CRII: CNS: RUI: Exploiting Robust Deep Learning Framework for Wireless Localization Systems in Adversarial IoT Environments

CRII：CNS：RUI：在对抗性物联网环境中利用强大的深度学习框架实现无线定位系统

• 批准号: 2321763
• 负责人: Xuyu Wang
• 金额: $ 17.5万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321763&HistoricalAwards=false
• 关键词: CRII; CNS; RUI; Exploiting; Robust

With the proliferation of wireless networks and mobile devices, wireless Internet of Things (IoT) applications (e.g., location-based services) have gained considerable attention. Indoor localization faces a number of challenges in the radio propagation environment, including the multipath effect, shadowing, fading, and delay distortion. To tackle the non-line-of-sight (NLOS) indoor environment, fingerprinting based wireless localization methods using deep neural networks (DNN) have been proposed. However, a data-driven only approach using DNN may perform poorly in adversarial IoT environments (e.g., wireless jamming). Specifically, DNN models are shown to be vulnerable to adversarial examples generated by introducing a subtle perturbation. Thus, the primary aim of the proposed research is to develop robust solutions for wireless localization in adversarial IoT environments, which fills in the gap between wireless localization accuracy and robustness. Particularly, we consider adversarial machine learning for wireless localization in IoT environments. The successful completion of this project will significantly improve the state-of-the-art of wireless localization and enable robust IoT applications. The project's educational plan includes developing a new graduate-level course on deep learning for wireless IoT systems and enhancing various core undergraduate and graduate-level courses. Also, the project strives to broaden participation from under-represented groups in research and will continue to greatly strengthen such efforts throughout the project years.The project research agenda is composed of two closely integrated research thrusts. In Thrust I, this project will use adversarial deep learning for indoor localization in a way that leverages adversarial training in the offline stage to improve the robustness of the deep network, thus alleviating the threat of the adversarial example attacks on wireless data. This project will consider two wireless localization tasks: adversarial examples for wireless localization in black-box attacks and unsupervised learning for adversarial examples detection. In Thrust II, this project will combine deep learning and Gaussian processes for uncertain location estimation, to improve robustness for wireless localization algorithms. Specifically, this project will exploit uncertainty location estimation with deep Gaussian process against both white-box and black-box attacks. Also, this project will model and analyze the fundamental limits and robustness of wireless localization. For all the proposed tasks in the two thrusts, this project will develop mathematical models and solution algorithms. The proposed algorithms will be implemented with wireless IoT devices/platforms (e.g., Wi-Fi, RFID, and LoRa), and validated with extensive experiments in representative indoor environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着无线网络和移动设备的扩散，无线物联网（IoT）应用程序（例如，基于位置的服务）引起了极大的关注。室内定位在无线电传播环境中面临许多挑战，包括多径效应，阴影，褪色和延迟失真。为了应对非视线（NLOS）室内环境，已经提出了基于指纹的无线定位方法使用深神经网络（DNN）。但是，仅使用DNN的数据驱动方法在对抗性物联网环境中的性能很差（例如，无线堵塞）。具体而言，通过引入微妙的扰动，DNN模型很容易受到产生的对抗示例的影响。因此，拟议的研究的主要目的是为对抗性物联网环境中的无线定位开发可靠的解决方案，该解决方案填补了无线定位精度和鲁棒性之间的空白。特别是，我们考虑在物联网环境中无线本地化的对抗机器学习。该项目的成功完成将显着改善无线本地化的最新面积，并启用强大的物联网应用程序。该项目的教育计划包括开发一门新的研究生课程，以实现无线物联网系统的深度学习，并增强各种核心本科和研究生级课程。此外，该项目旨在扩大代表性不足的研究小组的参与，并将在整个项目期间继续大大加强此类努力。项目研究议程由两个紧密综合的研究推力组成。在推力I中，该项目将使用对抗性深度学习进行室内定位，以一种在离线阶段利用对抗性训练来改善深网的鲁棒性的方式，从而减轻了对抗性示例对无线数据的攻击的威胁。该项目将考虑两个无线本地化任务：在黑盒攻击中无线本地化的对抗性示例和对抗性示例检测的无监督学习。在推力II中，该项目将结合深度学习和高斯过程，以实现不确定的位置估计，以提高无线定位算法的鲁棒性。具体而言，该项目将通过对白色框和黑盒攻击的深度高斯流程来利用不确定性位置估计。此外，该项目将建模和分析无线本地化的基本限制和鲁棒性。对于两个推力中的所有提议任务，该项目将开发数学模型和解决方案算法。 The proposed algorithms will be implemented with wireless IoT devices/platforms (e.g., Wi-Fi, RFID, and LoRa), and validated with extensive experiments in representative indoor environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

项目成果

Respiratory biofeedback using acoustic sensing with smartphones

• DOI: 10.1016/j.smhl.2023.100387
• 发表时间: 2023-03
• 期刊: Smart Health
• 作者: Azhar Chara;Tianya Zhao;Xuyu Wang;Shiwen Mao