• Journal of The Korean Association For Science Education
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• v.42 no.2
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• pp.177-184
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• 2022

This article aims to explain the concept and characteristics of the 'Intelligent Science Lab', which is being promoted nationwide in Korea since 2021. The Korean Ministry of Education creates a master plan containing a vision for science education every five years. The most recently announced '4th Master plan for science education (2020-2024)' emphasizes the policy of setting up an 'intelligent science lab' in all elementary and secondary schools as an online and offline space for scientific inquiry using advanced technologies, such as Internet of Things and Augmented and Virtual Reality. The 'Intelligent Science Lab' project is being pursued in two main directions: (1) developing an online platform named 'Intelligent Science Lab-ON' that supports science inquiry classes, and (2) building a science lab space in schools that encourages active student participation while utilizing the online platform. This article presents the key features of the 'Intelligent Science Lab-ON' and the characteristics of intelligent science lab spaces newly built in schools. Furthermore, it introduces inquiry-based science learning programs developed for intelligent science labs. These programs include scientific inquiry activities in which students generate and collect data ('data generation' type), utilize datasets provided by the online platform ('data utilization' type), or utilize open and public data sources ('open data source' type). The Intelligent Science Lab project is expected to not only encourage students to engage in scientific inquiry that solves individual and social problems based on real data, but also contribute to presenting a model of online and offline linked scientific inquiry lessons required in the post-COVID-19 era.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.69-75
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• 2018

The key technology of Industry 4.0, Smart factory is evaluated as the driving force of our economic development hereafter and a lot of researches have been established. Various entities including devices, products and managers exist in smart factory, but roles of these entities may be continuous or variable and can become extinct not long after. Existing methods for access control are not suitable to adapt to the variable environment. If we don't consider certain security level, important industrial data can be the targets of attacks. We need a new access control method satisfying desired level of efficiency and security without excessive system loads. In this paper, we propose a new RBAC-PAC which extend AC defined for PKC to the authority attribute of roles. We distribute PACs for roles through block chain method to provide the efficient access control. We verified that RBAC-PAC is more efficient in the smart factory with large number of entities which need a frequent permission update.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1904-1913
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• 2021

With the proliferation of smart phones and the development of IoT technology, it has become possible to collect personal data for public purposes. However, users are afraid of voluntarily providing their private data due to privacy issues. To remedy this problem, mainly three techniques have been studied: data disturbance, traditional encryption, and homomorphic encryption. In this work, we perform simulations to compare them in terms of accuracy, message length, and computation delay. Experiment results show that the data disturbance method is fast and inaccurate while the traditional encryption method is accurate and slow. Similar to traditional encryption algorithms, the homomorphic encryption algorithm is relatively effective in privacy preserving because it allows computing encrypted data without decryption, but it requires high computation costs as well. However, its main cost, arithmetic operations, can be processed in parallel. Also, data analysis using the homomorphic encryption needs to do decryption only once at any number of data.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.125-138
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• 2016

Currently, there is increasing demand for weather information, however, providing meteorology and climate information is limited. In order to improve them, supporting the meteorology and climate big data platform use and training the meteorology and climate big data specialist who meet the needs of government, public agencies and corporate, are required. Meteorology and climate big data requires high-value usable service in variety fields, and it should be provided personalized service of industry-specific type for the service extension and new content development. To provide personalized service, it is essential to build the collaboration ecosystem at the national level. Building the collaboration ecosystem environment, convergence of marine policy and climate policy, convergence of oceanography and meteorology and convergence of R&D basic research and applied research are required. Since then, demand analysis, production sharing information, unification are able to build the collaboration ecosystem.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.181-192
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• 2017

Internet of Things includes the whole process of collected information generated from a variety of objects, as well as analyzing and sharing it, and providing useful information services to people. This study seeks ways to improve security and safety in the areas of service security technology, ID management technology and service access control, all of which take place in the IoT environment. We have implemented the services that can design and issue C&C (Certificate and Capability) service token authentication, which is based on a public key, to improve the service security. In addition, we suggest LCRS (Left Child-Right Sibling) resource model management for the efficient control of resources when generating the resource services from the data collected from node devices. We also implemented an IoT services platform to manage URL security of the resource services and perform access control for services.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.75-94
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• 2021

The 4th industrial revolution needs a fusion of artificial intelligence, robotics, the Internet of Things (IoT), edge computing, and other technologies. For the fusion of technologies, cloud computing technology can provide flexible and high-performance computing resources so that cloud computing can be the foundation technology of new emerging services. The emerging services become a global-scale, and require much higher performance, availability, and reliability. Public cloud providers already provide global-scale services. However, their services, costs, performance, and policies are different. Enterprises/ developers to come out with a new inter-operable service are experiencing vendor lock-in problems. Therefore, multi-cloud technology that federatively resolves the limitations of single cloud providers is required. We propose a software platform, denoted as Cloud-Barista. Cloud-Barista is a multi-cloud service common platform for federating multiple clouds. It makes multiple cloud services as a single service. We explain the functional architecture of the proposed platform that consists of several frameworks, and then discuss the main design and implementation issues of each framework. To verify the feasibility of our proposal, we show a demonstration which is to create 18 virtual machines on several cloud providers, combine them as a single resource, and manage it.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.221-228
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• 2018

Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.205-212
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• 2018

With the development of high performance CPU / GPU, artificial intelligence algorithms such as deep neural networks, and a large amount of data, machine learning has been extended to various applications. In particular, a large amount of data collected from the Internet of Things, social network services, web pages, and public data is accelerating the use of machine learning. Learning data sets for machine learning exist in various formats according to application fields and data types, and thus it is difficult to effectively process data and apply them to machine learning. Therefore, this paper studied a method for building a learning data set for machine learning in accordance with standardized procedures. This paper first analyzes the requirement of learning data set according to problem types and data types. Based on the analysis, this paper presents the reference model to build learning data set for machine learning applications. This paper presents the target standardization organization and a standard development strategy for building learning data set.

• The Journal of Bigdata
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• v.6 no.1
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• pp.161-178
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• 2021

The importance of 'Big Data' is increasing to the point that it is likened to '21st century crude oil'. For smart city IoT data, attention should be paid to quality control as the quality of data is associated with the quality of public services. However, data quality indicators presented through ISO/IEC organizations and domestic/foreign organizations are limited to the 'User' perspective. To complement these limitations, the study derives supplier-centric indicators and their priorities. After deriving 3 categories and 13 indicators of supplier-oriented smart city IoT data quality evaluation indicators, we derived the priority of indicator categories and data quality indicators through AHP analysis and investigated the feasibility of each indicator. The study can contribute to improving sensor data quality by presenting the basic requirements that data should have to individuals or companies performing the task. Furthermore, data quality control can be performed based on indicator priorities to provide improvements in quality control task efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1257-1263
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• 2018

Secure authentication technology is a fundamental building block for secure services for Internet of Things devices. Particularly, the multiplication operation is a core operation of public key cryptography, such as RSA, ECC, and SIDH. However, modern low-power processor, namely ARM Cortex-M3 processor, is not secure enough for practical usages, since it executes the multiplication operation in variable-time depending on the input length. When the execution is performed in variable-time, the attacker can extract the password from the measured timing. In order to resolve this issue, recent work presented constant-time solution for multiplication operation. However, the implementation still missed various speed-optimization techniques. In this paper, we analyze previous multiplication methods over ARM Cortex-M3 and provide optimized implementations to accelerate the speed-performance further. The proposed method successfully accelerates the execution-time by up-to 25.7% than previous works.