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使传统领域和应用的新型方法成为可能。该项目将设计一种名为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属性。这种能力有可能影响许多对社会有益的应用。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可能很有用。该项目旨在解决使用无线信号进行材料检测方面的算法和实践挑战。将在该项目中开发的模拟、实验数据和软件库将通过微型项目提供给学生。鉴于这项工作的科幻性质，它旨在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、无线多路径(环境反射)和绕射、材料表面的复杂性、任意用户的移动性等原因，检测可能具有挑战性。具体的研究任务是：(I)信号-材料相互作用的物理模型与反射和折射信号的测量的融合。这使得IoTScope能够以比最先进的多路径解卷积技术更高的精度将信号纠缠与多径解耦。(Ii)用于减去环境多径以放大微弱折射信号的迭代硬件抵消技术。(Iii)基于激励/移动性的控制，以不同的入射角度、天线方向和频率探测材料，以最小的开销获得最大的空间分集。(Iv)为了应对特定应用领域在土壤水分检测方面的挑战，该提案利用了RF-VISION融合技术和有关土壤水分特性的先验信息。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。

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.