• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.294-296
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• 2017

Cloud computing is a computing technique that uploads all the data from a cloud node to a cloud server and provides it to users as a service. This is difficult to provide services in real time depending on the network conditions. This is because it is necessary to download information to the remote site through the network, not the local area, and to download additional services to provide services in the cloud. So fog computing has been proposed as an alternative. In this paper, we propose an efficient data exchange technique between cloud, fog and user. The proposed fog provides services to users and collects and processes data. The cloud is responsible for the flow of data exchange and control between the fog. We propose a standard method for data exchange. The application for this is to process and service the information generated by the BAN (Body Area Network) in the fog, and the cloud serves as a mediator. This can resolve data heterogeneity between devices or services and provide efficient data movement.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.233-239
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• 2022

Recently, research is being conducted on the rapid service provision and reliability of the instance-based rather than the existing IP-based structure. Research is mainly conducted through Block cloud, a platform that combines service-centric networking (SCN) and blockchain. In addition, the Internet of Things network has been proposed as a fog computing environment in the structure of the existing cloud computing. Fog computing is an environment suitable for real-time information processing. In this paper, we propose a new Internet network structure based on fog computing that requires real-time for rapid processing of IoT services. The proposed system applies IoTA, the third-generation blockchain based on DAG, to the block cloud. In addition, we want to propose a basic model of the object block chain and check the application services of electric vehicles.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.25-31
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• 2023

Fog computing diversifies cloud computing by using edge devices to provide computing, data storage, communication, management, and control services. As it has a decentralised infrastructure that is capable of amalgamating with cloud computing as well as providing real-time data analysis, it is an emerging method of using multidisciplinary domains for a variety of applications; such as the IoT, Big Data, and smart cities. This present study provides an overview of the security and privacy concerns of fog computing. It also examines its fundamentals and architecture as well as the current trends, challenges, and potential methods of overcoming issues in fog computing.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1192-1200
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• 2019

Recently, artificial intelligence techniques have been widely used in the computer science field, such as the Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is of utmost importance for maintaining the quality of services, service-level agreements, and the availability of the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource management based on artificial intelligence. We divide cloud resource management techniques based on artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud computing systems. The aim of the paper is to propose an intelligent resource management scheme that manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of the artificial intelligence techniques. We explore how our proposed resource management scheme can be extended to various cloud-based systems.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.310-328
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• 2022

Internet of things (IoT) has emerged as the most popular technique that facilitates enhancing humans' quality of life. However, most time sensitive IoT applications require quick response time. So, processing these IoT applications in cloud servers may not be effective. Therefore, fog computing has emerged as a promising solution that addresses the problem of managing large data bandwidth requirements of devices and quick response time. This technology has resulted in processing a large amount of data near the data source compared to the cloud. However, efficient management of computing resources involving balancing workload, allocating resources, provisioning resources, and scheduling tasks is one primary consideration for effective computing-based solutions, specifically for time-sensitive applications. This paper provides a comprehensive review of the source management strategies considering resource limitations, heterogeneity, unpredicted traffic in the fog computing environment. It presents recent developments in the resource management field of the fog computing environment. It also presents significant management issues such as resource allocation, resource provisioning, resource scheduling, task offloading, etc. Related studies are compared indifferent mentions to provide promising directions of future research by fellow researchers in the field.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.27-32
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• 2018

IoT technology is developing with various application areas in $4^{th}$ Industrial revolution. There are many users using the application services. Sensing data from various environment need to be transferred to cloud computing storage and store in the cloud storage. However, physical distance from the end node to cloud computing storage is far away, and it is not efficient to transfer data from sensors and store the sensing data in the cloud storage whenever sensing data happen. Therefore, Fog computing is proposed to solve these problems which can process and store the sensing data. However, Fog computing is new emerging technology, there is no standard security model and requirements. This research proposes to security requirements and security model for Fog computing to establish a secure and efficient cloud computing environment.

• 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.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.