• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.263-272
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• 2017

The concept of Internet of Things (IoT) has been increasingly popular these days, and its areas of application have been broadened. However, if the data stored in an IoT system is damaged and cannot be recovered, our society would suffer considerable damages and chaos. Thus far, most of the studies on fault-tolerance have been focused on computer systems, and there has not been much research on fault-tolerance for IoT systems. In this study, therefore, a fault-tolerance method in IoT environments is proposed. In other words, based on the EVENODD method, one of the traditional fault-tolerance methods, a fault-tolerance storage and recovery method for the data stored in the IoT server is proposed, and the method is implemented on an oneM2M IoT system. The fault-tolerance method proposed in this paper consists of two phases - fault-tolerant data storage and recovery. In the fault-tolerant data storage phase, some F-T gateways are designated and fault-tolerant data are distributed in the F-T gateways' storage using the EVENODD method. In the fault-tolerant recovery phase, the IoT server initiates the recovery procedure after it receives fault-tolerant data from non-faulty F-T gateways. In other words, an EVENODD array is reconstructed and received data are merged to obtain the original data.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.75-86
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• 2015

Internet of Thing (IoT) computing is an environment where various devices with sensors and actuators are connect, and interact together to acquire contexts and provide useful services. With the advances of IoT technologies, its usability becomes an in important issue. However, there exist various types of faults in IoT computing which are not conventionally addressed in software research community. Providing reliable IoT services is challenging. In this paper, we present a hierarchy of IoT faults and analyze causes and symptoms of the faults. Based on the analysis, we define effective methods for managing IoT faults. We believe that our proposed framework for managing IoT faults can be utilized in reducing the development cost of IoT applications and enhancing the quality of the applications.

• 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 Korea Multimedia Society
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• v.24 no.8
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• pp.1101-1113
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• 2021

The rapid growth of sense-and-respond applications and the emerging cloud computing model present a new challenge: providing publish/subscribe middleware as a scalable and elastic cloud service. The publish/subscribe interaction model is a promising solution for scalable data dissemination over wide-area networks. In addition, there have been some work on the publish/subscribe messaging paradigm that guarantees reliability and availability in the face of node and link failures. These publish/subscribe systems are commonly used in information-centric networks and edge-fog-cloud infrastructures for IoT. The IoT has an edge-fog cloud infrastructure to efficiently process massive amounts of sensing data collected from the surrounding environment. In this paper. we propose a quorum-based hierarchical fault-tolerant publish/subscribe systems (QHFPS) to enable reliable delivery of messages in the presence of link and node failures. The QHFPS efficiently distributes IoT messages to the publish/subscribe brokers in fog overlay layers on the basis of proposing extended stepped grid (xS-grid) quorum for providing tolerance when faced with node failures and network partitions. We evaluate the performance of QHFPS in three aspects: number of transmitted Pub/Sub messages, average subscription delay, and subscritpion delivery rate with an analytical model.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.562-565
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• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.1002-1003
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• 2016

IoT 기술은 직접적인 사람의 제어 없이 기기 간 자율 통신을 지원하며 빅데이터와 함께 미래 ICT 시장의 큰 비중을 차지할 유망 기술로써 높게 평가되고 있다. 하지만 IoT 기술 범주에 포함되는 Zigbee, Bluetooth, Bluetooth LE, Wi-Fi 등의 기술은 상호 호환되지 않고 통신 목적의 상이하기 때문에, 각 기기들은 다수의 통신 장치를 모두 탑재하고 있으며 이에 따른 운영 효율 또한 높지 않다. 또한, 통신 기술마다 존재하는 독립된 네트워크가 상호 복잡한 서비스 활용에 연계된 환경에서 특정 부분에 장애가 발생할 경우, 서비스별 관계정보 부재로 인하여 장애 발견과 복구 및 대체 서비스 제공이 어렵다. 본고는 이러한 문제를 해결하기 위하여 각 기기간의 이기종 통신I/F 연동방안과 기기 유형별로 발생될 수 있는 환경 변화에 대한 Physicality 분석을 통하여 제공 가능한 대체 서비스 분석방안을 제시한다.

• 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.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.497-498
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• 2017

Electrical fire is a bit part of the cause of a fire. To reduce fire, we study the system for efficient alert for ARC which is the most part of electrical fire. We use IoT to alert managers faster to prevent accident.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.8-15
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• 2020

Internet-of-Things (IoT) technologies are used everywhere, and communication is one of its core and essential aspect. To solve the networking and communication of small IoT terminals, in this paper, a communication scheme based on low-cost WiFi is proposed, which also has the advantages of good compatibility and low power consumption. At the same time, it has a convenient one-key configuration mode, which reduces the technical requirements for operators. In this study, a communication protocol is designed that mainly aims at up to dozens of domestic IoT terminals, in which the amount of data is not large, data exchange is not high, and network is unstable. According to the alarm data, update data, and equipment or network fault, the protocol can respectively transmit in real time, regularly and repeatedly. This protocol is open and easy to integrate, and after cooperating with tiny encryption algorithm, information can be safely transmitted.