RINGS: Internet of Things Resilience through Spectrum-Agile Circuits, Learning-Based Communications and Thermal Hardware Security

RINGS：通过频谱敏捷电路、基于学习的通信和热硬件安全实现物联网弹性

• 批准号: 2146754
• 负责人: Marvin Onabajo
• 金额: $ 100万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146754&HistoricalAwards=false
• 关键词: RINGS; Internet; Things; Resilience; through

As the Internet of Things (IoT) continues to grow at a fast pace, the increasing number of wireless devices in the frequency spectrum up to 6 GHz creates a compelling need to securely adapt IoT communications based on availability in the wireless spectrum environment. Autonomous coordination of wireless transmissions to avoid congestions becomes particularly important when numerous IoT devices with stringent power consumption restrictions communicate with an edge device connected to the cloud; collecting information with relatively low data rates such as biomedical signals, detected gases/chemicals levels, temperature, humidity, or vibration data. Such low-power IoT device applications include medical and health care, smart homes, transportation, manufacturing, agriculture, and environmental monitoring. It is imperative to design IoT networks with resilience features deeply embedded across layers from the integrated circuit level to the wireless system level. When IoT devices are employed with sensors in increasingly crowded environments to transmit sensed information, it is essential to increase their awareness of incumbent spectrum users and avoid interference. An overarching goal of this project is to create spectrum-agile IoT networks with low-power adaptive radio frequency (RF) circuits at the sensor nodes, and with coordinated optimization and enhanced security at the edge device. The synergies between the circuits, computing, and wireless networking components of this research are anticipated to create a paradigm for resilient next-generation IoT networks with energy-efficient secure communication between sensor nodes and edge devices. Research and education will be integrated by incorporating the obtained knowledge into graduate and undergraduate education. In addition, high school interns will be engaged through the Center for STEM Education at Northeastern University.The project entails the research and development of a coordinated cross-layer design methodology for agile communication between edge devices and IoT sensor nodes. This is achieved by distributing spectrum sensing and real-time reconfiguration as follows: fast coarse spectrum sensing and reconfiguration in the sub-6 GHz frequency range on the analog/RF circuit level within low-power IoT devices, fine carrier sensing and network level optimizations on the edge device, and enhancement of high-level authentication and anomaly detection with the computing capabilities on the edge device; all aided by wirelessly transmitted information from temperature sensors used as activity detectors embedded in the IoT device transceiver. This cross-layer approach aims at enabling adaptive edge networks by providing the device-level ability to quickly respond to disruptive interference events by changing the transmit and receive frequencies at the IoT nodes, while performing intelligent real-time machine learning (ML) functions for coordinated communication within the network on the edge device with a software-defined radio (SDR) and field-programmable gate array (FPGA). Security will be enhanced at the wireless system level through ML-based RF fingerprinting, while robustness will be enhanced through federated learning techniques. At the hardware level, security will be enhanced through monitoring of power dissipation via embedded temperature sensors. The cross-cutting approach is not only expected to increase the component-level trust that can be established when new IoT devices are introduced into the network, but also to improve run-time reliability by capturing abnormal operations due to malicious intrusions or hardware faults based on the wirelessly transmitted on-chip temperature profiles from the IoT devices.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.

随着物联网（IoT）的持续快速发展，频谱高达6 GHz的无线设备数量不断增加，迫切需要根据无线频谱环境中的可用性安全地适应IoT通信。当许多具有严格功耗限制的物联网设备与连接到云的边缘设备通信时，无线传输的自主协调以避免干扰变得尤为重要;收集数据速率相对较低的信息，如生物医学信号，检测到的气体/化学品水平，温度，湿度或振动数据。此类低功耗物联网设备应用包括医疗保健、智能家居、交通运输、制造业、农业和环境监测。设计具有从集成电路级到无线系统级的各层深度嵌入的弹性功能的物联网网络势在必行。当物联网设备在日益拥挤的环境中与传感器一起使用以传输感知信息时，必须提高它们对现有频谱用户的感知并避免干扰。该项目的首要目标是创建频谱敏捷的物联网网络，在传感器节点处使用低功耗自适应射频（RF）电路，并在边缘设备处进行协调优化和增强安全性。这项研究的电路，计算和无线网络组件之间的协同作用预计将为弹性下一代物联网网络创建一个范例，在传感器节点和边缘设备之间进行节能安全通信。研究和教育将通过将所获得的知识纳入研究生和本科教育来整合。此外，东北大学STEM教育中心还将邀请高中实习生参与。该项目需要研究和开发一种协调的跨层设计方法，以实现边缘设备和物联网传感器节点之间的敏捷通信。这是通过如下分布频谱感测和实时重新配置来实现的：在低功率物联网设备内的模拟/RF电路级上在低于6 GHz频率范围内的快速粗略频谱感测和重新配置，在边缘设备上的精细载波感测和网络级优化，以及利用边缘设备上的计算能力增强高级认证和异常检测;所有这些都由嵌入在物联网设备收发器中的温度传感器无线传输的信息辅助。这种跨层方法旨在通过提供设备级能力来实现自适应边缘网络，通过改变物联网节点的发送和接收频率来快速响应破坏性干扰事件，同时执行智能实时机器学习（ML）功能，以便在具有软件定义无线电（SDR）和现场可编程门阵列（FPGA）的边缘设备上的网络内进行协调通信。安全性将通过基于ML的RF指纹识别在无线系统级别得到增强，而鲁棒性将通过联邦学习技术得到增强。在硬件层面，将通过嵌入式温度传感器监测功耗来增强安全性。这种跨领域的方法不仅有望增加新的物联网设备引入网络时可以建立的组件级信任，而且还通过基于无线传输的数据捕获由于恶意入侵或硬件故障而导致的异常操作来提高运行时可靠性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

项目成果

On-Chip Power Monitoring: Leveraging High-Sensitivity Differential Temperature Sensors With Chopper Stabilization and Offset Calibration

片上功率监控：利用具有斩波稳定和偏移校准功能的高灵敏度温差传感器

• DOI: 10.1109/tim.2024.3370793
• 发表时间: 2024
• 期刊: IEEE Transactions on Instrumentation and Measurement
• 影响因子: 5.6
• 作者: Yan, Mengting;Gourousis, Thomas;Onabajo, Marvin
• 通讯作者: Onabajo, Marvin

Identification of Stealthy Hardware Trojans through On-Chip Temperature Sensing and an Autoencoder-Based Machine Learning Algorithm

• DOI: 10.1109/mwscas57524.2023.10405958
• 发表时间: 2023-08
• 期刊: 2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS)
• 作者: Thomas Gourousis;Ziyue Zhang;Mengting Yan;Milin Zhang;Ankit Mittal;A. Shrivastava;Francesco Restuccia;Yunsi Fei;Marvin Onabajo

Wi-Fi Sensing Based on IEEE 802.11bf

• DOI: 10.1109/mcom.007.2200347
• 发表时间: 2023-01
• 期刊: IEEE Communications Magazine
• 影响因子: 11.2
• 作者: Cheng Chen;Hao Song;Qinghua Li;F. Meneghello;Francesco Restuccia;C. Cordeiro