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An Aggregated Data Integration Approach to the Web and Cloud Platforms through a Modular REST-Based OPC UA

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  • 1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.

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Summary
This summary is machine-generated.

This study introduces a middleware using Open Platform Communication Unified Architecture (OPC UA) and Representational State Transfer (REST) to solve Internet of Things (IoT) interoperability issues. The solution enables efficient data exchange and remote supervision for industrial applications.

Keywords:
Industrial Internet of Things (IIoT)Industry 4.0 (IR 4.0)Internet of Things (IoT)OPC UARESTdata integrationintelligent systemsystem integration

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Area of Science:

  • Computer Science
  • Industrial Automation
  • Embedded Systems

Background:

  • The Internet of Things (IoT) enables heterogeneous systems but faces interoperability challenges in industrial automation due to diverse protocols and resource-constrained devices.
  • Efficient information exchange and remote supervision are hindered by incompatibility issues at various automation levels.
  • Integrating IoT with cloud and web services is crucial for unlocking network capabilities.

Purpose of the Study:

  • To develop a platform-independent middleware module that addresses interoperability and data integration challenges in industrial IoT.
  • To enable seamless information exchange between heterogeneous industrial systems and external domains like cloud and web services.
  • To facilitate resource-constrained device performance for data management and remote process supervision.

Main Methods:

  • Proposed a middleware module combining Open Platform Communication Unified Architecture (OPC UA) and Representational State Transfer (REST) paradigms.
  • Implemented RESTful web and cloud platforms for data collection, remote support, and aggregated resource allocation.
  • Utilized an object-oriented structure for information contextualization and aggregated data integration.

Main Results:

  • The developed middleware demonstrated acceptable platform resource utilization (CPU, bandwidth, power consumption) on Raspberry Pi and Intel NUC.
  • Achieved low service, update, and response times, indicating efficient system performance.
  • Successfully enabled aggregated data management and remote process supervision.

Conclusions:

  • The integrated OPC UA and REST middleware offers a cost-effective solution for interoperable Machine-to-Machine (M2M) communication.
  • This approach enhances industrial IoT by facilitating data exchange, remote supervision, and broader domain integration.
  • The middleware provides a scalable and efficient platform for managing diverse industrial data and applications.