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Smart Node Networks Orchestration: A New E2E Approach for Analysis and Design for Agile 4.0 Implementation.

Annalisa Bertoli¹, Andrea Cervo¹, Carlo Alberto Rosati¹

¹DISMI, University of Modena and Reggio Emilia, 41121 Modena, Italy.

Sensors (Basel, Switzerland)

|March 3, 2021

Summary

This research explores cyber-physical systems, integrating computing and control for complex industrial agents. It introduces multi-agent orchestration for intelligent sensor data mining and analysis, enhancing IoT applications with service-oriented programming.

Keywords:

cyber-physical systems decentralized automation systems industrial internet of things multi-agent systems smart nodes control wireless sensor networks

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Area of Science:

Cyber-Physical Systems (CPS)
Information Technology (IT)
Industrial IoT (IIoT)

Background:

CPS research integrates computing, communication, and process control, often with human interaction.
Complex mechatronics systems, termed industrial agents, require advanced modeling, control, and programming methodologies.

Purpose of the Study:

To investigate data mining and analysis in CPS using multi-agent orchestration of intelligent sensor nodes.
To apply service-oriented programming and microservices paradigms to wireless sensor networks within industrial use cases.
To provide web-based Human-Machine Interface (HMI) visualizations for enhanced data interpretability and analysis.

Main Methods:

Utilized multi-agent orchestration of intelligent sensor nodes with internet protocols.
Implemented a service-oriented programming paradigm, specifically the microservices architecture.
Developed two industrial use case prototypes demonstrating the proposed software architectural pattern for distributed smart sensors.
Employed Key Performance Indicators (KPIs) for system validation.

Main Results:

Demonstrated a common software architectural pattern for distributed smart sensors in IoT applications.
Successfully implemented and validated two industrial use cases as proof of concept.
Showcased the effectiveness of multi-agent systems and service-oriented programming for industrial data analysis.

Conclusions:

The integration of multi-agent systems and service-oriented programming offers a robust approach for data mining and analysis in CPS.
The developed architecture and prototypes provide a concrete solution for enhancing IoT applications, particularly with the advent of 5G technology.
Web-based HMI visualizations significantly improve data interpretability and analysis in industrial settings.