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

CRII：NetS：RUI：在非均匀环境中使用无电池设备的无线网络基础知识

• 批准号: 2341846
• 负责人: Hongzhi Guo
• 金额: $ 17.5万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341846&HistoricalAwards=false
• 关键词: CRII; NeTS; RUI; Fundamentals; Wireless

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.

Precision农业是一种变革性的技术，它通过使用最近开发的技术（例如无线传感器网络和自主机器人）来消耗较少的资源并产生更多的收益。在土壤中建造的无线地下传感器以提供有关土壤营养的丰富信息将在精确农业中起重要作用。尽管它们可以显着提高农场的收益率，但无线地下传感器的成本且难以维护，因为它们被部署在无法接近的土壤培养基中，这可能会降低农民的利润。预计该项目将通过显着降低其成本和复杂性来从根本上改变无线基础传感器网络。使用磁性感应反向散射通信的低成本无线传感器的想法是有影响力的，因为这些传感器不需要更换电池，并且消耗了可忽略不计的能量。拟议的研究有助于为在不均匀媒体（例如混凝土墙，水下和体内）提供无处不在的传感器提供无处不在的连通性，这可以实现大量的平民和军事应用。该项目还包括一项计划，为基础介绍的少数群体学生创造各种教育和研究经验，以促进劳动力中的多样性。该项目旨在使用具有磁性感应反向散射沟通的低成本电池传感器来开发适应性和可扩展的无线地下传感器网络。最终的目标是用无电池的无电池传感器代替当今的笨重的电池供电的无电感传感器，以在非均匀环境中的原位传感应用程序。由于不均匀的媒体破坏了基于几何原理的现有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个问题并提高我们在复杂的不均匀环境中对无线网络设计的了解。该项目将开发物理层解决方案，用于磁感应反向散射通信，包括通道估计，盲梁成形和能量收集。将建立可重新配置的测试台以验证提出的解决方案，在该解决方案上将在其中执行渠道测量活动以得出全面的渠道模型。土壤动态可以大大改变网络连接性和吞吐量。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范围，保持网络完整性并延长系统的寿命。该项目还将使用磁感应反向散射通信来开发本地化算法，以跟踪不可见的基础传感器的位置。该奖项反映了NSF的法定任务，并通过评估基金会的知识分子和更广泛的影响来审查标准，被认为是宝贵的支持。