# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 3 Proposal
### Proposed IIoT Architecture  
We propose an IIoT system based on Industry 4.0, specifically in the IIC reference, where we integrate operational and information technologies to generate a dataset that provides information for bottleneck detection. This is structured in the following tiers, shown in Figure 6.  
In Appendix A, we present a basic flow of data preprocessing. In the subsequent lines, we present the definition of the various tiers within the proposed model:  
* The field devices tier facilitates connections with control devices such as PLCs, microcontrollers, industrial PCs, embedded systems, and smart sensors. This layer features a communication interface emphasizing interoperability through various protocols,  
Figure 5: Proposed system for intelligent bottleneck detection in the manufacturing line.  
including Ethernet and Wireless. While adapting this technology to an Industry 4.0 system may require effort, optimizing IT-OT convergence is crucial, as highlighted in [45]. Overall, this tier serves as a key link, connecting with control devices to acquire data from sensors and actuators.
* The edge tier plays a crucial role in processing acquired data for bottleneck detection. Using primarily MQTT 5 and OPC-UA [46] protocols, it prepares and categorizes data, with additional connectivity options, such as HTTP for external data sources. The integration component connects to these protocols, collecting sensor values and key data related to bottleneck issues. The objective is to subscribe to MQTT broker \"topics\" [47] or obtain data from OPC-UA clients, integrating, classifying, and publishing datasets for further analysis in the subsequent tier.
* The platform tier consists of a distributed event streaming platform and a microservices backend [48]. The event streaming platform subscribes to datasets containing bottleneck information, allowing real-time or deferred data utilization. The backend, operating as microservices, analyzes and publishes results by comparing real-time datasets with trained data from Big Data for risk predictions. Traceability data are stored in a relational SQL database for business use, including dashboard displays of data trends. Simultaneously, a NoSQL database stores data in JSON format, facilitating Big Data analysis and supporting data traceability analysis in the enterprise tier.
* The enterprise tier is designed to fulfill the needs of bottleneck detection and facilitate decision-making regarding risk. It includes a graphical system interface resembling an Andon system [49; 50] for displaying bottleneck risks at key points on the manufacturing line. The front-end web client receives risk data from microservices, derived from Big Data information. Additionally, the component focuses on data analysis, observation, and monitoring over time using a NoSQL database type. This analysis aims to assess trends, calculate data, and create comparisons during moments of bottleneck risk, providing a solid foundation for informed manufacturing decisions.
* Data traceability is a cross-component application ensuring data quality by incorporating timestamps in each tier. This enables comprehensive tracking of information at every step, offering metrics for optimal path determination, especially across different network segments. Continuous measurement of data is conducted to verify their quality and detect deviations, with the enterprise layer utilizing this information for latency analysis.  
Figure 6: Proposed Industrial IoT Architecture Model.