CNS Core: Small: Collaborative Research: loTScope: Sensing Physical Materials via Inexpensive loT Radios

成熟的无线技术和低成本的物联网设备正在为传统领域和应用提供新的方法。该项目将设计一个名为IoTScope的系统，旨在将无线信号照射到任何物体上，并通过分析反射和折射信号来识别材料特性。这种能力有可能影响许多对社会有益的应用程序。利用无人机进行土壤水分检测可以帮助预测作物产量和肥料需求，从而有利于精准农业。检测液体和水污染的热值具有重要的保健应用。能够区分人、汽车和其他物体在自动驾驶和安全应用中很有用。该项目旨在解决使用无线信号实现材料检测的算法和实际挑战。模拟、实验数据和软件库将在项目中开发，将通过小型项目提供给学生。考虑到这项工作的“科幻”性质，它的设计自然地吸引学生进行研究，同时也教授无线通信，信号处理和波物质相互作用的基本原理。该项目试图利用无线信号中固有的丰富信息来检测材料并实现颠覆性的物联网应用。由于噪声数据、无线多径（环境反射）和衍射、材料表面复杂性、任意用户移动性等因素，检测可能具有挑战性。具体的研究任务是：(1)将信号-物质相互作用的物理模型与反射和折射信号的测量相融合。这使得IoTScope能够与多路径解耦信号纠缠，比最先进的多路径反卷积技术具有更高的精度。（二）环境多径相减放大弱折射信号的迭代硬件对消技术。（III）基于驱动/移动的控制，在不同入射角、天线方向和频率下探测材料，以最小的开销提取最大的空间分集。(4)为了应对土壤湿度检测中的特定应用挑战，该方案利用了rf -视觉融合以及土壤湿度特性的先验信息。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Maturing wireless technology and low-cost IoT devices are enabling new kinds of approaches to conventional fields and applications. This project will design a system called IoTScope that aims to shine wireless signals on any object and identify the material properties by analyzing the reflected and refracted signals. Such a capability has the potential to impact many applications helpful to society. Soil moisture detection using drones can help predict crop yield and fertilizer requirement, thus benefitting precision agriculture. Detection of the caloric values of liquids and of water contamination have important health-care applications. Being able to distinguish humans, cars, and other objects can be useful in autonomous-driving and security applications. The project aims to tackle algorithmic and practical challenges in enabling material detection using wireless signals. The simulations, experimental data, and software libraries that will be developed in the project will be made available to students through mini-projects. Given the “Sci-Fi" like nature of this work, it is designed to naturally draw students towards research, while also teaching fundamental principles of wireless communications, signal processing, and wave-matter interaction. The project attempts to harness the rich information inherent in wireless signals for detecting materials and enabling disruptive IoT applications. Detection can be challenging due to noisy data, wireless multipath (environmental reflections) and diffraction, complexity of material surface, arbitrary user mobility, etc. The specific research tasks are: (I) Fusion of physics models of signal-material interaction with measurements of reflected and refracted signals. This allows IoTScope to decouple signal entanglement with multipath with potentially higher accuracy than state-of-the art multipath deconvolution techniques. (II)Iterative hardware cancellation techniques for subtracting environmental multipath to amplify weak refracted signal. (III) Actuation/mobility based control of probing the material at various angles of incidence, antenna orientations, and frequencies to extract maximal spatial diversity with minimal overhead. (IV) To handle application-specific challenges in soil-moisture detection, the proposal exploits RF-vision fusion together with prior information about soil moisture properties.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.