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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

Analytics in the IoT Context

In any non-trivial IoT use case, a huge volume of data is generated at a high speed. This high-volume data needs to be analyzed at similar speeds so that meaningful insights can be deduced, and the required actions can be triggered quickly. Most of the advancements in (generic) analytics can be applied directly to IoT use cases, but two key characteristics of data ingestion (that is, high volume and high frequency) necessitate that some special considerations are taken while reusing generic learnings/algorithms in the context of IoT. For example, IoT visualizations (dashboards) need to be displayed at reasonable granularity while not missing out on crucial/anomalous data points.

In addition to data volume and data velocity, IoT data is different as it can be a combination of structured (sensed values in time series format, such as temperature values captured at intervals of 1 second, and inventory data), semi-structured (operator comments), and unstructured...

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