• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.312-313
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• 2017

Internet of Things(IoT) is an intelligent technology and service in which all objects communicate with each other through various networks. Recently Internet of Things(IoT) is one of the fields that is attracting attention as the development of ICT industry. MQTT is a protocol which is safe using TLS or adopting light packet structure for effciency of memory and power using. In this paper, when TLS is used the process of encryption / decryption in the broker occurs. We propose an efficient MQTT protocol through centralized key management by adding authentication server.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.325-327
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• 2022

최근 스마트 기기 시장이 커지며 이와 함께 스마트 기기를 이용한 연구가 활발하다. 현재 많이 사용되는 스마트 기기인 스마트워치와 스마트폰에는 다양한 센서들이 내장되어있다. 이 센서들을 통해 생성된 데이터를 이용하면 사용자의 행동 분류, 건강관리 등 사용자에게 도움이 되는 서비스를 제공할 수 있다. 본 논문에서는 어플리케이션 개발을 통해 상용 스마트기기인 갤럭시 워치 4와 갤럭시 S10에 내장되어있는 센서의 원시데이터를 수집하고 수집한 데이터를 oneM2M 표준 플랫폼에 저장하였다. oneM2M 표준 플랫폼에 저장된 데이터는 API를 통해 손쉽게 사용할 수 있으며 여러 대의 스마트 기기 데이터를 수집하고 빅데이터를 구축한다면 많은 연구자들이 보다 편리하게 데이터를 이용하여 다양한 의미 있는 연구들을 진행할 수 있을 것이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.890-891
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• 2015

With South Korea entering aging society, the problem of the elderly living alone is aggravating due to increasing health risks associated with social isolation. This should be counteracted by providing them with supports conducive to the recovery of social relationship and effective management of daily activities, such as health checkups, homecare services, chore services, and contents building for information service. This paper presents a human care system implementing miniaturization and portability for the elderly and other care recipients by integrating various contents into recipients' situation perception, direct experience, and sensor modules as a smartphone application in Internet of Things environment to facilitate their health status monitoring.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.95-104
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• 2019

Various kinds of devices are used for the Internet of Things (IoT) service, and IoT devices mainly use communication technology that uses the frequency of the unlicensed band. There are several types of communication technology in the unlicensed band, but WiFi is most commonly used. Devices used for IoT services vary in computing resources from devices with limited capabilities to smartphones and provide services over wireless networks such as WiFi. Most IoT devices can't perform complex operations for network control, thus they choose a WiFi access point (AP) based on signal strength. This causes a decrease in IoT service efficiency. In this paper, an intelligent AP system that can efficiently control the WiFi connection of the IoT devices is implemented. Based on the network information measured by the IoT device, the access point learns using a feed forward neural network algorithm, and predicts a network connection state to control the WiFi connection. By controlling the WiFi connection at the AP, the service efficiency of the IoT device can be improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1310-1338
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• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3958-3971
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• 2016

With Internet of Things (IoT) technology, home environment becomes smarter than ever. Not only smart devices such as smart phone or smart TV, but also various IoT devices including sensor, smart thermostat, and smart scale has now become very common on the market. These devices have connectivity to the Internet, so that user can read data from the device or control the device using Internet technology. However, due to diversity of smart home requirements, device collaboration in smart home remains a challenging task still. Usually smart home is built with various technologies to fulfill its own purpose, and these purposes cover very wide area from controlling low-power sensor devices to controlling high-performance devices like smart TV and smart phone. This variety of smart home requirements makes smart home very complicated due to mixed network architecture, protocol and technology. In this paper, a framework to enable managing and collaborating heterogeneous IoT devices in smart home environment is proposed. Several programming models are defined in the proposed framework to make application development for heterogeneous devices more intuitive. The proposed framework has been implemented as a web service, and a case study with real-world smart home IoT devices is presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.9-14
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• 2022

In the IoT(internet of things) where various devices can be connected, failure of essential devices may lead to a lot of economic and life losses. For reducing the losses, fault diagnosis techniques have been considered an essential part of IoT. In this paper, the method based on a graph neural network is proposed for determining fault and classifying types by extracting features from vibration data of systems. For training of the deep learning model, fault dataset are used as input data obtained from the CWRU(case western reserve university). To validate the classification performance of the proposed model, a conventional CNN(convolutional neural networks)-based fault classification model is compared with the proposed model. From the simulation results, it was confirmed that the classification performance of the proposed model outweighed the conventional model by up to 5% in the unevenly distributed data. The classification runtime can be improved by lightweight the proposed model in future works.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.151-155
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• 2017

With the rapid growth of the up-to-date wireless network and Internet technologies, huge and various types of things around us are connected to the Internet and build the hyper-connected society, and lots of smart applications using these technologies are actively developed recently. IoT connects human, things, space, and data with various types of networks to construct the hyper-connected network that can create, collect, share and appling realtime information. Furthermore, most of the smart applications are concentrated on the service that can collect and store realtime contexts using various sensors and cloud technology, and provide intelligence by making inferences and decisions from them nowadays. In this paper, we design a smart application that can accurately control and process the current state of the specific context in realtime by using the state-of-the-art ICT techniques such as various sensors and cloud technologies on the IoT based mobile computing environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.79-85
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• 2019

In this paper, we propose a short-term power forecasting method by applying Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network to Internet of Things (IoT) power meter. We analyze performance based on real power consumption data of households. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean squared error (MSE), and root mean squared error (RMSE) are used as performance evaluation indexes. The experimental results show that the GRU-based model improves the performance by 4.52% in the MAPE and 5.59% in the MPE compared to the LSTM-based model.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.27 no.4
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• pp.301-320
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• 2016

Internet of Things is a future technology driven the fourth industrial revolution. Internet of Things is expected to bring revolutionary changes in all sectors of society thanks to the data and service that is generated by connecting objects to human and other objects. However, while many different cases of the Internet of Things have introduced considerable innovations to various industries and public sectors, little cases and research have been devoted to library service innovation. The purpose of this study is to provide a review of relevant literature relating to the Internet of Things and Libraries and to briefly explain what the Internet of Things is and how it might be useful for libraries. It also analyze S university library case for Internet of Things services and suggest further steps and way for developing IoT services in university libraries.