  The development of wireless sensing technologies, using signals such as
Wi-Fi, infrared, and RF to gather environmental data, has significantly
advanced within Internet of Things (IoT) systems. Among these, Radio Frequency
(RF) sensing stands out for its cost-effective and non-intrusive monitoring of
human activities and environmental changes. However, traditional RF sensing
methods face significant challenges, including noise, interference, incomplete
data, and high deployment costs, which limit their effectiveness and
scalability. This paper investigates the potential of Generative AI (GenAI) to
overcome these limitations within the IoT ecosystem. We provide a comprehensive
review of state-of-the-art GenAI techniques, focusing on their application to
RF sensing problems. By generating high-quality synthetic data, enhancing
signal quality, and integrating multi-modal data, GenAI offers robust solutions
for RF environment reconstruction, localization, and imaging. Additionally,
GenAI's ability to generalize enables IoT devices to adapt to new environments
and unseen tasks, improving their efficiency and performance. The main
contributions of this article include a detailed analysis of the challenges in
RF sensing, the presentation of innovative GenAI-based solutions, and the
proposal of a unified framework for diverse RF sensing tasks. Through case
studies, we demonstrate the effectiveness of integrating GenAI models, leading
to advanced, scalable, and intelligent IoT systems.
