다. 본 논문의 분석법은 구체적으로 MAC 에뮬레이션 방법의 하나인 ANSI BusNet 프로토콜을 대상으로 진행된다. 각 실시간 태스크를 주기, CPU 시간, 종료시한, 메시지 패킷 개수로 모델링하고 스케쥴 가능성, 즉 주어진 종료 시한 내에 작업을 완료할 수 있는지의 여부를 검사하는 수식을 유도한다. 이를 위해 물리적인 버스 특성을 고려한 버스 전송 모델을 제시하고, 버스 중재 방식과 버스 하드웨어의 캐슁 지원 여부에 따른 스케쥴 가능성을 분석한다. 또한 본 논문에서는 실험을 통해 블록 전송이 실시간 통신 성능에 미치는 영향을 살펴본다. 비록 본 논문의 분석법이 BusNet에 기반하여 개발되었지만, BusNet이 대부분의 백플레인 하드웨어가 지원하는 기본적인 기능만을 가정하고 있으므로, 본 논문의 분석법은 다른 종류의 백플레인 네트워크 프로토콜에도 쉽게 적용될 수 있다.

• The KIPS Transactions:PartC
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• v.8C no.5
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• pp.607-614
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• 2001

With a remarkable growth and expansion of Internet, the security issues emerged from intrusions and attacks such as computer viruses, denial of services and hackings to destroy information have been considered as serious threats for Internet and the private networks. To protect networks from those attacks, many vendors have developed various security systems such as firewalls, intrusion detection systems, and access control systems. However, managing those systems individually requires too much work and high cost. Thus, in order to manage integrated security management and establish consistent security management for various security products, the policy model of PN-ISMS (Policy Based Integrated Security Management System) has become very important. In this paper, present the hierarchical policy model which explore the refinement of high-level/conceptual policies into a number of more specific policies to form a policy hierarchy. A formal method of policy description was used as the basis of the mode in order to achieve precision and generality. Z-Notation was chosen for this propose. The Z-Notation is mathematical notation for expressing and communicating the specifications of computer programs. Z uses conventional notations of logic and set theory organized into expressions called schemas.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.151-157
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• 2021

Recently, IoT technology has been applied to various cloud environments, requiring accurate verification of various information generated by IoT devices. However, due to the convergence of IoT technologies and 5G technologies, accurate analysis is required as IoT information processing is rapidly processed. This paper proposes a blockchain-based multi-IoT verification model for overlay cloud environments. The proposed model multi-processes IoT information by further classifying IoT information two layers (layer and layer) into bits' blockchain to minimize the bottleneck of overlay networks while ensuring the integrity of information sent and received from embedded IoT devices within local IoT groups. Furthermore, the proposed model allows the layer to contain the weight information, allowing IoT information to be easily processed by the server. In particular, transmission and reception information between IoT devices facilitates server access by distributing IoT information from bits into blockchain to minimize bottlenecks in overlay networks and then weighting IoT information.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.6-10
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• 2009

In these days, recommendation service in mobile environments is in the limelight due to the spread of mobile devices and an increase of information owing to advancement of computer network. The restaurant recommendation system reflecting user preference was proposed. This system uses Bayesian network to model user preference and analytical hierarchical process to recommend restaurants, but static inference model for user preference used in the system has some limitations that cannot manage changing user preference and enormous user survey must be preceded. This paper proposes a learning method for Bayesian network based on user requests. The proposed method is implemented on mobile devices and desktop, and we show the possibility of the proposed method through experiments.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.236-240
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• 2008

본 논문은 IEEE 802.11 네트워크 환경에서 다중 노드를 하나의 클라스터링 시스템으로 구축하고, UCC(User Created Contents)와 같은 사용자 멀티미디어 데이터를 모든 노드가 실시간으로 공유할 수 있는 시스템을 제안한다. 이를 위해 본 논문은 응용계층의 새로운 패러다임 모델을 제안 하였고, 이에 따라 Ad Hoc 네트워크기술과 Clustering 기술을 사용하였다. 또한 제안된 기술의 안정된 서비스를 제공하기 위해서 HeartBeat 기술을 활용한 시스템 모델을 제안한다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3896-3909
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• 2000

The MPEG Video Service of hilving il property of continuity and large cilPilcity needs lilrge network capilcity. Because most of users have a heterogeneous network environment, it's not efficient way that all users have same size of video data to provide them with real time video service. Therefore, for the pUl1Xlse of an efficient and appropriate utilization of network resources, it requires to develop and deploy a new scalable transmission technique in consideration of respective network environment and individual clients computing power. The purpose of this paper is to develop a technology that can adjust the amount of dilta transmitted as an M1'EG video stream according to its gi yen communication bandwidth, and a technique that can reflect dynamic bilndwidth while playing a video stream. For this purpose, we propose a TFS (Temporal-Fidelity Scaling) technique that splits the MPEG video stream into various substream according to picture type or resolution. Those methods proposed her can filcilitilte an effective use of network resources, and provide multimedia MPEG video services in real- time with respect to individual client computing environment

• Journal of KIISE
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• v.42 no.4
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• pp.451-458
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• 2015

Previous multimodal learning methods focus on problem-solving aspects, such as image and video search and tagging, rather than on knowledge acquisition via content modeling. In this paper, we propose the Multimodal Concept Hierarchy (MuCH), which is a content modeling method that uses a cartoon video dataset and a character-based subtitle generation method from the learned model. The MuCH model has a multimodal hypernetwork layer, in which the patterns of the words and image patches are represented, and a concept layer, in which each concept variable is represented by a probability distribution of the words and the image patches. The model can learn the characteristics of the characters as concepts from the video subtitles and scene images by using a Bayesian learning method and can also generate character-based subtitles from the learned model if text queries are provided. As an experiment, the MuCH model learned concepts from 'Pororo' cartoon videos with a total of 268 minutes in length and generated character-based subtitles. Finally, we compare the results with those of other multimodal learning models. The Experimental results indicate that given the same text query, our model generates more accurate and more character-specific subtitles than other models.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.950-953
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• 2007

무선 인프라와 이동성 지원 기술의 발달로 한 장소에서 머물던 무선 인터넷 서비스를 이동 중에도 받을 수 있게 되었다. 이러한 환경의 변화로 인해 모바일 기기의 이동성에 대한 관심은 더욱 더 커지고 있으며, 이동성뿐만 아니라 유선과 마찬가지로 끊김 없는 서비스를 받고자 하는 요구도 높아지고 있다. 모바일 노드의 이동성을 지원하기 위해서 기존 네트워크 계층의 Mobile IP 와는 달리 전송 계층에서 동작하는 mSCTP (mobile Stream Control Transmission Protocol)가 등장하였다. mSCTP 는 기존 SCTP 의 멀티호밍과 동적으로 IP 주소를 변경할 수 있는 DAR (Dynamic Address Resolution) 시그널을 이용하여 모바일 노드의 핸드오프를 지원하고 있다. 하지만, IP 주소의 추가, 변경 및 삭제에 대한 각 시그널의 전송 시기에 대한 정의가 없어 전송 시기를 결정하는 메커니즘이 필요하다. 따라서 본 논문에서는 퍼지 IF-THEN 룰을 적용한 퍼지 모델을 이용하여 이러한 문제점을 해결하고자 한다. 모바일 노드가 이동하게 될 새로운 네트워크의 신호 세기와 현재 네트워크 신호와의 신호비를 퍼지 모델에 입력하고 그 결과 값을 통해 시그널 전송 시기를 판단한다. 모바일 노드는 핸드오프 시기를 적절히 판단 할 수 있기 때문에 잘못된 핸드오프로 인한 세션의 단절을 줄일 수 있고, 기존에 발생하던 핸드오프 지연 시간을 줄일 수 있어 이동 중에도 손실 없는 서비스를 제공 받을 수 있게 된다.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.189-194
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• 2020

Recently, various clustering techniques have been studied to efficiently handle data generated by heterogeneous IoT devices. However, existing clustering techniques are not suitable for mobile IoT devices because they focus on statically dividing networks. This paper proposes a probabilistic deep learning-based dynamic clustering model for collecting and analyzing information on IoT devices using edge networks. The proposed model establishes a subnet by applying the frequency of the attribute values collected probabilistically to deep learning. The established subnets are used to group information extracted from seeds into hierarchical structures and improve the speed and accuracy of dynamic clustering for IoT devices. The performance evaluation results showed that the proposed model had an average 13.8 percent improvement in data processing time compared to the existing model, and the server's overhead was 10.5 percent lower on average than the existing model. The accuracy of extracting IoT information from servers has improved by 8.7% on average from previous models.