• Informatization Policy
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• v.26 no.3
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• pp.90-106
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• 2019

The cloud services market is growing rapidly from the on-premises environment to the cloud computing environment and the domestic cloud software market in Korea is expected to grow at a CAGR of around 15%. In Korea, research teams are providing open cloud platforms using open source software under the government taking the initiative, which intends to enhance the reliability and functionality of open cloud platforms, provide users with a world-class open cloud platform-based and developer-friendly environment that is managed on heterogeneous cloud infrastructure and supported by full-lifecycle management of application software. In this paper, we propose a method to utilize CaaS in the open cloud platform, through incorporating the platform with the container orchestration platform. Finally, by providing users with the application runtime and container runtime, it presents how the two platforms can coexist and cooperate in the same ecosystem.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.97-111
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• 2019

This study discuss how the meaning and role of the state in the process of globalization are defined by the media and how the regulation is related to the ideology and value inherent in the process of globalization. Specifically, we tried to examine how the meaning and role of neoliberal state, which characterizes globalization process, is justified and reinforced by media. The purpose of this study is to understand the characteristics of the dominant globalization process inherent in the discourse of national competitiveness by analyzing how the national competitiveness index report released by the World Economic Forum is reported by the media every year. In addition, we sought to understand the significance and role of the state in the globalization process by examining what areas are emphasized and excluded from the national competitiveness index composed of economic infrastructure, economic efficiency, and enterprise innovation activities.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.241-247
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• 2021

As a way to solve the side effects of urban development, a smart city with information and communication technology converges in the city is being built. For this, a smart city platform should support the development and integration of smart city services. Therefore, the underlying technology and the functional and non-functional requirements that the smart platform must support were analyzed. As a result of this, we classified the Internet of Things, cloud computing, big data and cyber-physical systems into four categories as the underlying technologies supported by the smart city platform, and derived the functional and non-functional requirements that can be implemented and the reference model of the smart city platform. The reference model of the smart city platform is used for decision-making on investment in infrastructure technology and the development scope of services according to functional or non-functional requirements to solve specific city problems for city managers. It provides platform developers with guidelines to identify and determine the functional and non-functional requirements and implementation technologies of software platforms for building smart cities.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.133-139
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• 2020

Big data, artificial intelligence (AI), and machine learning are keywords that represent the Fourth industrial Revolution. In addition, as the development of science and technology, the Korean government, public institutions and industries want professionals who can collect, analyze, utilize and predict data. This means that data analysis and utilization education become more important. Education on data analysis and utilization is increasing with trends in other academy. However, it is true that not many academy run long-term and systematic education. Korea Institute of Science and Technology Information (KISTI) is a data ecosystem hub and one of its performance missions has been providing data utilization and analysis education to meet the needs of industries, institutions and governments since 1966. In this study, KISTI's data education was analyzed using the number of curriculum trainees per year from 2001 to 2019. With this data, the change of interest in education in information and data field was analyzed by reflecting social and historical situations. And we identified the characteristics of KISTI and trainees. It means that the identity, characteristics, infrastructure, and resources of the institution have a greater impact on the trainees' interest of data-use education.In particular, KISTI, as a research institute, conducts research in various fields, including bio, weather, traffic, disaster and so on. And it has various research data in science and technology field. The purpose of this study can provide direction forthe establishment of new curriculum using data that can represent KISTI's strengths and identity. One of the conclusions of this paper would be KISTI's greatest advantages if it could be used in education to analyze and visualize many research data. Finally, through this study, it can expect that KISTI will be able to present a new direction for designing data curricula with quality education that can fulfill its role and responsibilities and highlight its strengths.

• Smart Media Journal
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• v.10 no.1
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• pp.99-107
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• 2021

In addition to the 4th Industrial Revolution and Industry 4.0, the recent megatrends in the ICT field are Big-data, IoT, Cloud Computing, and Artificial Intelligence. Therefore, rapid digital transformation according to the convergence of various industrial areas and ICT fields is an ongoing trend that is due to the development of technology of AI services suitable for the era of the 4th industrial revolution and the development of subdivided technologies such as (Business Intelligence), IA (Intelligent Analytics, BI + AI), AIoT (Artificial Intelligence of Things), AIOPS (Artificial Intelligence for IT Operations), and RPA 2.0 (Robotic Process Automation + AI). This study aims to integrate and advance various machine learning services of infrastructure-side GPU, CDA (Connected Data Architecture) framework, and AI based on mass distributed Abyss storage in accordance with these technical situations. Also, we want to utilize AI business revenue model in various industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3950-3969
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• 2021

Radio Frequency (RF)-based indoor localization technologies play significant roles in various Internet of Things (IoT) services (e.g., location-based service). Most such technologies require that all the devices comply with a specified technology (e.g., WiFi, ZigBee, and Bluetooth). However, this requirement limits its application scenarios in today's IoT context where multiple devices complied with different standards coexist in a shared environment. To bridge the gap, in this paper, we propose a cross-technology localization approach, which is able to localize target nodes using a different type of devices. Specifically, the proposed framework reuses the existing WiFi infrastructure without introducing additional cost to localize Non-WiFi device (i.e., ZigBee). The key idea is to leverage the interference between devices that share the same operating frequency (e.g., 2.4GHz). Such interference exhibits unique patterns that depend on the target device's location, thus it can be leveraged for cross-technology localization. The proposed framework uses Principal Components Analysis (PCA) to extract salient features of the received WiFi signals, and leverages Dynamic Time Warping (DTW), Gradient Boosting Regression Tree (GBRT) to improve the robustness of our system. We conduct experiments in real scenario and investigate the impact of different factors. Experimental results show that the average localization accuracy of our prototype can reach 1.54m, which demonstrates a promising direction of building cross-technology technologies to fulfill the needs of modern IoT context.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.87-97
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• 2023

Charger infrastructure facilities are designed and installed based on a constant power supply. Initially designed charging facilities support charging of rapidly growing electric vehicles on a limited power supply basis. In addition, current commercial vehicles can only be fully charged, and are supported by the rapid equalization charging method. However, commercial vehicles operate according to a set schedule, so flexible charging is essential. In this paper, we propose a power operation method with more than 20% efficiency improvement by using a fixed schedule-based charging scheduling and power distribution technique of a commercial bus based on the same amount of power in accordance with the rapid growth and increase of electric vehicles.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Convergence Security Journal
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• v.23 no.5
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• pp.21-27
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• 2023

In the cloud computing environment, servers and applications can be set up within minutes, and recovery in case of fail ures has also become easier. Particularly, using virtual servers in the cloud is not only convenient but also cost-effective compared to the traditional approach of setting up physical servers just for temporary services. However, most of the und erlying networks and security systems that serve as the foundation for such servers and applications are primarily hardwa re-based, posing challenges when it comes to implementing cloud virtualization. Even within the cloud, there is a growing need for virtualization-based security and protection measures for elements like networks and security infrastructure. This paper discusses research on enhancing the security of cloud networks using network virtualization technology. I configured a secure network by leveraging virtualization technology, creating virtual servers and networks to provide various security benefits. Link virtualization and router virtualization were implemented to enhance security, utilizing the capabilities of virt ualization technology. The application of virtual firewall functionality to the configured network allowed for the isolation of the network. It is expected that based on these results, there will be a contribution towards overcoming security vulnerabil ities in the virtualized environment and proposing a management strategy for establishing a secure network.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.157-167
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• 2023

The use of big data for transportation often involves using data that includes personal information, such as the driver's driving routes and coordinates. This study explores the creation of a route choice prediction model using a large dataset from mobile navigation apps using federated learning. This privacy-focused method used distributed computing and individual device usage. This study established preprocessing and analysis methods for driver data that can be used in route choice modeling and compared the performance and characteristics of widely used learning methods with federated learning methods. The performance of the model through federated learning did not show significantly superior results compared to previous models, but there was no substantial difference in the prediction accuracy. In conclusion, federated learning-based prediction models can be utilized appropriately in areas sensitive to privacy without requiring relatively high predictive accuracy, such as a driver's preferred route choice.