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

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781803245492

Length 304 pages

Edition 1st Edition

Tools

Combine

Concepts

IoT Development

Author (1):

Jasbir Singh Dhaliwal

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Table of Contents (20) Chapters

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

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Implementing IoT analytics

Although this section specifies the characteristics/considerations of IoT analytics from a technical standpoint, it is worth noting that when implementing an IoT use case, domain know-how is equally important. This know-how varies vastly from one domain to another – for example, the mechanism for detecting anomalies in the agriculture domain would be quite different from the one used for detecting anomalies in the manufacturing domain. Some of the typical scenarios/use cases for which IoT analytics is used are shown in the following figure:

Figure 10.3 – Application scenarios for IoT analytics

IoT analytics is categorized into four different areas, depending on the insights that are generated:

Descriptive analytics: In this type of analytics, stored historical data is analyzed to provide a view of historical performance, anomalies, and more. Even the real-time data stream can be analyzed, but the focus remains...