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

CNS 核心：中：Field-Nets：下一代智能农业网络中联合通信和传感的田间到边缘连接

基本信息

批准号：
2212050
负责人：
Mehmet Vuran
金额：
$ 100万
依托单位：
University of Nebraska-Lincoln
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212050&HistoricalAwards=false
关键词：
CNS Core Medium Field Nets

项目摘要

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.

在没有高速互联网的2400万美国人中，有80％居住在农村地区。这种数字鸿沟因低人口密度区域缺乏经济激励措施而加剧了下一代网络解决方案所需的适当基础设施，从而导致需要新颖的农村宽带连接解决方案。新颖的跨学科方法是弥合这一差距的必要条件。该项目调查了在此方程式中改变游戏规则的潜力，这是由于物联网（IoT）的最新进展及其在农业领域的需求日益增加的可能性。为此，由毫米波沟通专家组成的跨学科团队，超材料和跨面启发的天线阵列设计，动态频谱访问以及无线电访问网络与农业机器人的专家和传感器的工厂的专家合作，基于传感器的工厂型型旨在提高与农场农场的竞争力，并为农场领域的发展提供了连接，以使其与农村领域的竞争力相连感知方式，基于视觉的农业决策以及在田野中自动驾驶汽车的利用将显着增加田野连通性的需求。农业网络需要适应农作物在生长阶段的高度动态影响，以及典型的垂直基础设施（谷物垃圾箱，农舍，灌溉系统，饲养场）的独特阻塞和散射特征。该项目提供了一个端到端的现场网络体系结构，通过新颖的技术创新明智地利用相互依存的研究挑战。该项目（i）表征了毫米波（MMWave）频谱上的农村移动通道，以将最新的最先进的方式推向传统的乡村蜂窝环境和光谱限制。（ii）开发了深度学习技术，以自主管理未来农业网络的频谱需求的增加。（iii）制定了一个自动化和安全的网络切片系统，紧密整合了MMWave和Spectrum Management解决方案，因此可以在现场场所满足新兴农业用例的各种需求。该解决方案与农业科学专家合作，在与作物动态的现实垂直和移动基础设施下进行了评估。该项目解决了中西部和其他农村地区的独特需求，这些需求通过促进经济增长而受益于农村的高级农村连通性。与UNL计算机扩展参与计算计划（BPC）计划的一致，团队积极追求改善少数群体和女性的参与，主要是通过建筑物招募和包容多样性（Braid）活动以及项目发现，软件，数据和进步的项目网站。该项目为其他农业领域提供了国家模型。该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛影响的审查标准的评估值得支持的。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

On Crop Canopy Scattering for Integrated mmWave Sensing and Communication in Agricultural Fields

DOI：
10.1109/mass58611.2023.00018
发表时间：
2023-09
期刊：
2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
影响因子：
0
作者：
Shuai Nie;Yufeng Ge;M. Vuran
通讯作者：
Shuai Nie;Yufeng Ge;M. Vuran

Atlas: automate online service configuration in network slicing

DOI：
10.1145/3555050.3569115
发表时间：
2022-10
期刊：
Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies
影响因子：
0
作者：
Qiang Liu;Nakjung Choi;Tao Han
通讯作者：
Qiang Liu;Nakjung Choi;Tao Han

RoNet: Toward Robust Neural Assisted Mobile Network Configuration

RoNet：迈向稳健的神经辅助移动网络配置