• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.4
• /
• pp.705-717
• /
• 2020

Cities in the world are rushing to develop smart cities to create a sustainable and happy city by solving many problems in cities using information and communication technologies such as big data and IoT. However, in Korea's smart cities and smart city certification systems, the focus is on platform-oriented hardware infrastructure, and the information security aspect is first considered to build and authenticate. It is a situation in which a response system for the risk of leakage of big data containing personal information is needed through policy research on the aspect of personal information protection for smart city operation. This paper analyzes the types of personal information in smart cities, problems associated with the construction and operation of smart cities, and the limitations of the current smart city law and personal information protection management system. As a solution, I would like to present a model of a personal information protection management system in the smart city field and propose a plan to strengthen personal information protection through this. Since the management system model of this paper is applied and operated in the national smart city pilot cities, demonstration cities, and CCTV integrated control centers, it is expected that citizens' personal information can be safely managed.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.6
• /
• pp.77-83
• /
• 2020

The heat treatment facility is in a situation where the scope of application of the remote IOT system is expanding due to the harsh environment caused by high heat and long working hours among the root industries. In this heat treatment process environment, the IOT middleware is required to play a pivotal role in interpreting, managing and controlling data information of IoT devices (sensors, etc.). Until now, the system controlled by the heat treatment remotely was operated with the command of the operator's batch system without overall monitoring of the site situation. However, for the safety and precise control of the heat treatment facility, it is necessary to control various sensors and recognize the surrounding work environment. As a solution to this, the heat treatment safety support system presented in this paper proposes a support system that can detect the access of the work manpower to the heat treatment furnace through thermal image detection and operate safely when ordering work from a remote location. In addition, an OPEN CV-based deterioration analysis system using DNN deep learning network was constructed for faster and more accurate recognition than general fixed hot spot monitoring-based thermal image analysis. Through this, we would like to propose a system that can be used universally in the heat treatment environment and support the safety management specialized in the heat treatment industry.

• Journal of Biomedical Engineering Research
• /
• v.44 no.6
• /
• pp.363-376
• /
• 2023

According to the COVID-19, development of various medical software based on IoT(Internet of Things) was accelerated. Especially, interest in a central software system that can remotely monitor and control ventilators is increasing to solve problems related to the continuous increase in severe COVID-19 patients. Since medical device software is closely related to human life, this study aims to develop central monitoring system that can remotely monitor and control multiple ventilators in compliance with medical device software development standards and to verify performance of system. In addition, to ensure the safety and reliability of this central monitoring system, this study also specifies risk management requirements that can identify hazardous situations and evaluate potential hazards and confirms the implementation of cybersecurity to protect against potential cyber threats, which can have serious consequences for patient safety. As a result, we obtained medical device software manufacturing certificates from MFDS(Ministry of Food and Drug Safety) through technical documents about performance verification, risk management and cybersecurity application.The purpose of this study is to conduct a usability assessment to ensure that ergonomic design has been applied so that the ventilator central monitoring system can improve user satisfaction, efficiency, and safety. The rapid spread of COVID-19, which began in 2019, caused significant damage global medical system. In this situation, the need for a system to monitor multiple patients with ventilators was highlighted as a solution for various problems. Since medical device software is closely related to human life, ensuring their safety and satisfaction is important before their actual deployment in the field. In this study, a total of 21 participants consisting of respiratory staffs conducted usability test according to the use scenarios in the simulated use environment. Nine use scenarios were conducted to derive an average task success rate and opinions on user interface were collected through five-point Likert scale satisfaction evaluation and questionnaire. Participants conducted a total of nine use scenario tasks with an average success rate of 93% and five-point Likert scale satisfaction survey showed a high satisfaction result of 4.7 points on average. Users evaluated that the device would be useful for effectively managing multiple patients with ventilators. However, improvements are required for interfaces associated with task that do not exceed the threshold for task success rate. In addition, even medical devices with sufficient safety and efficiency cannot guarantee absolute safety, so it is suggested to continuously evaluate user feedback even after introducing them to the actual site.

• Journal of Internet Computing and Services
• /
• v.15 no.3
• /
• pp.45-52
• /
• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.