Generative AI for RF Sensing in IoT systems

• URL: http://arxiv.org/abs/2407.07506v1
• Date: Wed, 10 Jul 2024 09:51:44 GMT
• Title: Generative AI for RF Sensing in IoT systems
• Authors: Li Wang, Chao Zhang, Qiyang Zhao, Hang Zou, Samson Lasaulce, Giuseppe Valenzise, Zhuo He, Merouane Debbah,
• Abstract summary: Radio Frequency (RF) sensing stands out for its cost-effective and non-intrusive monitoring of human activities and environmental changes. Traditional RF sensing methods face significant challenges, including noise, interference, incomplete data, and high deployment costs. This paper investigates the potential of Generative AI (GenAI) to overcome these limitations within the Internet of Things ecosystem.
• Score: 10.326067512318163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles [65.14115295214636]
  We specifically consider Internet of electric vehicles (IoEV) and we categorize GenAI for IoEV into four different layers. We introduce various GenAI techniques used in each layer of IoEV applications. Public datasets available for training the GenAI models are summarized.
  arXiv  Detail & Related papers  (2024-09-24T05:12:10Z)
• Strategic Demand-Planning in Wireless Networks: Can Generative-AI Save Spectrum and Energy? [17.59973153669422]
  This article introduces the concept of strategic demand-planning through demand-labeling, demand-shaping, and demand-rescheduling. GenAI is proposed as a powerful tool to facilitate demand-shaping in wireless networks. GenAI can serve a function in saving energy and spectrum in wireless networks.
  arXiv  Detail & Related papers  (2024-07-02T14:27:06Z)
• Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning [6.117706409613191]
  Wang Zhong lin and his team invented the triboelectric nanogenerator (TENG), which uses Maxwell displacement current as a driving force to directly convert mechanical stimuli into electrical signals. This paper is based on the intelligent sound monitoring and recognition system of TENG, which has good sound recognition capability.
  arXiv  Detail & Related papers  (2024-01-30T08:53:54Z)
• MultiIoT: Benchmarking Machine Learning for the Internet of Things [70.74131118309967]
  The next generation of machine learning systems must be adept at perceiving and interacting with the physical world. sensory data from motion, thermal, geolocation, depth, wireless signals, video, and audio are increasingly used to model the states of physical environments. Existing efforts are often specialized to a single sensory modality or prediction task. This paper proposes MultiIoT, the most expansive and unified IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 real-world tasks.
  arXiv  Detail & Related papers  (2023-11-10T18:13:08Z)
• Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities [4.800138615859937]
  Radio Frequency Fingerprinting (RFF) techniques promise to authenticate wireless devices at the physical layer based on inherent hardware imperfections introduced during manufacturing. Recent advances in Machine Learning, particularly in Deep Learning (DL), have improved the ability of RFF systems to extract and learn complex features that make up the device-specific fingerprint. This paper systematically identifies and analyzes the essential considerations and challenges encountered in the creation of DL-based RFF systems.
  arXiv  Detail & Related papers  (2023-10-25T06:45:49Z)
• Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks [63.246437631458356]
  Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native. This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
  arXiv  Detail & Related papers  (2023-09-23T00:05:39Z)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [0.0]
  We develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2%, respectively.
  arXiv  Detail & Related papers  (2022-04-28T23:10:47Z)
• Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification [9.05607520128194]
  We present novel feature design approaches that exploit the distinct structures of the RF communication signals and the spectrum emissions caused by transmitter hardware impairments. Our proposed DNN feature designs substantially improve classification robustness in terms of scalability, accuracy, signature anti-cloning, and insensitivity to environment perturbations.
  arXiv  Detail & Related papers  (2021-03-02T20:19:05Z)
• Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial [64.77665786141166]
  Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal prorogation in wireless networks. IRS is capable of dynamically altering wireless channels to enhance the communication performance. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks.
  arXiv  Detail & Related papers  (2020-07-06T13:59:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.