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 Architectural Patterns and Techniques for Developing IoT Solutions

You're reading from   Architectural Patterns and Techniques for Developing IoT Solutions Build IoT applications using digital twins, gateways, rule engines, AI/ML integration, and related patterns

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803245492
Length 304 pages
Edition 1st Edition
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Author (1):
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Jasbir Singh Dhaliwal Jasbir Singh Dhaliwal
Author Profile Icon Jasbir Singh Dhaliwal
Jasbir Singh Dhaliwal
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Understanding IoT Patterns
2. Chapter 1: Introduction to IoT Patterns FREE CHAPTER 3. Chapter 2: IoT Patterns for Field Devices 4. Chapter 3: IoT Patterns for the Central Server 5. Part 2: IoT Patterns in Action
6. Chapter 4: Pattern Implementation in the Consumer Domain 7. Chapter 5: Pattern Implementation in the Smart City Domain 8. Chapter 6: Pattern Implementation in the Retail Domain 9. Chapter 7: Pattern Implementation in the Manufacturing Domain 10. Chapter 8: Pattern Implementation in the Agriculture Domain 11. Part 3: Implementation Considerations
12. Chapter 9: Sensor and Actuator Selection Guidelines 13. Chapter 10: Analytics in the IoT Context 14. Chapter 11: Security in the IoT Context 15. Part 4: Extending IoT Solutions
16. Chapter 12: Exploring Synergies with Emerging Technologies 17. Chapter 13: Epilogue 18. Index 19. Other Books You May Enjoy

Automatic inspection of finished goods or parts

One interesting use case in the discrete manufacturing space is the automated inspection of finished goods or parts. Finished goods are inspected by analyzing video feeds from digital cameras; by deploying the required machine learning (visual inspection) models on the Device Gateway (DG), these models output a binary value indicating whether a part is defective or not.

Important note

The term sensing device is not limited to video feeds from digital cameras; alternate sensing technologies (such as infrared cameras and x-ray cameras) may very well be used to support specific operating conditions and use case requirements.

If a defective part is detected, a robotic arm will take that part from the conveyor belt and put it in a waste bin. These models are trained at a central server and refined models are pushed to a gateway from the central server. If the model is not able to identify the part with the required level of confidence...

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