• Smart Media Journal
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• v.11 no.2
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• pp.70-76
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• 2022

IoT application development using a cloud server causes problems such as data transmission and reception delay, network traffic, and cost for real-time processing support in network connected hardware. To solve this problem, edge cloud-based platforms can use neuromorphic hardware to enable fast data transfer. In this paper, we propose a model optimization method for supporting spiking neural networks on FPGA hardware. We focused on auto-adjusting network model parameters optimized for neuromorphic hardware. The proposed method performs optimization to show higher performance based on user requirements for accuracy. As a result of performance analysis, it satisfies all requirements of accuracy and showed higher performance in terms of expected execution time, unlike the naive method supported by the existing open source framework.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.169-177
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• 2024

Industrial Wireless Sensor Network (IWSN) is a key feature of Industrial IoT that enables industrial automation through process monitoring and control by connecting industrial equipment such as sensors, robots, and machines wirelessly, and must support the strict requirements of modern industrial environments such as real-time, reliability, and energy efficiency. To achieve these goals, IWSN uses reliable communication methods such as multipath routing, fixed redundant resource allocation, and non-contention-based scheduling. However, the issue of wasting redundant resources that are not utilized for communication degrades not only the efficiency of limited radio resources but also the energy efficiency. In this paper, we propose a scheme that utilizes reinforcement learning in communication scheduling to periodically identify unused wireless resources and reallocate them to save energy consumption of the entire industrial network. The experimental performance evaluation shows that the proposed approach achieves about 30% improvement of resource efficiency in scheduling compared to the existing method while supporting high reliability. In addition, the energy efficiency and latency are improbed by more than 21% and 38%, respectively, by reducing unnecessary communication.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.444-452
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• 2017

Recently, a demand of Internet-of-things (IoT) rises dramatically and an interest in Low Power Wide Area (LPWA) grows larger accordingly. In this paper, performance in LoRa which is included in LPWA standard is analyzed. Particularly, after measuring Received Signal Strength Indication (RSSI) of received signal on Line-of-sight (LoS) and Non-line-of-sight (NLoS) environment and it is compared with RSSI which theoretical path loss model is applied to. Among many path loss models, the simulation for theoretical RSSI use Log-distance, Two-ray model and Okumura-Hata model that is based on the test database. Consequently, the result of Okumura-Hata model is the most similar with the measured RSSI. When a network based on LoRa is built, this result can used to decide optimal node arrangement.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.101-102
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• 2019

산업용 IoT (IIoT)는 최근들어 제조 시스템의 중요한 구성 요소로 간주된다. IIoT를 통해 시설에서 감지된 데이터를 수집하여 작동 조건을 적절하게 분석하고 처리한다. 여기서 비정상적인 데이터는 전체 시스템의 안전성 및 생산성을 위해 신속하게 탐지되어야한다. 기존 임계 값 기반 방법은 임계 값 미만의 유휴 오류 또는 비정상적인 동작을 감지 할 수 없으므로 IIoT에 적합하지 않다. 본 논문에서는 예측 구간과 우선 순위기반 스케줄링을 이용한 분할 선형 회귀 분석을 기반으로 비정상적인 데이터를 검출하는 새로운 방법을 제안한다. 시뮬레이션 결과 제안한 기법은 비정상적인 데이터 검출 속도에서 임계치, 일반 선형 회귀 또는 FCFS 정책을 사용하는 기존의 기법보다 우수함을 알 수 있었다.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.284-292
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• 2017

With the development of Internet of Things (IoT) technology, the combination of ICT and medical services has been increasing to improve the quality of medical services. Using the IoTs, we can collect personal health information continuously in a patient's everyday life. We expect that this will improve the quality of medical service through analysis. However, the problem of ensuring the protection of personal information within the personal health information has been hampering the research, development, and application of such services. Other problems include lack of IoT devices and lack of user convenience for collecting health information about a patient's everyday life. Therefore, in this study, we construct a daily health information management service that can collect the health related information at any time and store this data in personal storage. This data is then only provided to the healthcare worker when necessary. We built a test bed for an IoT-based smart home platform and are currently conducting user experiments. Based on the results of this study, we are attempting to provide a high quality medical trial service based on daily health information through linkage with medical device manufacturers, medical clinics, insurance companies, etc. We expect the proposed health information management service will contribute to the revitalization of smart health care services via activating various health related IoT devices and analyzing daily health information.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.359-367
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• 2020

WSNs as the main technology of IoT often deliver information or authenticate based on location. Thus, verifying location information is essential. This paper aims to present the comprehensive analysis and classification of location verification techniques in WSN. For this, classification criteria are suggested based on the result of feature analysis of existing techniques. In addition, the existing techniques are classified according to the suggested criteria, and each characteristic and development direction are described. The result of this paper is expected to be a useful reference material when designing a new technique.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.634-642
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• 2020

With the proliferation of Internet of Things (IoT) devices, using the Linux operating system in various architectures has increased. Also, security threats against Linux-based IoT devices are increasing, and malware variants based on existing malware are constantly appearing. In this paper, we propose a system where the binary data of a visualized Executable and Linkable Format (ELF) file is applied to Local Binary Pattern (LBP) image processing techniques and a median filter to classify malware in a Convolutional Neural Network (CNN). As a result, the original image showed the highest accuracy and F1-score at 98.77%, and reproducibility also showed the highest score at 98.55%. For the median filter, the highest precision was 99.19%, and the lowest false positive rate was 0.008%. Using the LBP technique confirmed that the overall result was lower than putting the original ELF file through the median filter. When the results of putting the original file through image processing techniques were classified by majority, it was confirmed that the accuracy, precision, F1-score, and false positive rate were better than putting the original file through the median filter. In the future, the proposed system will be used to classify malware families or add other image processing techniques to improve the accuracy of majority vote classification. Or maybe we mean "the use of Linux O/S distributions for various architectures has increased" instead? If not, please rephrase as intended.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.129-136
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• 2018

This study analyzed technology application field and technology transfer type related to the 4th industrial revolution using frequency, visualization, and association analysis of text mining of Big Data. The analysis was conducted between the last three years (2015 - 2017) registered with the NTB of KIAT transfer technology database was utilized. As a result of analysis, First, First, transfer technologies called core technologies of the Fourth Industrial Revolution are a lot of about robots, 3D, autonomous driving, and wearables. Second, as the year go by, transfer technolgy registration such as IoT, Cloud, VR is increasing. Third, the results of the association analysis of technology transfer type are as follows. IoT and VR showed preference for technology trading and licensing, autonomous driving technology trading, wearable licensing, robots preferring technology cooperation, licensing, and technology trading.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.517-524
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• 2017

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.