• Journal of Korea Multimedia Society
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• v.20 no.12
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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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• v.18 no.3
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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.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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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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• v.21 no.1
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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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• v.23 no.1
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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.

• 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.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.107-117
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• 2017

Recently, IoT applications are being deployed in Smart TVs, and these IoT applications are using smart TVs as application platforms rather than broadcast platforms. With the advent of Hybrid broadcast technologies, now it becomes available to develop IoT applications which are coupled to the broadcast information. However, the existing IoT services are not suitable for Hybrid broadcast application since they are built on cloud and require various protocol implementations. In this paper, a Fog Computing-based architecture for hybrid broadcast application is proposed. Instead of accessing IoT services from hybrid broadcast app directly, the proposed architecture places Fog Applet Server between them and distribute loads of hybrid broadcast app to the Fog Applet. The proposed architecture is implemented as a service to control IoT device with hybrid application.

• 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.