CRII: NeTS: RUI: Fundamentals of Wireless Networking using Battery-Free Devices in Inhomogeneous Environments

精准农业是一种变革性技术，通过使用无线传感器网络和自主机器人等最新开发的技术，消耗更少的资源，产生更多的产量。无线地下传感器埋在土壤中，提供丰富的土壤营养信息，将在精准农业中发挥重要作用。尽管无线地下传感器可以显著提高农业产量，但由于它们部署在难以接近的土壤介质中，因此成本高昂且难以维护，这可能会降低农民的利润。该项目有望通过显著降低成本和复杂性，从根本上改变无线地下传感器网络。采用低成本无电池无线传感器与磁感应反向散射通信的想法是革命性的，因为这些传感器不需要更换电池，消耗的能量可以忽略不计。提出的研究有助于为非均匀介质（如混凝土墙、水下和体内）中的原位传感器提供无处不在的连接，这可以实现大量民用和军事应用。该项目还包括为少数族裔学生创造各种教育和研究经验的计划，以促进劳动力的多样性。该项目旨在开发适应性和可扩展的无线地下传感器网络，使用低成本的无电池传感器和磁感应反向散射通信。最终目标是用无电池无线传感器取代目前笨重的电池供电无线传感器，用于非均匀环境中的原位传感应用。由于非均匀介质破坏了现有的基于几何原理的无线通信和网络解决方案，因此将开发新的理论和算法来克服这一问题，并提高我们在复杂非均匀环境中无线网络设计的知识。该项目将开发磁感应反向散射通信的物理层解决方案，包括信道估计、盲波束形成和能量收集。将建立一个可重构的测试平台来验证提出的解决方案，在此基础上，将执行渠道测量活动，以得出一个全面的渠道模型。土壤动态可以极大地改变网络连通性和吞吐量。为此，基于机器学习的自适应网络控制算法将用于提供无处不在的覆盖，保持网络完整性，并延长系统的使用寿命。该项目还将开发使用磁感应反向散射通信的定位算法，以跟踪隐形地下传感器的位置。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。

Precision agriculture is a transformative technology that consumes fewer resources and generates more yields by using recently developed technologies such as wireless sensor networks and autonomous robots. Wireless underground sensors that are buried in the soil to provide rich information about soil nutrition will play an important role in precision agriculture. Although they can significantly improve farm yields, wireless underground sensors are costly and hard to maintain since they are deployed in the inaccessible soil medium, which may reduce farmers' profits. This project is expected to radically change the wireless underground sensor network by significantly reducing its cost and complexity. The idea of employing low-cost battery-free wireless sensors with magnetic induction backscatter communications is transformative since these sensors do not require battery replacement and consume negligible energy. The proposed research contributes to a much wider vision of providing ubiquitous connectivity for in-situ sensors in inhomogeneous media such as concrete walls, underwater, and intra-body, which can enable a large number of civilian and military applications. This project also includes a plan to create various educational and research experiences for underrepresented minority students to promote diversity in the workforce.This project aims to develop adaptable and scalable wireless underground sensor networks using low-cost battery-free sensors with magnetic induction backscatter communications. The ultimate goal is to replace today’s bulky battery-powered wireless sensors with battery-free ones for in-situ sensing applications in inhomogeneous environments. Since the inhomogeneous media destroy existing wireless communication and networking solutions that are based on geometric principles, new theories and algorithms will be developed to overcome this problem and advance our knowledge of wireless network design in complex inhomogeneous environments. This project will develop physical layer solutions for magnetic induction backscatter communications, including channel estimation, blind beamforming, and energy harvesting. A reconfigurable testbed will be built to verify the proposed solutions, upon which channel measurement campaigns will be performed to derive a comprehensive channel model. Soil dynamics can dramatically change network connectivity and throughput. To this end, adaptive network control algorithms based on machine learning will be used to provide ubiquitous coverage, maintain network integrity, and prolong the system’s lifetime. This project will also develop localization algorithms using magnetic induction backscatter communications to track the locations of invisible underground sensors.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.