Analysis of architectures implemented for IIoT

Area of Science:

• Computer Science
• Engineering
• Information Technology

Background:

• Cloud computing offers resources for industrial IoT (Internet of Things) but faces challenges like bandwidth limitations, latency, and security concerns.
• Emerging solutions leverage edge computing and fog computing platforms near production plants to address cloud limitations.
• Integrating Information Technology (IT) and Operational Technologies (OT) is crucial for this industrial paradigm shift.

Purpose of the Study:

• To conduct a systematic literature review (SLR) of recent studies on hierarchical and flat peer-to-peer (P2P) architectures for manufacturing IIoT (Industrial Internet of Things).
• To analyze the successes and weaknesses of these architectures concerning latency, security, computing methodologies, virtualization, Fog Computing (FC) in Manufacturing Execution Systems (MES), Quality of Service (QoS), and connectivity.
• To identify potential research areas for implementing IIoT with edge and fog computing technologies.

Main Methods:

• Systematic literature review (SLR) of peer-to-peer (P2P) architectures in manufacturing IIoT.
• Analysis of studies focusing on hierarchical and flat P2P designs.
• Evaluation of factors including latency, security, computing, virtualization, FC in MES, QoS, and connectivity.

Main Results:

• Identified key technological blocks for industrial IoT implementation, highlighting the trade-offs of cloud-based solutions.
• Examined the application of Fog Computing Platforms (FCP) and Edge Computing (EC) in manufacturing IIoT.
• Assessed the impact of IT/OT cooperation on accelerating the adoption of new industrial technologies.

Conclusions:

• Edge and fog computing offer promising alternatives to traditional cloud models for IIoT, mitigating issues like latency and bandwidth.
• Further research is needed to optimize P2P architectures, enhance security, and improve QoS in manufacturing IIoT.
• The integration of IT and OT, supported by academia and industry, is vital for advancing IIoT capabilities.