Radio environment maps (REMs) hold a central role in optimizing wireless network
deployment, enhancing network performance, and ensuring effective spectrum …

Software defined radios (SDRs) are often used in the experimental evaluation of next-
generation wireless technologies. While crowdsourced spectrum monitoring is an important …

As demand for radio spectrum increases with the widespread use of wireless devices,
effective spectrum allocation requires more flexibility in terms of time, space, and frequency …

Localizing transmitters using crowdsourced data is considered a practical and cost-effective
method, enabling real-time and accurate location tracking over large areas in order to …

Transmitter localization is an important component of next-gen spectrum sharing and
management systems. Recently, machine learning (ML) methods have shown promising …

Transmitter localization remains a challenging problem in large-scale outdoor environments,
especially when transmitters and receivers are allowed to be mobile. We consider …

We investigate the robustness of a convolutional neural network (CNN) RF transmitter
localization model in the face of adversarial actors which may poison or spoof sensor data to …

This dissertation introduces advanced methods for wireless device localization. Recent
machine learning (ML) methods for localization achieve high accuracy, but are prone to high …

Abstract The Internet-of-Things (IoT) paradigm is resha** the ways to interact with the
physical space. Many emerging IoT applications need to acquire, process, gain insights …