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

随着物联网(IoT)持续快速增长，高达6 GHz的频谱中的无线设备数量不断增加，迫切需要基于无线频谱环境中的可用性安全地调整物联网通信。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，自主协调无线传输以避免拥塞变得尤为重要；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/化学物质水平、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、制造、农业和环境监测。从集成电路层到无线系统层，设计具有深度嵌入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使用传感器传输传感信息时，提高其对现有频谱用户的意识并避免干扰是至关重要的。该项目的主要目标是创建频谱灵活的物联网网络，在传感器节点使用低功率自适应射频(RF)电路，并在边缘设备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效应预计将为具有弹性的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高能效的安全通信。通过将获得的知识纳入研究生和本科教育，研究和教育将整合在一起。此外，高中实习生将通过东北大学STEM教育中心进行招聘。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灵活通信。这是通过分布式频谱感知和实时重新配置实现的，具体如下：低功率物联网设备内模拟/射频电路级别的低于6 GHz频率范围的快速粗频谱感知和重新配置；边缘设备上的精细载波侦听和网络级优化；以及利用边缘设备上的计算能力增强高级身份验证和异常检测；所有这些都得益于用作物联网设备收发器中活动检测器的温度传感器无线传输的信息。这种跨层方法旨在通过提供设备级能力，通过更改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，以利用软件定义无线电(SDR)和现场可编程门阵列(FPGA)在边缘设备上实现网络内的协调通信，从而实现自适应边缘网络。通过基于ML的RF指纹识别将增强无线系统级别的安全性，同时将通过联合学习技术增强健壮性。在硬件层面，将通过嵌入式温度传感器来监测功耗，从而增强安全性。这种交叉方法不仅有望增加在将新的物联网设备引入网络时可以建立的组件级信任，还将基于物联网设备无线传输的芯片上温度分布捕获由于恶意入侵或硬件故障而导致的异常操作，从而提高运行时可靠性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.