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 settings for indoor localization services in IoT environments. The following topics will be briefly covered in this chapter:
- Introducing indoor...
