• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1243-1257
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• 2019

With the emergence of the IoT environment, various smart devices provide consumers with the convenience and various services. However, as security threats such as invasion of privacy have been reported, the importance of security issues in the IoT environment has emerged, and in particular, the security problem of key management has been discussed, and the PUF has been discussed as a countermeasure. In relation to the key management problem, a protocol using MIPUF has been proposed for the security problem of the group key management system. The system can be applied to lightweight IoT environments and the safety of the PUF ensures the safety of the entire system. However, in some processes, it shows vulnerabilities in terms of safety and efficiency of operation. This paper improves the existing protocol by adding authentication for members, ensuring data independence, reducing unnecessary operations, and increasing the efficiency of database searches. Safety analysis is performed for a specific attack and efficiency analysis results are presented by comparing the computational quantities. Through this, this paper shows that the reliability of data can be improved and our proposed method is lighter than existing protocol.

• Journal of Korean Library and Information Science Society
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• v.52 no.3
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• pp.177-196
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• 2021

The purpose of this study is to analyze priorities of competencies and to find the direction of development of teacher librarian training and retraining program. A total of 238 subjects were used for the final analysis. They were analayzed using IPA, Borich's needs analysis and the Locus for Focus model. As a result, First, teacher librarians perceived that the importance and performance of teacher and manager competency were higher than information specialist and cooperative leader. Second, they needed competencies of data-science, coding, Internet of Things in the field of information specialist as changing educational environment. Third, they needed competencies of information ethics, copyright instruction, and digital and media literacy education in the field of teacher. Fourth, they needed competencies of facility designing for future education, online and offline school library marketing skills, and establishment of makerspaces and learning commons in the field of ibrary manager. Fifth, they needed competencies of library based instruction, library cooperative instruction, and building a collection related to subject in the field of cooperative leader. Sixth, the highest required competency for teacher librarians was suggested for teacher librarians' role area.

• Journal of the Korean Geosynthetics Society
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• v.21 no.4
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• pp.1-12
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• 2022

Generally, the plate load test and the field density test are conducted for compaction quality control in earthwork, and then additional analysis. Recently developed that the DCPT (Dynamic Cone Penetration Test) equipment for smart compaction quality control its the system are able to get location and real-time information about worker history management. The IoT-based the DCPT system improved the time-cost in the field compared traditional test, and the functions recording and storage of the DPI (Dynamic Cone Penetration Index) were automated. This paper describes using these DCPT equipment on in-situ and compared to the standards of the DCPT, and the compaction trend had be confirmed with DPI as the field test data. As a result, the DPI of the final compaction decreased by 1.4 times compared to the initial compaction, confirming the increase in the compaction strength of the subgrade compaction layer 10 to 14 cm deep from the surface. A trend of increasing compaction strength was observed. This showed a tendency to increase the compaction strength of the target DPI proposed by MnDOT and the results of the existing plate load test, but there was a difference in the increase rate. Therefore, additional studies are needed on domestic compaction materials and laboratory conditions for target DPI and correlation studies with the plate load tests. If this is reflected, it is suggested that DCPT will be widely used as smart construction equipment in earthworks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.949-955
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• 2020

In this Study, Social Network Analysis of IoT related patents in South Korea, China, and Japan was conducted from the viewpoint of patent informatics. To this end, 2,526 patents filed by PCT until December 2019 were investigated up to the subclass level of the IPC code. As a result, in the case of South Korea, representative IPC codes are in the order of G06Q, H04L, G06F, H04W, and the highest frequency of interconnection is H04L→H04W, H04W→H04L, H04W→H04B. In China, the representative IPC codes are in the order of H04L, H04W, G05B, G06Q. South Korea has strong technological convergence centered on the G06Q, while China has strong convergence centered around H04L and H04W. Moreover, in China, H04L and H04W have more diverse combinations than in South Korea in Section A, B, G, and H. In the future, it is necessary to study the diversity of technology convergence of H04L and H04W in China.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• Journal of Food Hygiene and Safety
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• v.34 no.6
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• pp.542-550
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• 2019

To detect Escherichia coli from agri-food and production environments, a device based on IoT (internet of things) technology that can check test results in real time on a mobile phone has been developed. The efficiency of the developed device, which combines an incubator equipped with a UV lamp, a high-resolution camera and software to detect E. coli in the field, was evaluated by measuring the device's temperature, detection limit, and detection time. The device showed a difference between its programmed temperature setting and actual temperature of about 1.0℃. In a detection limit test performed with a single-colony inoculation, a color change to yellow and a florescent signal were detected after 12 and 15 h incubations, respectively. The incubation time also decreased along with increasing bacteria levels. When applying the developed method and device to various samples, including utensils, gloves, irrigation water, seeds, and vegetables, detection rates of E. coli using the device were higher than those of the Korean Food Code method. These results show that the developed protocol and device can efficiently detect E. coli from agri-food production environments and vegetables.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.442-454
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• 2017

As the demand for meat products has steadily increased in Korea, so the livestock industry has full grown. However, the opening of import meat products is taking a toll on the local industry. Cost reduction on livestock feed, which comprises the majority of costs involved in the industry is urgent to gain a competitive edge. As Internet of Things (IoT) technologies are being applied across a multiple of industries, so are the cases of applied Smart Farm technology for efficient production. The following research aims to utilize IoT technologies to measure, in real time, the rate of depletion of feed and remaining amount and to propose an effective automated reorder & delivery system. First, a method of utilization of ultrasonic and temperature/humidity sensors to obtain corresponding data of remaining feed in the Feedbin is proposed. In addition, a method of sending the obtained data via on-the-farm gateway to Supply Chain Management (SCM) servers is proposed. Finally, utilization of the stored data to construct an automated reorder & delivery service system is proposed. It is in the researcher's intention to contribute to and enable the local livestock industry with the application of various IoT services.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1228-1236
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• 2018

Recently, the maritime environment contaminated by the abandoned fishing gears. To solve this problem, there requires systematic management techniques for the fishing gears based on ICT technologies. The existed systems are optionally used by owners, but the systems need to adopt the monitoring and control architecture for integrated national surveillance. To do this, we designed an architecture for effective monitoring and management which collects position and state information using automatic identification buoy (AIB) device, to send the fishing ship, administrator ship, and shore side control center based on the IoT networks. Especially, in this paper, we developed the ENC-based integrated control system for efficient management which provides functions for position indication, state information display and loss alarm of fishing gears. Also, we conduct performance tests for data processing and visualization functions of the system to use a virtual buoy generation module.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.611-618
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• 2020

The reliability of the results of LCC analysis is determined by accurate analytical procedures and energy data from which the uncertainty is removed. Until now, systems that can automatically measure these energy data and produce databases have not been commercialized. Therefore this paper proposes a concept model of an S-LCC platform that can automatically collect and analyze electric energy consumption data of equipment systems using the IOT, which is the core tool in the Fourth Industrial Revolution and operates the equipment system efficiently using the analyzed results. The proposed concept model was developed by the convergence of existing BLCS and IOT and was comprised of five modules: Facility Control Module, LCC Analysis Module, Energy Consumption Control Module, Efficiency Analysis Module, and Maintenance Standard Reestablishment Module. Using the results of LCC analysis deduced from this system, the deterioration condition of an equipment system can be identified in real-time. The results can be used as the baseline data to re-establish standards for the maintenance factor, replacement frequency, and lifetime of existing equipment, and establish new maintenance standards for new equipment. If the S-LCC platform is established, it would increase the reliability of LCC analysis, reduce the labor force for entering data and improve accuracy, and would also change disregarded data into big data with high potential.