• 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 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.

• 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.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1002-1015
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• 2018

The advent of Internet of Things (IoT) and the evident inadequacy of Cloud networks concerning management of numerous end nodes have brought about a shift of paradigm giving birth to Fog computing. Fog computing is an extension of Cloud computing that extends Cloud resources at the edge of the network, closer to the user. Cloud computing has become one of the essential needs of people over the Internet but with the emerging concept of IoT, traditional Clouds seem inadequate. IoT entails extremely low latency and for that, the Cloud servers that are distant and unknown to the user appear to be unsuitable. With the help of Fog computing, the Fog devices installed would be closer to the user that will provide an immediate storage for the frequently needed data. This paper discusses data migration between different storage types especially between Cloud devices and then presents a mechanism to migrate data between Cloud and Fog Layer. We call this mechanism Adaptive Deadline-Aware Scheme (ADAS) for Data migration between Cloud and Fog. We will demonstrate that we can access and process latency sensitive "hot" data through the proposed ADAS more efficiently than with a traditional Cloud setup.

• Journal of Environmental Science International
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• v.25 no.1
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• pp.163-171
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• 2016

We analyzed the characteristics of fog formation in the Gumi Reservoir of Nakdong river with the field observation data for recent 2 years (1 April 2013~31 March 2015) collected by the national institute of meteorological research, KMA. In early morning, we frequently observe the steam rising from the water surface. The fog occurs from adding water vapor into the air. We call the fog as steam fog. Steam fogs occur when cold, dry air mixes with warm, moist air above a water surface. The steam fog appears mainly in autumn under the following conditions; (1) sensible heat is positive values ($10{\sim}20W/m^2$), (2) latent heat is more positive values ($25{\sim}35W/m^2$) than sensible heat, (3) cloudless nights with light winds (about 1.5 m/s), (4) under condition(3), mountainous winds easily blows into the reservoir.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1841-1846
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• 2000

To examine the effect of acid fog on the corrosion fatigue behavior in structural steel, fatigue tests under acid fog atmosphere were carried out in comparison with distilled water. The corrosive c omponents contained in acid fog pile up the corrosion products on crack face and show a crack branching and crack tip blunting. Therefore, due to these workings crack growth rate was reduced by decreasing the effective stress range in crack tip rather than under distilled water. Also the effect of sulfuric acid, which is the main component of acid fog, and testing speed on fatigue crack growth were examined. It was found that corrosion behavior was remarkably dependent upon pH and Hz rather than components of acid fog. According as pH and testing speed decrease below a specific value, crack growth was accelerated in comparison with distilled water. This reveals that due to liquid having strong acidity and slow speed of test the crack face dissolution was promoted, so crack closure was disturbed in the process of stress descent.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.814-817
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• 2013

In this paper, we propose a visibility enhancement method using dehazing method in fog situation. The proposed method calculate low bound of the transmission rate that indicate fog rate and transmission that processed power operation in each pixel by the user's control. And we obtain the dehazed image using calculated transmission rate. Proposed method is possible real-time processing, because the method don't cause halo effect and drop operations from filtering by closed form. We can obtain the dehazed image in various fog conditions by user control that strength of removing fog can be adjusted according to the dgree of fog.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.1
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• pp.68-73
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• 2013

This study is to make a protype of forecast guidance for forecasters from analyzing the characteristics of Korea Fog. The trend of Korea fog showed the decline in the number of foggy days and the duration time, the gradient is -1.24days/year under 3 miles and -0.98days/year under 1 mile and -1.64hours/year under 3 miles and -3.18hours/year under 1 mile in duration time in 27 ROKAF base. To find the protype of inland and coastal forecast guidance, Daegu base as a representation of the inland base and Gangneung base as the representation of the coastal base were chosen. For Daegu base, the mixture of relative humidity, sky condition, and the position of high pressure were selected for the forecast guidance. For Gangneung base, pressure pattern, sea surface temperature, sea currents, and 850hPa temperature patterns were selected for the forecast guidance.

• Journal of ILASS-Korea
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• v.22 no.1
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• pp.13-21
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• 2017

The fog screen is a device projecting the media to the aerosol flow field. As major parameters to generate dense and steady fog screen, shear stress, optical blockage ratio and SMD were obtained result through experiment. The micro droplet was generated by the piezo oscillation element, and the aerosol flow mixed with an air flow was sprayed into the vertical direction from the top of the fog screen through the 280 mm slot. For produce a dense, uniform fog screen, the shear effect, optical blockage ratio and SMD between aerosol and air curtain were measured. The minimum and maximum shear stress conditions were selected and it was confirmed that the optical transmission deviation of the aerosol flow field was small when the aerosol and air curtain flow rates were changed. When the aerosol and air curtain flow power were 18 V (1.51 m/s) and 24 V (2.55 m/s), respectively, under the condition of the minimum shear stress and laminar flow, the optical blockage ratios with the spray length were small, and it produced a most stable and high density uniform fog screen by injecting a constant of $10{\mu}m$ or less.