• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.25-31
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• 2023

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.60-71
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• 2019

As IoT systems such as smart farms and smart cities become popular, a large amount of data collected from many sensor nodes is sent to a server in the Internet, which causes network traffic explosion, delay in delivery, and increase of server's workload. To solve these problems, the concept of fog computing has been proposed to store data between IoT systems and servers. In this study, we implemented a software platform of the fog node and applied it to the prototype smart farm system in order to check whether the problems listed above can be solved when using the fog node. When the fog node is used, the time taken to control an IoT device is lower than the response time of the existing IoT device-server case. We confirmed that it can also solve the problem of the Internet traffic explosion and the workload increase in the server. We also showed that the intelligent control of IoT system is feasible by having the data visualization in the server and real time remote control, emergency notification in the fog node as well as data storage which is the basic capability of the fog node.

• Railway Journal
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• v.18 no.2
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• pp.74-78
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• 2015

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.61-68
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• 2020

In the era of the fourth industrial revolution, many production plants are gradually evolving into smart factories that apply information and communication technology to manufacturing, distribution, production, and quality management. The conversion from conventional factories to smart factories has resulted in the automation of production sites using the internet and the internet of things (IoT) technology. Thus, labor-intensive production can easily collect necessary information. However, implementing a smart factory required a significant amount of time, effort, and money. In particular, labor-intensive production industries are not automated, and productivity is determined by human skill. A representative industry of such industries is sewing the industry. In the sewing industry, wherein productivity is determined by the operator's skills. This study suggests that production performance, inventory management and product delivery of the sewing industries can be managed efficiently with existing production method by using smart buttons incorporating IoT functions, without using automated machinery.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.127-128
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• 2018

본 프로젝트에서는 IoT를 활용한 스마트한 풋살 대관 예약 시스템이다. 최근 건강 및 취미에 관심이 많아지면서 하나의 국면으로 풋살에 대한 관심이 많아지고 있다. 이러한 관심은 관련 블로그 등과 같은 그룹 활동으로 많은 정보를 공유하고 있다. 하지만 풋살장 예약, 팀매칭, 용병에 구하기 등과 풋살장 관리에도 어려움이 있다. 이러한 문제점들을 해결하기 위해 본 프로젝트는 웹과 앱을 활용한 커뮤니티 제공, 풋살장 관리, 팀매칭, 용병 등을 실시간으로 공유하고 활용할 수 있도록 하였다. 팀매칭 및 용병은 실시간으로 상대팀과 채팅 통해 조율할 수 있고 용병게시판을 이용하여 용병을 구할 수 있다. 앱에서는 상대팀이 예약시간의 지각, 불참석을 하면 도착알림을 오게 해주고 풋살장에 도착하여 구장문을 열거나 닫고, 구장 조명을 키거나 끄거나, 구장 실시간 영상, 구장 실시간 이용현황 등을 볼 수 있는 기능들을 제공하며, IoT를 활용한 스마트하게 풋살장 관리 및 운영, 활용할 수 있는 시스템이다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.728-738
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• 2016

We are currently able to share not only the information from humans but also the data from connected things on the Internet. We are getting close to IoT era because of progress of Information Communication Technology. Therefore, the ways of data transfer are offered between things to things. One of typical way is the HTTP protocol. However, The field of Internet of Things platforms requires more fast and more stable communication protocol to handle massive data. The system supporting HTTP protocol, there is a problem of transmission efficiency in a relatively large header compared to data. also HTTP protocol system overload situations and the problem of data compatibility happens due to each standard of many organizations. Thus, To solve these problems, I suggest the data collection solution based on MQTT protocol for the operation of the stable IoT platforms.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.3
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• pp.81-90
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• 2018

Due to the dramatic advancement of IoT (Internet of Things), it is expected that tens of billions of IoT devices will be connected by the year 2024. Furthermore, as IoT technologies evolves, the security management in IoT platforms has become a critical issue. For example, there are interworking problems between heterogeneous IoT platforms caused by differences from communication protocols, security policies, etc. in each platform. In addition, unsecured interworking can cause major security issues including the information leakage. In this paper, in order to solve these problems, a security interworking architecture is proposed and implemented in both FIWARE and oneM2M, which are representative IoT platforms. First, the security architecture of FIWARE is analyzed and implemented, and then the security framework based on OAuth 2.0 is developed on Mobius platform. Finally, in order to validate the proposed security interworking architecture, a LED (Light-Emitting Diode) example, where the LED is controlled by only authorized users, is developed. The proposed architecture can be extended to the diverse IoT platforms and devices.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.101-108
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• 2022

We propose an intrusion detection model that detects denial-of-service(DoS) and distributed reflection denial-of-service(DRDoS) attacks, based on the empirical data of each internet of things(IoT) device by training system and network metrics that can be commonly collected from various IoT devices. First, we collect 37 system and network metrics from each IoT device considering IoT attack scenarios; further, we train them using six types of machine learning models to identify the most effective machine learning models as well as important metrics in detecting and distinguishing IoT attacks. Our experimental results show that the Random Forest model has the best performance with accuracy of over 96%, followed by the K-Nearest Neighbor model and Decision Tree model. Of the 37 metrics, we identified five types of CPU, memory, and network metrics that best imply the characteristics of the attacks in all the experimental scenarios. Furthermore, we found out that packets with higher transmission speeds than larger size packets represent the characteristics of DoS and DRDoS attacks more clearly in IoT networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1494-1502
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• 2016

With the increasing availability of medical sensors and Internet of Things(IoT) devices for personal use, considering the interaction with users, it is planned to add intelligent function design to the fashionable jewelry, and develop composite multi-function intelligent jewelry through sensors identification. By means of IoT technology, while possessing communication function of intelligent jewelry, the function of intelligent jewelry can be expanded to the linkage network. In order to rapidly manage the mass data produced by intelligent jewelry sensors based on IoT, an intelligent jewelry system for health management is designed and an ontology model of intelligent jewelry system based on IoT is worked out. After the state of the services through the smart phone application is shown. The application provides a personalized service to the user and to determine the risk to show the guide lines according to the disease.