• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.3
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• pp.133-141
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• 2018

There are increasing interests on smart home systems. However, most of the existing works focus on the functionality itself. In this paper, we propose an extensible smart home system based on low power networking such as CoAP, 6LoWPAN, and Zigbee. The proposed home IoT system consists of Home APP, Home Server, Home Broker, and Power Devices. Each component of the system is connected by the low-power network technologies aforementioned. As the end device, Power Device senses the current consumption of the attached appliance and controls the power to it. Power Device reports the sensing data to Home Server via Home Broker. The Home Broker enhances the scalability of the system. Home Broker extends the service area and the user's services, and it manages the connection of the underlying devices and processes, and transmits data to Home Server from Power Devices. Through the experimental evaluation, we show that the proposed system achieves the design goals such as extensibility and low power networking.

• 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 Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.543-550
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• 2018

The rapid growth of IoT technology induced by the fourth industrial revolution has resulted in research into various types of wireless sensors, and applications based on this technology are prevalent in many areas. However, among the various sites where this technology is used, railway bridges and tunnels with lengths of tens of kilometers have problems with data acquisition, due to the signal noise induced by the long distance measurement and EMI induced by the high voltage power feeding system, when conventional electric sensors are used. To overcome these problems, many studies on fiber optic sensors have been conducted as a substitute for the conventional electric sensors. However, restrictions on the types of fiber optic sensors have limited their application in railways. For this reason, a hybrid measurement system with IoT based wireless data communication, in which both electric and fiber optic sensors can be applied simultaneously, has been developed. In this study, in order to evaluate the applicability of the hybrid measurement system developed in the previous study, a real-time test for 4 types of measurement environments, which reflect possible railway sites, is performed. As a result, it was confirmed that the signals from both the electric and fiber optic sensors, which were acquired at a remote area in real-time, showed good agreement with each other and that this measurement system has the potential to handle sensors with a sampling rate of 2.5 kHz. In the future, it is expected that the IoT-based hybrid measurement system will contribute to the improvement of structural safety by enabling real-time structural health monitoring when applied to various measurement sites.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2183-2190
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• 2013

Container Security Device (ConTracer) which is suggested in this study is to monitor temperature, humidity, and impact inside of the container while the container is transported. ConTracer could also give information to users when a door of the container is opened over 2 inch within 1 second. Additionally, GPS/GLONASS based global position and status information about container are transmitted to a remote server using IoT (Internet of Things) based communication. In this research, we are looking into the development trend of global container security devices; and applying ConTracer to real freight transport from domestic to overseas using Global Roaming Service which is offered for domestic Mobile Communication Companies as well. As a result, we estimate the performance of ConTracer and verify it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Journal of Korean Society for Quality Management
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• v.44 no.4
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• pp.869-881
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• 2016

Purpose: The objective of this study is to verify the economic validation of quality management integrated train freight car by analyzing economic evaluation indicators such as benefit and cost, net present value, and inter rate of return. Methods: First, we itemize benefit and cost field by reviewing literatures; Benefit consists of 1)Safety, 2)Operation, and 3)Maintenance; Cost consists of 1)Set-up fee, 2)Wireless internet fee, and 3)Cloud storage using fee. Second, based on these estimated values, we conduct an economic evaluation analysis. Among them, benefit and cost, net present value, and internal rate of return are selected. Results: As a result, all estimated values are highly over criterion of economic validity($$B/C{\geq}_-1$$, $$NPV{\geq}_-0$$, $$IRR{\geq}_-R$$); 1)benefit over cost ratio is 28.22, 2)Net present value is 8,121.66million KRW, and 3)Internal rate of return value is 2272%. Conclusion: The findings of this study will help making a decision when train industry adopts IoT technology for improving the effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.35-57
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• 2021

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.181-192
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• 2017

Internet of Things includes the whole process of collected information generated from a variety of objects, as well as analyzing and sharing it, and providing useful information services to people. This study seeks ways to improve security and safety in the areas of service security technology, ID management technology and service access control, all of which take place in the IoT environment. We have implemented the services that can design and issue C&C (Certificate and Capability) service token authentication, which is based on a public key, to improve the service security. In addition, we suggest LCRS (Left Child-Right Sibling) resource model management for the efficient control of resources when generating the resource services from the data collected from node devices. We also implemented an IoT services platform to manage URL security of the resource services and perform access control for services.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1286-1295
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• 2020

It has been increasingly difficult to predict the amounts of Acer mono sap to be collected due to droughts and cold waves caused by recent climate changes with few studies conducted on the prediction of its collection volume. This study thus set out to propose a Big Data prediction system based on meteorological information for the collection of Acer mono sap. The proposed system would analyze collected data and provide managers with a statistical chart of prediction values regarding climate factors to affect the amounts of Acer mono sap to be collected, thus enabling efficient work. It was designed based on Hadoop for data collection, treatment and analysis. The study also analyzed and proposed an optimal prediction model for climate conditions to influence the volume of Acer mono sap to be collected by applying a multiple regression analysis model based on Hadoop and Mahout.