• 전기전자학회논문지
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• 제22권4호
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• pp.1104-1108
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• 2018

IIoT는 IoT를 산업현장에 적용하여 생산, 제조, 안전 등의 요소를 모니터링하며, 작업자가 쉽게 현장을 관리하게 해주는 솔루션이다. 이러한 IIoT에서 중요한 기술요소는 센서를 이용하여 산업현장의 정보수집과 관리자에게 신뢰성 있는 정보를 전달하는 기술이 요구된다. 따라서 일반적인 산업현장에는 Ethernet과 RS485 등의 유선 네트워크 방식을 이용해 정보를 전달한다. 하지만 네트워크 구축에 있어 기반비용의 문제와 넓은 범위의 회선구축에 있어 한계가 존재한다. 따라서 본 논문에서는 공작기계가 즐비해있는 생산라인에 IEEE 802.15.4 Ad-Hoc 무선 센서 네트워크를 구축한다. 또한 공작기계의 배치형태를 고려한 라우팅 방식과 센서노드 고장을 감지하는 알고리즘을 설명한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• International journal of advanced smart convergence
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• 제7권4호
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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.