June 2019
Intermediate to advanced
308 pages
7h 21m
English
Content preview from Hands-On Deep Learning for IoT
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Indoor Localization in IoT
Many IoT applications, such as indoor navigation and location-aware marketing by retailers, smart homes, smart campuses, and hospitals, rely on indoor localization. The input data generated from such applications generally comes from numerous sources such as infrared, ultrasound, Wi-Fi, RFID, ultrawideband, Bluetooth, and so on.
The communication fingerprint of those devices and technologies, such as Wi-Fi fingerprinting data, can be analyzed using DL models to predict the location of the device or user in indoor environments. In this chapter, we will discuss how DL techniques can be used for indoor localization in IoT applications in general with a hands-on example. Furthermore, we will discuss some deployment ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.