• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2770-2776
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• 2015

The development of efficient wearable antennas is required to implement short range body-centric wireless communication links for various internet of thing applications. We present simulation and measurement results of conductive-fiber-based wearable antennas which can comfortably fabricated directly on usual clothing materials. The proposed antenna is a form of a rectangular patch antenna designed by weaving conductive fibers on a felt substrate. A full-wave electromagnetic simulation tool is used to investigate the antenna performance such as antenna impedance, resonant frequency, and radiation efficiency. Parametric studies show that the radiation efficiency increases from 67.5% to 70.4% by widening the gap between conductive fibers from 0.25mm to 3mm. This implies a wearable antenna with good radiation efficiency can be designed despite of less portion of conductive fibers on the antenna. The simulation results are also verified by measured results with fabricated antennas.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.189-194
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• 2020

Recently, various clustering techniques have been studied to efficiently handle data generated by heterogeneous IoT devices. However, existing clustering techniques are not suitable for mobile IoT devices because they focus on statically dividing networks. This paper proposes a probabilistic deep learning-based dynamic clustering model for collecting and analyzing information on IoT devices using edge networks. The proposed model establishes a subnet by applying the frequency of the attribute values collected probabilistically to deep learning. The established subnets are used to group information extracted from seeds into hierarchical structures and improve the speed and accuracy of dynamic clustering for IoT devices. The performance evaluation results showed that the proposed model had an average 13.8 percent improvement in data processing time compared to the existing model, and the server's overhead was 10.5 percent lower on average than the existing model. The accuracy of extracting IoT information from servers has improved by 8.7% on average from previous models.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.177-182
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• 2017

As major ICT technologies such as IoT, cloud computing, and Big Data are being applied to manufacturing, smart factories are beginning to be built. The key of smart factory implementation is the ability to acquire and analyze data of the factory. Therefore, the need for a big data analysis platform is increasing. The purpose of this study is to construct a platform for big data analysis of manufacturing process and propose integrated method for analysis. The proposed platform is a RHadoop-based structure that integrates analysis tool R and Hadoop to distribute a large amount of datasets. It can store and analyze big data collected in the unit process and factory in the automation system directly in HBase, and it has overcome the limitations of RDB - based analysis. Such a platform should be developed in consideration of the unit process suitability for smart factories, and it is expected to be a guide to building IoT platforms for SMEs that intend to introduce smart factories into the manufacturing process.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.35-43
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• 2019

The safety of industrial valves, which are the core parts of plant facilities, are managed by manpower and there are difficulties because of side area for inspection and limited accessibility due to the nature of facilities. The industrial valves used in plant facilities cause problems such as interrupted production; a loss of life due to leak or explosion of poisonous material and flammable gases, and difficulty in locating accident positions in the event of leakage or failure. Therefore, safety management and control systems based on IoT technology are needed. This study is about the development of risk factor prediction technique among the safety management of industrial valves through IoT- based wireless communication and the development of actuator control system. We have developed IoT-based industrial valve safety management techniques to prevent accidents caused by main risk factors by conducting an analysis of the structural characteristics of valves and an analysis of the causes of main risk factors through review of failure data and literature and an analysis of accident scenarios.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1001-1009
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• 2016

In LTE networks, the base station transmits control information over the physical downlink control channel (PDCCH) including scheduling grants, which are used to indicate the resources that the user equipment uses to send data to the base station. Because the size of the PDCCH message and the number of the PDCCH transmissions increase in proportion to the number of user equipments, the overhead of the PDCCH may cause serious network congestion problems in the narrowband LTE (NB-LTE) system. This paper proposes the compact PDCCH information bit allocation to reduce the size of the PDCCH message and evaluates the performance of the semi-persistent scheduling (SPS) in the NB-LTE system. The simulation results show that the SPS can significantly reduce the signaling overhead of the PDCCH and therefore increase the system utilization.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.8
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• pp.197-210
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• 2016

Lately smartphone uasage is increasing and many Internet of Things (IoT) devices support wireless communications. Accordingly, small base stations which called femtocells are supplied to prevent saturation of existing base stations. However, unlike the original purpose of the femtocell with the advanced hacking technologies, Vulnerability such as gaining the administrator authority was discovered and this can cause serious problems such as the leakage of personal information of femtocell user. Therefore, identify security threats that may occur in the femtocell and it is necessary to ways for systematic vulnerability analysis. In this paper, We analyzed the security threats that can be generated in the femtocell and constructed a checklist for vulnerability analysis using the Threat Modeling method. Then, using the constructed checklist provides a scheme that can improve the safety of the femto cell through the actual analysis and taken the results of the femtocell vulnerabilities analysis.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.81-90
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• 2014

For necessary information sharing or operation control via wire-wireless/mobile network connecting of devices at disaster area in greatest need of attention, an emergency network efficient construction method quickly connecting nodes within specific range using Delaunay triangulation is proposed. The emergency network constructed by proposed method shows the same aggregate network length, but does more excellent performance in term of network construction time the more long max length connectable to adjacent node as compared with the network by naive method. In experiment of 1000 input terminal nodes, 5 max length connectable to adjacent node, our proposed method enhances 89.1% in execution time without network length increase compared to naive method. So our method can go well to many useful applications as shift construction of communication network of adjacent devices, internet of things and efficient routing in the sensor network in continuous improvement of communication capability.

• Journal of the Korean Institute of Gas
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• v.25 no.5
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• pp.88-96
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• 2021

Advanced Internet of Things (IoT) technology is being used in various ways for the safety of the energy industry. At the center of safety measures, drones play various roles on behalf of humans. Drones are playing a role in reaching places that are difficult to reach due to large-scale facilities and space restrictions that are difficult for humans to inspect. In this study, the accuracy and completeness of movement of dangerous facilities were tested using drone images, and it was confirmed that the movement recognition accuracy was 100%, the average data analysis accuracy was 95.8699%, and the average completeness was 100%. Based on the experimental results, a future-oriented facility risk analysis system combined with ICT technology was implemented and presented. Additional experiments with diversified conditions are required in the future, and ICT convergence analysis system implementation is required.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.861-868
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• 2019

Various server-based services such as big data, IoT and artificial intelligence have been made online. As a result, the demand for IDC to support stable server operation is increasing. IDC is a server-based facility with a stable line and power supply facility that manages 20 to 30 servers in an efficiently separated rack-level subnetwork. Here, we need a way to efficiently manage servers security, firewall, and traffic on a rack-by-rack basis. Including traffic shaping capabilities that control routers, firewalls, IPS, and line speeds, as well as VPN technology, a recent interest. If three or five kinds of commercial equipment are adopted to support this, it may be a great burden to the management cost as well as the introduction cost. Therefore, in this paper, we propose a method to implement the five functions in one rack-unit small integrated security router. In particular, IDC intends to integrate traffic shaping and IPS, which are essential technologies, and to propose the utility accordingly.

• Smart Media Journal
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• v.8 no.1
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• pp.9-18
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• 2019

In the era of the 4th industrial revolution, the big data industry is gathering attention for new business models in the public and private sectors by utilizing various information collected through the internet and mobile. However, although the big data integration and analysis are performed with de-identification techniques, there is still a risk that personal privacy can be exposed. Recently, there are many studies to invent effective methods to maintain the value of data without disclosing personal information. In this paper, a personal information protection system is investigated to boost big data utilization in industrial sectors, such as healthcare and agriculture. The criteria for evaluating the de-identification adequacy of personal information and the protection scope of personal information should be differently applied for each industry. In the field of personal sensitive information-oriented healthcare sector, the minimum value of k-anonymity should be set to 5 or more, which is the average value of other industrial sectors. In agricultural sector, it suggests the inclusion of companion dogs or farmland information as sensitive information. Also, it is desirable to apply the demonstration steps to each region-specific industry.