• 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 Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1524-1530
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• 2018

In this paper, we designed a product that can prevent environmental pollution, waste of resources, and leakage of foreign currency by commercializing a green IT solution by merging a used smart phone with the IoT object communication technology for the first time in the world. For the experiment of the designed system, various performance and communication condition was experimented by installing it in the actual crop cultivation facility. As a result, when a problem occurs, the alarm sound and video notification are generated by the user's smart phone, and remote control of various installed devices and data analysis in real time are possible. In this study, it is thought that the terminal management board developed for the utilization of the used smart phone can be applied to various fields such as agriculture and environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.613-621
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• 2019

Recently, with the development of the Internet of Things (IoT) and cloud computing technologies, security threats have increased as malicious codes infect IoT devices, and new malware spreads ransomware to cloud servers. In this study, we propose a threat-detection technique that checks obfuscated script patterns to compensate for the shortcomings of conventional signature-based and behavior-based detection methods. Proposed is a malicious code-detection technique that is based on malicious script-pattern analysis that can detect zero-day attacks while maintaining the existing detection rate by registering and checking derived distribution patterns after analyzing the types of malicious scripts distributed through websites. To verify the performance of the proposed technique, a prototype system was developed to collect a total of 390 malicious websites and experiment with 10 major malicious script-distribution patterns derived from analysis. The technique showed an average detection rate of about 86% of all items, while maintaining the existing detection speed based on the detection rule and also detecting zero-day attacks.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.17-23
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• 2019

In this paper, quality control (QC) is applied to each meteorological element of weather data collected from seven IoT sensors such as temperature. In addition, we propose a method for estimating the data regarded as error by means of machine learning. The collected meteorological data was linearly interpolated based on the basic QC results, and then machine learning-based QC was performed. Support vector regression, decision table, and multilayer perceptron were used as machine learning techniques. We confirmed that the mean absolute error (MAE) of the machine learning models through the basic QC is 21% lower than that of models without basic QC. In addition, when the support vector regression model was compared with other machine learning methods, it was found that the MAE is 24% lower than that of the multilayer neural network and 58% lower than that of the decision table on average.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.9-17
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• 2021

In this paper, we propose a reversely using the "Man In The Middle Attack" attack technique as a way to introduce network security without changing the physical structure and configuration of the existing network, a Virtual Network Overlay is formed with only a single Ethernet Interface. Implementing In-line mode to protect the network from external attacks, we propose an integrated control method through a micro network security sensor and cloud service. As a result of the experiment, it was possible to implement a logical In-line mode by forming a Virtual Network Overlay with only a single Ethernet Interface, and to implement Network IDS/IPS, Anti-Virus, Network Access Control, Firewall, etc.,. It was possible to perform integrated monitor and control in the service. The proposed system in this paper is helpful for small and medium-sized enterprises that expect high-performance network security at low cost, and can provide a network security environment with safety and reliability in the field of IoT and embedded systems.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.430-436
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• 2019

It is needed to develop an antenna with features of subminiature, light weight and multi-band operation for the variaty services in maritime and industrial fields. The PCB pattern antenna is one of the appropreiate antennas solving these requirements. In this research, the design and fabrication of the PCB pattern antenna operating on the single band of 920 MHz are investigated. The final goal is that the development of the dual band PCB pattern antenna operating on 260 MHz and 920 MHz, which is based on the proposed antenna. It is evident that the performance in the frequencies of 902 MHz, 915 MHz and 928 MHz among of 920 MHz ISM band is better than that in other frequencies. It is also confirmed that the differences of the voltage standing wave ratio, return loss, gain and efficiency between three frequencies are less than 5%. It is expected that the development of communication link of 5-10 km is possible when the induced results are applied into the low power wide area (LPWA) network desinged by the rule of -30 dB sensitivity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.115-121
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• 2020

With the spread of IoT technology, various IoT applications using facial recognition are emerging. This paper describes the design and implementation of a remote control system using deep learning-based face recognition and hand gesture recognition. In general, an application system using face recognition consists of a part that takes an image in real time from a camera, a part that recognizes a face from the image, and a part that utilizes the recognized result. Raspberry PI, a single board computer that can be mounted anywhere, has been used to shoot images in real time, and face recognition software has been developed using tensorflow's FaceNet model for server computers and hand gesture recognition software using OpenCV. We classified users into three groups: Known users, Danger users, and Unknown users, and designed and implemented an application that opens automatic door locks only for Known users who have passed both face recognition and hand gestures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1158-1164
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• 2020

In Korea, the dependence on imported energy is very high, and research to reduce standby power is being conducted based on Zigbee, a low-power technology, to reduce wasted power and improve power efficiency. However, because Zigbee is not an IoT standard protocol and is not network-based, it is necessary to build a network with a separate gateway, and research on standby power is insufficient because the standards for international power consumption of devices are ambiguous. Therefore, in this paper, we applied the IoT standard protocol MQTT to the existing Zigbee technology to build a network network without a separate gateway, and designed and implemented a standby power reduction system that collects standby power degradation and user patterns. As a result of evaluating with the existing system, it was confirmed that about 7.11% of standby power was consumed compared to the existing system.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.399-406
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• 2023

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.