• 한국멀티미디어학회논문지
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• 제20권12호
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• pp.1928-1939
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• 2017

Fog computing is another paradigm of the cloud computing, which extends the ubiquitous services to applications on many connected devices in the IoT (Internet of Things). In general, if we access a lot of IoT devices with existing cloud, we waste a huge amount of bandwidth and work efficiency becomes low. So we apply the paradigm called fog between IoT devices and cloud. The network architecture based on cloud and fog computing discloses the security and privacy issues according to mixed paradigm. There are so many security issues in many aspects. Moreover many IoT devices are connected at fog and they generate much data, therefore light and efficient security mechanism is needed. For example, with inappropriate encryption or authentication algorithm, it causes a huge bandwidth loss. In this paper, we consider issues related with data encryption and authentication mechanism in the network architecture for cloud and fog-based M2M (Machine to Machine) IoT services. This includes trusted encryption and authentication algorithm, and key generation method. The contribution of this paper is to provide efficient security mechanisms for the proposed service architecture. We implemented the envisaged conceptual security check mechanisms and verified their performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.685-703
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• 2024

Cloud computing provides each consumer with a large-scale computing tool. Different Cyber Attacks can potentially target cloud computing systems, as most cloud computing systems offer services to many people who are not known to be trustworthy. Therefore, to protect that Virtual Machine from threats, a cloud computing system must incorporate some security monitoring framework. There is a tradeoff between the security level of the security system and the performance of the system in this scenario. If strong security is needed, then the service of stronger security using more rules or patterns is provided, since it needs much more computing resources. A new way of security system is introduced in this work in cloud environments to the VM on account of resources allocated to customers are ease. The main spike of Fog computing is part of the cloud server's work in the ongoing study tells the step-by-step cloud server to change the tremendous measurement of information because the endeavor apps are relocated to the cloud to keep the framework cost. The cloud server is devouring and changing a huge measure of information step by step to reduce complications. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and Fog layer based on the guard intensity and the key is provoked for ingress the file. The monitoring center sustains the Activity Log, Risk Table, and Health Records. Cloud computing and Fog computing were combined in this paper to review data movement and safe information about MDHC.

• 스마트미디어저널
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• 제8권1호
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• pp.35-42
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• 2019

IoT 디바이스 기술의 발전으로 디바이스가 주변 환경을 인식하고 동작하게 되면서, 막대한 양의 IoT 디바이스 데이터를 효율적으로 처리하기 위한 방안이 요구되고 있다. 기존에 사용되던 클라우드 컴퓨팅은 부하와 거리에 따른 전송 지연 문제가 발생한다. 이러한 문제를 해결하기 위해 포그 컴퓨팅이 등장하였다. 포그 컴퓨팅은 IoT 디바이스를 제어하기 위한 환경으로, 클라우드의 단점을 해결하기 위해 IoT 디바이스를 가까이 두어 근거리 통신을 수행한다. 그러나 IoT를 위한 포그 컴퓨팅 관련 연구들은 포그컴퓨팅의 구조와 프레임워크에 대한 연구가 주를 이룬다. 따라서 본 논문에서는 포그컴퓨팅을 수행하기 위한 플랫폼을 제안한다. 제안하는 플랫폼은 포그 컴퓨팅 환경에서 IoT 디바이스를 모니터링 및 분석, 제어할 수 있는 통합 플랫폼이다.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.163-172
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• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.137-142
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• 2021

The Internet of Things (IoT) paradigm is at the forefront of present and future research activities. The huge amount of sensing data from IoT devices needing to be processed is increasing dramatically in volume, variety, and velocity. In response, cloud computing was involved in handling the challenges of collecting, storing, and processing jobs. The fog computing technology is a model that is used to support cloud computing by implementing pre-processing jobs close to the end-user for realizing low latency, less power consumption in the cloud side, and high scalability. However, it may be that some resources in fog computing networks are not suitable for some kind of jobs, or the number of requests increases outside capacity. So, it is more efficient to decrease sending jobs to the cloud. Hence some other fog resources are idle, and it is better to be federated rather than forwarding them to the cloud server. Obviously, this issue affects the performance of the fog environment when dealing with big data applications or applications that are sensitive to time processing. This research aims to build a fog topology job scheduling (FTJS) to schedule the incoming jobs which are generated from the IoT devices and discover all available fog nodes with their capabilities. Also, the fog topology job placement algorithm is introduced to deploy jobs into appropriate resources in the network effectively. Finally, by comparing our result with the state-of-art first come first serve (FCFS) scheduling technique, the overall execution time is reduced significantly by approximately 20%, the energy consumption in the cloud side is reduced by 18%.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.140-146
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• 2023

Resource allocation is one of the top challenges in Internet of Things (IoT) networks. This is due to the scarcity of computing, energy and communication resources in IoT devices. As a result, IoT devices that are not using efficient algorithms for resource allocation may cause applications to fail and devices to get shut down. Owing to this challenge, this paper proposes a novel algorithm for managing computing resources in IoT network. The fog computing devices are placed near the network edge and IoT devices send their large tasks to them for computing. The goal of the algorithm is to conserve energy of both IoT nodes and the fog nodes such that all tasks are computed within a deadline. A bi-partite graph-based algorithm is proposed for stable matching of tasks and fog node computing units. The output of the algorithm is a stable mapping between the IoT tasks and fog computing units. Simulation results are conducted to evaluate the performance of the proposed algorithm which proves the improvement in terms of energy efficiency and task delay.

• 사물인터넷융복합논문지
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• 제6권4호
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• pp.89-92
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• 2020

다양한 IoT(Internet of Things) 서비스들이 등장하면서 IoT 기기의 테스크를 오프로딩 시키는 연구가 진행되었다. 기존에는 클라우드 컴퓨팅을 통한 오프로딩이 고려되었지만 서비스 응답 지연 및 코어 네트워크의 부하 등의 이슈로 인해 IoT 기기 근처에서 오프로딩을 지원하는 포그 컴퓨팅 개념이 도입되었다. 하지만 포그 컴퓨팅 환경에서도 서비스 대상 IoT 기기가 증가하게 되면 클라우드 환경과 마찬가지로 부하 집중 문제로 인해 서비스 응답 지연이 발생할 수 있다. 이를 해결하기 위하여 자동차, 드론 등 IoT 기기 근처에 존재하는 컴퓨팅 가능 노드들을 통해 오프로딩을 수행하는 개념인 기회적 포그 컴퓨팅이 등장하였다. 기존의 포그 및 기회적 포그 컴퓨팅 노드들을 활용한 오프로딩 연구들은 서비스의 요청이 있을 때 가용한 노드를 통해 오프로딩을 수행한다. 기존의 연구 방법대로 오프로딩을 수행한다면 기회적 포그 컴퓨팅 노드가 가용할 때에 발생된 요청들만 해당 노드들로 오프로딩이 가능하다. 하지만 서비스의 응답 지연 요구사항만 만족시킨다면 즉시적으로 요청을 처리할 필요가 없고 최대한 많은 테스크를 기회적 포그 컴퓨팅 노드로 오프로딩 시키는 것이 부하 분산에 용이하다. 그러므로 본 논문에서는 오프로딩 타이머를 기반으로 서비스 응답 지연 요구사항을 만족시키면서 최대한 기회적 포그 컴퓨팅 노드들을 통해 오프로딩 시킬 수 있는 지연된 오프로딩 방법을 제안하고자 한다.

• 방송공학회논문지
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• 제22권1호
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• pp.107-117
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• 2017

기존의 방송 단말에서 제공되는 IoT 서비스는 방송과 연계되지 않은 독립형 서비스의 형태로 제공되고 있었으나 최근 하이브리드 방송 관련 기술의 확산으로 방송과 IoT가 유기적으로 연계된 다양한 서비스로의 발전이 기대되고 있다. 하지만 현행 IoT 서비스는 다양한 프로토콜이 혼재된 클라우드 형태로 구성되어 임베디드 어플리케이션인 하이브리드 방송 단말에서의 접근에 많은 제약을 가지고 있다. 이러한 문제를 해결하기 위해, 본 논문에서는 Fog Computing의 개념을 어플리케이션으로 확장한 하이브리드 방송용 Fog Applet 아키텍쳐를 제안한다. Fog Applet 아키텍쳐는 클라우드 기반 IoT 서비스와 방송 단말 어플리케이션 사이에 Fog Applet을 위치시킴으로써 임베디드 어플리케이션의 서비스 접근 요구를 감소시키고 다양한 클라우드 기반 IoT 서비스와 유연한 구성을 제공하는 목적을 가진다. 제안된 아키텍쳐는 하이브리드 방송 기반의 서비스 환경에 대한 구현을 통하여 다종 IoT 서비스의 연동을 지원하는 하이브리드 어플리케이션의 구현을 통하여 그 기능을 검증한다.

• 한국통신학회논문지
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• 제42권1호
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• pp.257-267
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• 2017

IoT(사물인터넷) 기술이 발전하여 적용 분야가 다양해지고 이에 따라 서비스를 이용하는 사용자 수도 크게 증가하였다. 수많은 IoT 디바이스들에 의해 발생되는 실시간 대용량 데이터를 클라우드 컴퓨팅 환경에서 처리하는 것은 더 이상 적합하지 않다. 이러한 문제를 해결하기 위해서 응답시간을 최소화 하고 실시간 처리가 적합하도록 하는 포그 컴퓨팅이 제안되었다. 하지만 포그 컴퓨팅이라는 새로운 패러다임에 대한 보안 요구사항이 아직 정립되지 않았다. 이 논문에서는 포그 컴퓨팅에 대한 모델 정의와 정의된 모델에 대한 보안 요구사항을 정리하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권1호
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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.