• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.193-194
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• 2018

본 논문에서는 IIoT(Industrial IoT) 환경에서 사용되는 수 천 개 이상의 센서 데이터를 효율적으로 처리하기 위하여 분산 게이트웨이 시스템을 제안한다. 이 시스템은 대량의 센서에서 측정되는 데이터를 단일 게이트웨이 단위로 처리할 때 늘어나는 작업부하와 처리 시간 지연, 신뢰성 및 정확성 저하를 해결하기 위하여 복수의 게이트웨이 간 연계를 통해 대량의 센서에서 측정되는 데이터를 실시간 처리가 가능한 게이트웨이로 지연시간 없이 균등 할당함으로써 작업부하의 완화와 처리 시간의 가속화, 신뢰성 및 정확성 확보를 이루어낼 수 있다. 본 논문에서는 단일 게이트웨이 시스템과 분산 게이트웨이 시스템의 비교를 통해 수천 개 이상의 센서에서 측정되는 데이터 처리 시간의 차이를 확인함으로써 IIoT 환경에서 분산 게이트웨이의 활용도 면에서 우수함을 보인다.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).

• Journal of IKEEE
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• v.22 no.4
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• pp.1104-1108
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• 2018

IIoT applies IoT to industrial sites to monitor factors such as production, manufacturing, and safety, and it is a solution that allows the worker to easily manage the site. An important technology element in this IIoT is a technology that collects information on industrial sites and delivers reliable information to managers using sensors. Therefore, general industrial sites use wired network methods such as Ethernet and RS485 to deliver information. However, there are limitations to the problem of infrastructure costs and to the wide range of line constructions in network deployment. Therefore, in this paper, the network of IEEE 802.15.4 Ad-Hoc wireless sensors is deployed on production lines with machine tools. In addition, we describe the routing method considering machine tool layout and sensor node failure detection algorithm.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.65-69
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• 2018

In this paper, we propose an interworking architecture for building indoor air quality monitoring server (BEMS) using IIoT (Industrial Internet of Things). The proposed monitoring server adopts IIoT-based standard protocol so that interaction with BEMS installed in existing buildings can be performed easily. It can effectively communicate with indoor air quality measurement sensor installed in the building based on IIoT, Indoor air quality monitoring is possible. We implemented a proposed monitoring server, and confirmed the availability and monitoring of data from sensors in the building.

• Journal of IKEEE
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• v.22 no.2
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• pp.289-301
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• 2018

IIoT, the IoT technology applied to the industrial field, is being used as a monitoring technology for increasing in production rate and safety of workers. However, monitoring through the construction of IIoT network using Ethernet and RS485 in production lines where dozens to hundreds of machine tools are manufacturing components, have difficulties of infrastructure cost and network flexibility and fluidity. Therefore, in this paper, using IEEE 802.15.4 standard WSN device to construct a Ad-Hoc WSN in the production line. In addition, the transmission period and order of the sensor nodes are set by applying the EtherCAT communication method in which the payload frames are shared by all the sensor nodes. From this, we have overcome the problem of reliability decline and real-time issue due to the packet collision of wireless network and confirmed that it is a wireless network routing method that can be used in the actual industrial field.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.39-40
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• 2018

본 논문에서는 IIoT(Industrial IoT) 환경의 분산 게이트웨이 시스템(Distributed-gateway System)에서 하위의 수 천 개 이상의 센서로부터 데이터를 전송받는 각 게이트웨이의 작업부하(Workload)를 감소시키고 데이터 처리 속도를 향상시키기 위하여 신경망(Neural network) 알고리즘을 이용한 센서 데이터 할당 기법을 소개한다. 각 센서의 중요도에 따른 Weight와 측정 간격에 따른 Bias를 설정하고 학습과정을 통해 Output weight를 산출하여 데이터를 효율적으로 게이트웨이에 할당시킴으로써 신뢰성과 정확성, 신속성을 확보한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.195-196
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• 2018

본 논문에서는 IIoT(Industrial IoT) 환경에서 사용되는 각 종 센서의 특성을 고려하여 K-means clustering을 이용해 측정 주기에 따른 군집화를 통해 평준화함으로써 센서에서 게이트웨이로의 데이터 전송 시 일어날 수 있는 1:1 독점 통신 현상 및 작업부하를 해결 할 수 있는 기법을 제안한다. 본 논문에서는 해당 기법의 효율을 보다 극대화할 수 있는 분산 게이트웨이 환경에서 실험을 진행하였으며, 해당 실험의 결과에 따르면 분산 게이트웨이 시스템에서 사용되는 게이트웨이들의 작업부하가 현저히 낮아졌고 각 종 센서들이 할당되는 빈도수가 일정하게 나타남으로써 신뢰성과 정확성을 확보에 보다 우수함을 보인다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.199-200
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• 2018

본 논문에서는 IIoT(Industrial IoT) 환경의 분산 게이트웨이 시스템(Distributed-Gateway System)에서 사용되는 수 천 개 이상의 센서에서 데이터 전송을 받는 각 게이트웨이의 데이터 처리 속도를 향상시키고 작업 오류를 줄이기 위하여 SVM(Support Vector Machine) 알고리즘을 사용한 센서 데이터 할당 기법을 제안한다. 각 센서의 반복 측정 간격과 중요도에 따라 작업부하(Workload)를 구하고, 이를 순차 반복 비교를 통해 Sub-task 값을 구한다. 이렇게 구해진 Sub-task값을 기준으로 각 게이트웨이에 할당시킴으로써 신뢰성과 정확성, 신속성을 확보한다.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.75-83
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• 2018

Predictive IoT Sensor Algorithm is a technique of data science that helps computers learn from existing data to predict future behaviors, outcomes, and trends. This algorithm is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Sensors and computers collect and analyze data. Using the time series prediction algorithm helps to predict future temperature. The application of this IoT in industrial environments like power plants and factories will allow organizations to process much larger data sets much faster and precisely. This rich source of sensor data can be networked, gathered and analyzed by super smart software which will help to detect problems, work more productively. Using predictive IoT technology - sensors and real-time monitoring - can help organizations exactly where and when equipment needs to be adjusted, replaced or how to act in a given situation.

• Smart Media Journal
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• v.5 no.4
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• pp.9-17
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• 2016

Over the next 20 years it will begin the exodus from the Internet and smart phones to the Internet of Things. The heart of IoT gives new utility and value with connectivity among things around people to the human. In future, Industrial environment will be intimately connect all among machines and machines or factories and factories in all processing, and by digitizing of all goods and production life-cycle, which is a combination of virtual world and real world, the digital factory will become reality eventually. The proposed IoT or IIoT based Download OTA (Over-the-Air) provides a flexible mechanism for downloading Media objects of any type and size from a network. Moreover, proposed IoT based DLOTA provides a part of security by lightweight encryption, OTP, and CapBAC technique.