NB-IoT Localization (with Sapienza University of Rome)
The Narrowband Internet of Things (NB-IoT) technology is a leading solution in the context of Low Power Wide Area Networks (LPWANs), and exploits the cellular infrastructure to enable several Internet of Things (IoT) services for massive Machine Type Communications (mMTC), including smart cities, industrial automation, logistics tracking, and wearables.
Many of these use cases require or benefit from location information, making positioning a key dimension for NB-IoT. However, the widely used global navigation satellite system (GNSS) is not suitable, due to a) further power consumption and costs needed to support GNSS chips, and b) challenging locations in which many NB-IoT devices may be deployed (e.g., deep indoor), where GNSS systems cannot operate. Alternatives enabling reliable localization via direct exploitation of NB-IoT signals are thus needed, as highlighted by current investigations in the research community.
This thesis work focuses on the development and testing of positioning techniques for NB-IoT, leveraging the application of enhanced machine and deep learning techniques on empirical measurements.
Required Skills: Wireless communications and IoT fundamentals, Machine Learning fundamentals, Matlab and/or Python programming, knowledge of data processing techniques.