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Hands-On Deep Learning for IoT

You're reading from  Hands-On Deep Learning for IoT

Product type Book
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Pages 308 pages
Edition 1st Edition
Languages
Authors (2):
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Profile icon Dr. Mohammad Abdur Razzaque
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
View More author details
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
2. The End-to-End Life Cycle of the IoT 3. Deep Learning Architectures for IoT 4. Section 2: Hands-On Deep Learning Application Development for IoT
5. Image Recognition in IoT 6. Audio/Speech/Voice Recognition in IoT 7. Indoor Localization in IoT 8. Physiological and Psychological State Detection in IoT 9. IoT Security 10. Section 3: Advanced Aspects and Analytics in IoT
11. Predictive Maintenance for IoT 12. Deep Learning in Healthcare IoT 13. What's Next - Wrapping Up and Future Directions 14. Other Books You May Enjoy

Deployment techniques

As we argued earlier, each Wi-Fi scan contains the signal strength measurements for APs available in its vicinity, but only a subset of the total number of networks in the environment are observed. Many IoT devices, such as a mobile phone or a Raspberry Pi, are low-end with very little processing power. So, deploying such a DL model would be a challenging task.

Many solution providers and technology companies provide smart positioning services commercially. Using Wi-Fi fingerprinting from indoor and outdoor location data, the accurate tracking of devices is now possible. In most of these companies, the RSSI fingerprint positioning is used as the core technology. In such a setting, signals or messages that bear different sensitivity levels across RSSI values (which is of course subject to the proximity) can be picked up by gateways. Then, if there are gateways...

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