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.

通过使用无线传感技术，如 Wi-Fi、红外线和射频信号来收集环境数据，本研究论文探讨了生成式人工智能（GenAI）在物联网（IoT）系统中克服传统射频感知方法所面临的噪声、干扰、不完整数据和高部署成本等挑战方面的潜力。研究发现通过生成高质量的合成数据、优化信号质量和整合多模态数据，GenAI 为射频环境重建、定位和成像等问题提供了强大的解决方案，并且 GenAI 的泛化能力使得物联网设备能够适应新环境和未知任务，提高了其效率和性能。本文的主要贡献包括对射频感知中的挑战进行详细分析，提出了基于创新的 GenAI 解决方案，并提出了一个统一的框架用于处理各种射频感知任务。通过案例研究，我们证明了整合 GenAI 模型可以实现先进、可伸缩和智能的物联网系统。

物联网系统中的射频感知的生成性人工智能

Generative AI for RF Sensing in IoT systems

We are experiencing an abundance of Internet-of-Things (IoT) middleware
solutions that provide connectivity for sensors and actuators to the Internet.
To gain a widespread adoption, these middleware solutions, referred to as
platforms, have to meet the expectations of different players in the IoT
ecosystem, including device providers, application developers, and end-users,
among others. In this article, we evaluate a representative sample of these
platforms, both proprietary and open-source, on the basis of their ability to
meet the expectations of different IoT users. The evaluation is thus more
focused on how ready and usable these platforms are for IoT ecosystem players,
rather than on the peculiarities of the underlying technological layers. The
evaluation is carried out as a gap analysis of the current IoT landscape with
respect to (i) the support for heterogeneous sensing and actuating
technologies, (ii) the data ownership and its implications for security and
privacy, (iii) data processing and data sharing capabilities, (iv) the support
offered to application developers, (v) the completeness of an IoT ecosystem,
and (vi) the availability of dedicated IoT marketplaces. The gap analysis aims
to highlight the deficiencies of today's solutions to improve their integration
to tomorrow's ecosystems. In order to strengthen the finding of our analysis,
we conducted a survey among the partners of the Finnish IoT program, counting
over 350 experts, to evaluate the most critical issues for the development of
future IoT platforms. Based on the results of our analysis and our survey, we
conclude this article with a list of recommendations for extending these IoT
platforms in order to fill in the gaps.

通过对物联网中间件的代表性样本进行评估，本文重点关注了这些平台对不同物联网用户的预期如何满足，对当前物联网生态系统的差距进行了分析，并在此基础上提出了推荐措施。