• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.348-351
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• 2016

Recently on IoT environment, there is necessary of protected network, which is only specific user can access it. Applying OAuth protocol on IoT, it can be easier to construct network authentication system, but it is hard to construct protected network authentication system. And there is weakness of OAuth protocol, which is easily attacked by sniffing Token by attacker. So, it is necessary to secondary authentication for OAuth. In ultimate IoT, the fog computing is essential. Fog computing is extension of cloud that enables networking not only in core system but also in edge system and communication node to node. Strength of fog computing is location awareness, support for mobility, and so on. If authentication in fog computing uses this strength, it can be more specialized in Fog Computing. So, in secondary Authentication, using Cyber-Physical-Social System will increase convenience of user than using existing authentication system, such as authentication certificate, id/password and group key, which is inconvenient for user. This study is about authentication based Cyber-Physical-Social System.

• Smart Media Journal
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• v.8 no.1
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• pp.35-42
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• 2019

With IoT device technology developments, such devices now can perceive the surrounding environment and operate upon the condition, but a method for efficiently processing an enormous amount of IoT device data is required. The existing cloud computing has a transmission delay problem due to load and distance. Fog Computing, an environment to control IoT devices, therefore, emerged to solve this problem. In Fog Computing, IoT devices are located close to each other to solve the shortcomings of the cloud system. While many earlier studies on Fog Computing for IoT mainly focus on its structure and framework, we would like to propose an integrated Fog Computing platform that monitors, analyzes, and controls IoT devices.

• Smart Media Journal
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• v.12 no.7
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• pp.27-42
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• 2023

The development of cloud services and IoT technology has radically changed the cloud environment, and has evolved into a new concept called fog computing and F2C (fog-to-cloud). However, as heterogeneous cloud/fog layers are integrated, problems of access control and security management for end users and edge devices may occur. In this paper, an F2C-based IoT smart health monitoring system architecture was designed to operate a medical information service that can quickly respond to medical emergencies. In addition, a role-based service access control technology was proposed to enhance the security of user's personal health information and sensor information during service interoperability. Through simulation, it was shown that role-based access control is achieved by sharing role registration and user role token issuance information through blockchain. End users can receive services from the device with the fastest response time, and by performing service access control according to roles, direct access to data can be minimized and security for personal information can be enhanced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.257-267
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• 2017

With the development of IoT (Internet of Things) technology, the application area has been diversified and the number of users using this service also has increased greatly. Real time big data generated by many IoT devices is no longer suitable for processing in a cloud computing environment. To solve this issue, fog computing is suggested which minimizes response time and makes real time processing suitable. However, security requirement for new paradigm called fog computing is not established until now. In this paper, we define models for fog computing, and the security requirements for the defined model.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4545-4566
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• 2021

The evolution of IoT technology is having a significant impact on people's lives. Almost all areas of people's lives are benefiting from increased productivity and simplification made possible by this trending technology. On the downside, however, the application of IoT technology is posing some security challenges, among them, unauthorized access to IoT devices. This paper presents an Attribute-based Access Control Fog architecture that aims to achieve effective distribution, increase availability and decrease latency. In the proposed architecture, the main functional points of the Attribute-based Access Control are distributed to provide policy decision and policy information mechanisms in fog nodes, locating these functions near end nodes. To evaluate the proposed architecture, an access control engine based on the Attribute-based Access Control was built using the Balana library and simulated using EdgeCloudSim to compare it to the traditional cloud-based architecture. The experiments show that the fog-based architecture provides robust results in terms of reducing latency in making access decisions.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.197-205
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• 2022

The Cyber Physical System(CPS) is an important concept in achieving SMSs(Smart Manufacturing Systems). Generally, CPS consists of physical and virtual elements. The former involves manufacturing devices in the field space, whereas the latter includes the technologies such as network, data collection and analysis, security, and monitoring and control technologies in the cyber space. Currently, all these elements are being integrated for achieving SMSs in which we can control and analyze various kinds of producing and diagnostic issues in the cyber space without the need for human intervention. In this study, we focus on implementing a production equipment monitoring system related to building a SMS. First, we describe the development of a fog-based gateway system that links physical manufacturing devices with virtual elements. This system also interacts with the cloud server in a multimedia network environment. Second, we explain the proposed network infrastructure to implement a monitoring system operating on a cloud server. Then, we discuss our monitoring applications, and explain the experience of how to apply the ML(Machine Learning) method for predictive diagnostics.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.339-351
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• 2016

For the safety of sea, it is important to monitor sea fog, one of the dangerous meteorological phenomena which cause marine accidents. To detect and monitor sea fog, Moderate Resolution Imaging Spectroradiometer (MODIS) data which is capable to provide spatial distribution of sea fog has been used. The previous automatic sea fog detection algorithms were focused on detecting sea fog using Terra/MODIS only. The improved algorithm is based on the sea fog detection algorithm by Wu and Li (2014) and it is applicable to both Terra and Aqua MODIS data. We have focused on detecting spring season sea fog events in the Yellow Sea. The algorithm includes application of cloud mask product, the Normalized Difference Snow Index (NDSI), the STandard Deviation test using infrared channel ($STD_{IR}$) with various window size, Temperature Difference Index(TDI) in the algorithm (BTCT - SST) and Normalized Water Vapor Index (NWVI). Through the calculation of the Hanssen-Kuiper Skill Score (KSS) using sea fog manual detection result, we derived more suitable threshold for each index. The adjusted threshold is expected to bring higher accuracy of sea fog detection for spring season daytime sea fog detection using MODIS in the Yellow Sea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1141-1163
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• 2019

In an effort to minimize operational expenses and supply users with more scalable services, distributed applications are actually going towards the Cloud. These applications, sent out over multiple environments and machines, are composed by inter-connecting independently developed services and components. The implementation of such programs on the Cloud is difficult and generally carried out either by hand or perhaps by composing personalized scripts. This is extremely error prone plus it has been found that misconfiguration may be the root of huge mistakes. We introduce AutoBot, a flexible platform for modeling, installing and (re)configuring complex distributed cloud-based applications which evolve dynamically in time. AutoBot includes three modules: A simple and new model describing the configuration properties and interdependencies of components; a dynamic protocol for the deployment and configuration ensuring appropriate resolution of these interdependencies; a runtime system that guarantee the proper configuration of the program on many virtual machines and, if necessary, the reconfiguration of the deployed system. This reduces the manual application deployment process that is monotonous and prone to errors. Some validation experiments were conducted on AutoBot in order to ensure that the proposed system works as expected. We also discuss the opportunity of reusing the platform in the transition of applications from Cloud to Fog computing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.744-747
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• 2017

The Internet of things (IoT) needs to be an event-driven approach for efficient real time response and processing. An IoT gateway is sometimes employed to provide the connection and translation between devices and the cloud. Storing data in the local database, and then forwarding it on the cloud is a task to be relegated to a gateway device In this paper, we propose the design of the IoT gateway with Fog computing for storing data from sensors into a local database. In the procedure of designing storing tasks, we propose to use the interfacing software known as Ardulink MQTT bridge. MQTT is a protocol for sensors to publish data to the clients. When it comes to needing historical data, MQTT connector can push MQTT data into SQL database. We write an MQTT client and based on the message topic insert the values into a SQL Database The design of IoT gateway with Fog computing adds value because it provides processing of the data across multiple devices before it sends to the cloud.