Program Overview
This program equips participants with a practical, engineering-focused understanding of how to design and deploy scalable Industrial IoT architectures. It covers the full IIoT stack – sensors, industrial protocols, gateways, edge computing, cloud platforms, OT–IT integration, data pipelines, and cybersecurity. Led by a senior expert with 25+ years of experience, the course addresses real-world challenges including latency, protocol mismatches, legacy system integration, data quality issues, security gaps, and multi-site scalability. Through hands-on simulations and an architecture design exercise, participants learn to build resilient, maintainable, and cost-effective IIoT systems for manufacturing, energy, utilities, logistics, and industrial automation.
Features
- Design end-to-end IIoT architectures integrating sensors, connectivity, edge compute, cloud platforms, and enterprise systems
- Evaluate and select industrial protocols, gateways, platforms, and cybersecurity layers based on real plant scenarios
- Build scalable data pipelines and digital workflows for monitoring, predictive analytics, automation, and remote operations
- Diagnose architecture failures and propose practical OT–IT integration improvements for reliability, safety, and uptime
Target audiences
- Automation & Control Engineering Teams
- IIoT & Systems Architecture Professionals
- Manufacturing Data & Digital Engineering Teams
- Reliability, Maintenance & Smart Factory Engineers
Curriculum
- 5 Sections
- 36 Lessons
- 1 Day
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- Foundations of IIoT Architecture7
- 1.1IIoT system layers: perception → connectivity → edge/fog → platform → applications
- 1.2Sensor selection logic: Vibration, thermal, acoustic, current signature, position, pressure, flow
- 1.3Edge computing fundamentals: Filtering, pre-processing, event detection, security
- 1.4Network stack: Modbus/TCP, OPC-UA, MQTT, DDS, proprietary industrial protocols
- 1.5Data ingestion pathways: Gateways, brokers, firewalls, device twins
- 1.6Cloud vs On-Premise vs Hybrid IIoT architectures
- 1.7Interoperability, digital thread and standardisation requirements
- Typical IIoT Challenges8
- 2.1High sensor noise leading to poor analytics performance
- 2.2Data loss or latency due to weak network configuration
- 2.3Interoperability conflicts between legacy PLCs, SCADA and new IIoT devices
- 2.4High bandwidth consumption from raw data streaming without edge filtering
- 2.5Difficulty implementing device management at scale (firmware updates, health monitoring)
- 2.6Weak cybersecurity posture: unsecured gateways, open ports, unencrypted device traffic
- 2.7Storage and retention challenges for long-duration time-series data
- 2.8Mismatched data models across production, maintenance, and analytics teams
- Real-World IIoT Architecture Models7
- 3.1Reference Architectures: ISA-95 aligned hierarchical architectures; Purdue Model 4.0 for IT–OT separation; Edge–Cloud Hybrid for low-latency decisions
- 3.2Device-to-Cloud Architectures: Secure MQTT brokers, certificate-based authentication; Multi-protocol gateways enabling PLC bridging
- 3.3Edge Intelligence: On-device analytics, anomaly detection, model retraining schedules
- 3.4Data Pipeline Models: Time-series data pipelines with compression, retention policies; Event-driven architectures for alarms, triggers, and workflow automation
- 3.5Digital Twin Enablement: Real-time synchronisation, operational + physics-based twins
- 3.6IT–OT Convergence Examples: Engineering data + historian + MES integration
- 3.7Cybersecurity Controls: Network segmentation, Zero Trust for IIoT, device identity lifecycle
- IIoT Blueprinting & System Design Methodology9
- 4.1latency, throughput, accuracy, device count, retention
- 4.2Edge–Cloud partitioning strategy
- 4.3Protocol selection matrix (MQTT vs OPC-UA vs Modbus)
- 4.4Security architecture: key management; Identity lifecycle; Encrypted communication; Network segmentation
- 4.5Data model design: semantic tagging, namespace, metadata schemas
- 4.6Storage architecture: cold vs warm vs hot paths
- 4.7Integration strategy: MES, SCADA, ERP, EAM/CMMS, historian
- 4.8Architecting for scale: multi-site deployment, device provisioning
- 4.9Governance for data quality, uptime and auditability
- Hands-On Architectural Exercises & Technical Simulations5
Instructor
