The Industrial Internet of Things (IIoT) combines all the devices that were originally programmed independently into the same intelligent network system. These systems typically have real-time computing, data interconnection, security integration, high performance, reliability, and scalability. Due to the limitations of the current cloud solutions, there are more and more advanced and high-value IIoT use cases, starting to shift from the cloud to fog computing (fog compuTIng).
Embedded CompuTIng Design reports that the evolution of IIoT can be divided into three levels: monitoring, optimization and autonomy, depending on the degree of data interconnectivity and artificial intelligence (AI). Monitoring includes asset performance management such as predictive maintenance and asset tracking. This level of data flows only from the edge to the back-end cloud or control center, without interconnecting each other. Both artificial intelligence and advanced analysis run only on the back end.
The focus of this level of use case is process optimization, which uses data captured by sensors to monitor and improve the entire industrial process. As the number of sensors used increases, in order to reduce the amount of data transmitted to the back end, the system begins to generate the need for edge analysis, and edge analysis will continue to improve the P2P data interconnectivity of the process. As a result, the IIoT system has data interconnectivity and layered computing capabilities, all of which require foggy computing support.
Entering the autonomy level of IIoT, the main use of the AI ​​management system eliminates the need for human intervention. Decision-making powers and controls will be allocated to different levels of the IIoT system based on response time requirements and data size. This level of real-time data is moved between the various compute nodes. If there is no fog calculation, the system will not have autonomy, and through the fog calculation, the industry can develop a more flexible and resilient autonomy system.
For example, after the introduction of IoT technology in offshore wind farms, the data bus in each wind turbine can share data between the analysis and control applications of each computing node. When a sensor installed in a wind turbine detects wind direction or wind changes, it can notify other wind turbines to make adjustments to maintain a stable output of the entire wind farm.
The data from the power generation farm can also be combined with other services such as meteorology and integrated business systems to generate long-term forecasts and analysis data after being passed back to the back-end control center. Monitoring level IIoT does not have the need to deploy fog calculations, but fog calculations are an ideal feature for optimizing use cases; for autonomous use cases, fog calculations are more critical.
Flexible computing frameworks such as Linux containers help simplify the deployment and management of AI applications, while networked standards such as Data Distributed Services (DDS) are an essential part of optimizing data connectivity for autonomous use cases.
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