• 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.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.89-92
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• 2020

According to the various IoT(Internet of Things) services, there have been lots of task offloading researches for IoT devices. Since there are service response delay and core network load issues in conventional cloud computing based offloadings, fog computing based offloading has been focused whose location is close to the IoT devices. However, even in the fog computing architecture, the load can be concentrated on the for computing node when the number of requests increase. To solve this problem, the opportunistic fog computing concept which offloads task to available computing resources such as cars and drones is introduced. In previous fog and opportunistic fog node researches, the offloading is performed immediately whenever the service request occurs. This means that the service requests can be offloaded to the opportunistic fog nodes only while they are available. However, if the service response delay requirement is satisfied, there is no need to offload the request immediately. In addition, the load can be distributed by making the best use of the opportunistic fog nodes. Therefore, this paper proposes a delayed offloading scheme to satisfy the response delay requirements and offload the request to the opportunistic fog nodes as efficiently as possible.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.250-255
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• 2020

For the resolution of the latency problem of the cloud center-based cloud computing, fog computing was proposed that allows end devices to offload computations to nearby fog nodes. In the fog computing, virtualized service images are placed on fog nodes and, if service images are placed close to end devices, the duplicate service image placement problem may occur. Therefore, in this paper, we propose a service image placement mechanism based on the logical fog network that reduces duplicate service images by considering the pattern of collected service requests. For the performance evaluation of the proposed mechanism, through simulations, we compare ours with the on-demand mechanism placing a service image upon the receipt of a service request. We consider the performance factors like the number of service images, the number of non-accommodated service requests, and the network cost.

• Smart Media Journal
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• v.11 no.3
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• pp.39-53
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• 2022

Cloud of Things (CoT) can provide IoT applications with unlimited storage functions and processing power supported by cloud services. However, in a centralized cloud of things, it can create a single point of failure that can lead to bottleneck problems, outages of the CoT network. In this paper, to solve the problem of centralized cloud of things and interoperate between different service domains, we propose an IoT service interoperability system using distributed fog computing and blockchain technology. Distributed fog is used to provide real-time data processing and services in fog systems located at a geographically close distance to IoT devices, and to enable service interoperability between each fog using smart contracts and distributed ledgers of the blockchain. The proposed system provides services within a region close to the distributed fog entrusted with the service from the cloud, and it is possible to access the services of other fogs without going through the cloud even between fogs. In addition, by sharing a service right token issuance information between the cloud and fog nodes using a blockchain network, the integrity of the token can be guaranteed and reliable service interoperability between fog nodes can be performed.

• 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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• 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.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.37-47
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• 2018

The number of capabilities of Internet of Things (IoT) devices will exponentially grow over the next years. These devices may generate a vast amount of time-constrained data. In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. In addition, most of IoT services will be content-centric rather than host centric to increase the data availability and the efficiency of data delivery. IoT will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. Also, fog computing keeps data and computation close to end users at the edge of network, and thus provides a new breed of applications and services to end users with low latency, high bandwidth, and geographically distributed. In this paper, we propose Edge-Fog cloud-based Hierarchical Data Delivery ($EFcHD^2$) method that effectively and reliably delivers IoT data to associated with IoT applications with ensuring time sensitivity. The proposed $EFcHD^2$ method stands on basis of fully decentralized hybrid of Edge and Fog compute cloud model, Edge-Fog cloud, and uses information-centric networking and bloom filters. In addition, it stores the replica of IoT data or the pre-processed feature data by edge node in the appropriate locations of Edge-Fog cloud considering the characteristic of IoT data: locality, size, time sensitivity and popularity. Then, the performance of $EFcHD^2$ method is evaluated through an analytical model, and is compared to fog server-based and Content-Centric Networking (CCN)-based data delivery methods.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.51-57
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• 2021

In this paper, we propose a task distribution scheme in fog computing environment considering opportunistic fog computing nodes. As latency is one of the important performance metric for IoT(Internet of Things) applications, there have been lots of researches on the fog computing system. However, since the load can be concentrated to the specific fog computing nodes due to the spatial and temporal IoT characteristics, the load distribution should be considered to prevent the performance degradation. Therefore, this paper proposes a task distribution scheme which considers the static as well as opportunistic fog computing nodes according to their mobility feature. Especially, based on the task requirements, the proposed scheme supports the delay sensitive task processing at the static fog node and delay in-sensitive tasks by means of the opportunistic fog nodes for the task distribution. Based on the performance evaluation, the proposed scheme shows low service response time compared to the conventional schemes.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.789-795
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• 2021

In order to provide useful Internet of Things (IoT) services, the locations of IoT devices should be well managed. However, frequent location updates of lots of IoT devices result in signaling overhead in networks. To solve this problem, this paper utilizes the opportunistic fog node (OFN) which is opportunistically available according to the mobility to perform the location updates as a representative of IoT devices. Therefore, the location updates through OFN can reduce the signaling loads of networks. To show the performance of the proposed scheme, we develop an analytic model for the opportunistic location update offloading probability that the location update can be offloaded to OFN from the IoT device. Then, the extensive simulation results are given to validate the analytic model and to assess the performance of the proposed scheme in terms of the opportunistic location update offloading probability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.731-751
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• 2021

The Internet of Things is a huge group of devices communicating each other and the interconnection of objects in the network is a basic requirement. Choosing a reliable device is critical because malicious devices can compromise networks and services. However, it is difficult to create a trust management model due to the mobility and resource constraints of IoT devices. For the centralized approach, there are issues of single point of failure and resource expansion and for the distributed approach, it allows to expand network without additional equipment by interconnecting each other, but it has limitations in data exchange and storage with limited resources and is difficult to ensure consistency. Recently, trust management models using fog nodes and blockchain have been proposed. However, blockchain has problems of low throughput and delay. Therefore, in this paper, a trust management model for selecting reliable devices in a fog-based IoT environment is proposed by applying IOTA, a blockchain technology for the Internet of Things. In this model, Directed Acyclic Graph-based ledger structure manages trust data without falsification and improves the low throughput and scalability problems of blockchain.