• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.349-352
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• 2020

클라우드는 가상화 기술을 사용한 리소스의 유연성과 뛰아난 접근성을 장점으로 빅데이터, 딥러닝 등 여러 분야에서 클라우드를 사용하고 있다. 최근 클라우드와 결합된 IoT 시스템을 통해 시스템 관리, 데이터 처리 및 저장, 데이터를 이용한 빅데이터 활용 등 여러 방법으로 사용 할 수 있어 많은 관심을 받고 있다. 그러나 IoT 클라우드의 많은 활용에 따라 대규모 시스템화, 여러 사용자의 개인정보 저장 등의 이유로 많은 공격자의 표적이 되고있다. 여러 공격자의 공격을 방아하기 위해 IoT 클라우드 시스템은 블록체인, 보안 IoT 디바이스, 변형된 클라우드 모델등 여러 연구가 진행되고 있다. 본 논문에서는 최근 연구되고 있는 블록체인, 클라우드, IoT 시스템의 동향에 대해 조사하고, 기존에 연구되었던 기술을 바탕으로 효과적인 블록체인 기반의 IoT 클라우드 시스템을 제안한다. 제안하는 IoT 클라우드 시스템은 블록체인 기술을 사용하여 보안정책을 관리할 수 있어 신뢰성이 높으며, 클라우드 시스템이 작동하지 않을 경우 페일오버 기능을 수행할 수 있어 가용성이 뛰어나다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.117-126
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• 2019

The data generated in the IoT environment is very diverse. Especially, the development of the fourth industrial revolution has made it possible to increase the number of fixed and unstructured data generated in manufacturing facilities such as Smart Factory. With Big Data related solutions, it is possible to collect, store, process, analyze and visualize various large volumes of data quickly and accurately. Therefore, in this paper, we will directly generate data using Raspberry Pi used in IoT environment, and analyze using various Big Data solutions. Collected by using an Sqoop solution collected and stored in the database to the HDFS, and the process is to process the data by using the solutions available Hive parallel processing is associated with Hadoop. Finally, the analysis and visualization of the processed data via the R programming will be used universally to end verification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.1-9
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• 2018

In the fourth industrial revolution, which has become an issue now, we have been able to receive instant analysis results faster than the existing slow speed through various Big Data technologies, and to conduct real-time monitoring on mobile and web. First, various irregular sensor Data is generated using IoT device, Raspberry Pi. Sensor Data is collected in real time, and the collected data is distributed and stored using several nodes. Then, the stored Sensor Data is processed and refined. Visualize and output the analysis result after analysis. By using these methods, we can train the human resources required for Big Data and mobile related fields using IoT, and process data efficiently and quickly. We also provide information that can confirm the reliability of research results through real time monitoring.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.130-145
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• 2021

As part of the Smart Seoul policy, the importance of using big urban data is being highlighted. Furthermore interest in the impact of transportation-related urban environmental factors such as PM10 and noise on citizen's quality of life is steadily increasing. This study established the integrated DB by matching IoT big data with transportation data, including traffic volume and speed in the microscopic Spatio-temporal scope. This data analyzed the impact of a spatial unit in the road-effect zone on environmental risk level. In addition, spatial units with similar characteristics of road traffic and environmental factors were clustered. The results of this study can provide the basis for systematically establishing environmental risk management of urban spatial units such as PM10 or PM2.5 hot-spot and noise hot-spot.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1085-1092
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• 2018

Recently, Internet (IoT) and big data have been utilized in various fields such as medical, military and sports. Korea Nazarene University is a rehabilitation-welfare university with about 300 students with disabilities. This paper proposes a smart campus that provides the optimal path for the calculation of the route and risk avoidance using the BLE beacon and the 3-axis acceleration sensor when the students with disabilities move in the campus both indoors and outdoors. So we can manage the big data and sensor-based IoT technology for students with disabilities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.547-548
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• 2018

SCM(Supply Chain Management)은 초기에 생산의 효율성 증대를 위해 생산 계획에 초점을 둔 제품으로 발전하였으나, 최근에는 수요자 중심으로의 비즈니스 패러다임 변화, 프로세스 간 상호연계 강화, 수요의 다양성 및 변동성 증가 등의 환경이 변화하면서 지능적인 SCM이 요구되고 있다. 지능적인 SCM은 IoT, 빅데이터, 인공지능 등의 기술을 활용하여 공급 사슬 전체에 대한 자동화, 자율화, 연결성을 보장하는 것을 강조하고 있다. 따라서 본 논문에서는 급변하는 비즈니스 환경변화에 대응하여 공급 사슬 최적화를 달성하기 위해 IoT와 빅데이터 기반의 SCM 시스템에 관하여 연구하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.215-216
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• 2018

분산 컴퓨팅 환경에서 사용되어지는 빅 데이터 파일 시스템은 IoT(Internet of Things) 노드에서 처리해야할 데이터 탐색 시 모든 저장장치를 탐색하기 때문에 속도가 느리며 트래픽으로 인한 오버헤드가 발생할 수 있다. 본 연구에서는 IoT 노드의 분산 컴퓨팅 환경에서 빅 데이터를 좀 더 효율적으로 처리하고 빠른 검색을 위해 머신 러닝 기법을 이용한 분산 프레임워크를 제안하며 IoT 노드에서의 데이터 처리를 위해 다른 저장 장치로의 불필요한 액세스를 사전에 방지하여 빠르고 정확한 연산 결과를 도출하여 효율성을 향상 시키고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.548-551
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• 2018

Most vehicles are in the form of maintenance when a problem occurs by the user himself or herself. During maintenance, users are not able to operate the car while it is being serviced, and if the target vehicle is a revenue-generating vehicle, they will have to bear economic losses. Collecting vehicle information in real time, identifying problems that could arise with a vehicle based on the collected big data and providing advance service rather than after-sales service can help secure vehicle operation and reduce economic loss. Thus, in this thesis, a platform was designed to design IoT-based gateways, collect real-time vehicle information, and organize big data to provide vehicle information in real time.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.177-182
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• 2017

As major ICT technologies such as IoT, cloud computing, and Big Data are being applied to manufacturing, smart factories are beginning to be built. The key of smart factory implementation is the ability to acquire and analyze data of the factory. Therefore, the need for a big data analysis platform is increasing. The purpose of this study is to construct a platform for big data analysis of manufacturing process and propose integrated method for analysis. The proposed platform is a RHadoop-based structure that integrates analysis tool R and Hadoop to distribute a large amount of datasets. It can store and analyze big data collected in the unit process and factory in the automation system directly in HBase, and it has overcome the limitations of RDB - based analysis. Such a platform should be developed in consideration of the unit process suitability for smart factories, and it is expected to be a guide to building IoT platforms for SMEs that intend to introduce smart factories into the manufacturing process.

• Air Cleaning Technology
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• v.30 no.1
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• pp.40-56
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• 2017