CNS Core: Medium: Field-Nets: Field-to-Edge Connectivity for Joint Communication and Sensing in Next-Generation Intelligent Agricultural Networks

在没有高速互联网的2400万美国人中，有80%生活在农村地区。低人口密度地区缺乏下一代网络解决方案所需的足够基础设施的经济激励，导致对新型农村宽带连接解决方案的需求，从而加剧了这种数字鸿沟。需要新的跨学科方法来弥合这一差距。由于物联网（IoT）的最新进展及其在农业领域日益增长的需求，该项目研究了改变这一等式的潜力。为此，一个由毫米波通信、超材料和超表面启发的天线阵列设计、动态频谱接入和无线电接入网络专家组成的跨学科团队与农业机器人和基于传感器的植物表型专家合作，旨在为农村农田提供连接，并提高国家能力，将新技术迅速引入美国农村。最近农业传感模式、基于视觉的农业决策以及自动驾驶汽车在农田中的应用的改进将显著增加对农田连接的需求。农业网络需要适应作物生长阶段的高度动态影响，以及典型垂直基础设施（粮仓、农舍、灌溉系统、饲养场）独特的阻塞和散射特性。该项目提供了一个端到端的Field-Net架构，通过新颖的技术创新，明智地利用相互依赖的研究挑战。该项目(i)在毫米波（mmWave）频谱上描述农村移动信道的特征，以推动最先进的技术超越传统的农村蜂窝设置和频谱限制。（ii）深度学习技术的开发是为了自主管理未来农业网络日益增长的频谱需求。（iii）紧密集成毫米波和频谱管理解决方案，设计了一个自动化和安全的网络切片系统，以便在现场满足新兴农业用例的各种需求。与农业科学专家合作，在具有作物动态的实际垂直和移动基础设施下对解决方案进行评估。该项目旨在满足中西部和其他农村地区的独特需求，这些地区通过促进经济增长，从先进的农村互联互通中受益。与UNL计算机学院扩大计算机参与（BPC）计划保持一致，该团队积极寻求提高少数族裔和女性的参与，主要通过建设招聘和包容多样性（BRAID）活动和项目发现、软件、数据和进展在项目网站上发布。该项目为其他农业领域提供了全国样板。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。

Eighty percent of the 24 million Americans who do not have high-speed Internet live in rural areas. This digital divide is aggravated by a lack of economic incentives in low-population-density areas for adequate infrastructure that next-generation networking solutions require, leading to a need for novel rural broadband connectivity solutions. Novel interdisciplinary approaches are necessary to bridge this gap. This project investigates the potential for a game-changer in this equation motivated by the recent advances in the Internet of Things (IoT) and their increasing need in agricultural fields. To this end, an interdisciplinary team of experts in millimeter-wave communications, metamaterial and metasurface-inspired antenna array design, dynamic spectrum access, and radio access networks in collaboration with experts in agricultural robotics and sensor-based plant phenotyping aim to provide connectivity to rural farm fields and increase national competence to bring new technologies to rural America rapidly.Recent improvements in agricultural sensing modalities, vision-based agricultural decision-making, and utilization of autonomous vehicles in fields will significantly increase the demand for connectivity in fields. Agricultural networks need to be adaptive to the highly dynamic impacts of crops during their growth stages, and the unique blockage and scattering characteristics of typical vertical infrastructure (grain bins, farmhouses, irrigation systems, feedlots). The project provides an end-to-end Field-Net architecture that judiciously leverages interdependent research challenges through novel technological innovations. The project (i) characterizes rural mobile channels at the millimeter wave (mmWave) spectrum to push the state-of-the-art beyond conventional rural cellular settings and spectral limits. (ii) Deep learning techniques are developed to manage the increasing spectrum demands of future agricultural networks autonomously. (iii) Tightly integrating the mmWave and spectrum management solutions, an automated and safe network slicing system is devised so that the diverse demands of emerging agricultural use cases can be met at the field premises. Collaborating with agricultural science experts, the solutions are evaluated under realistic vertical and mobile infrastructures with crop dynamics. The project addresses the unique needs of the Midwest and other rural regions, which benefit from advanced rural connectivity by fostering economic growth. Aligned with the UNL School of Computing broadening participation in computing (BPC) plan, the team aggressively pursues to improve participation by minorities and females, primarily through the Building Recruiting And Inclusion for Diversity (BRAID) activities and project findings, software, data, and progress are disseminated on the project website. The project provides a national model for other agricultural fields.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.