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

• 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

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2400万没有高速互联网的美国人中，80%生活在农村地区。下一代网络解决方案所需的充足基础设施在低人口密度地区缺乏经济激励措施，导致对新型农村宽带连接解决方案的需求，加剧了这一数字鸿沟。要弥合这一差距，必须采用新的跨学科方法。该项目调查了在物联网(IoT)的最新进展及其在农业领域日益增长的需求的推动下，这一等式中的游戏规则改变的可能性。为此，一个由毫米波通信、超材料和超表面天线阵列设计、动态频谱接入和无线电接入网络专家组成的跨学科专家团队，与农业机器人和基于传感器的植物表型鉴定专家合作，旨在为农村农场提供连接，并提高国家能力，迅速将新技术引入美国农村。最近农业传感模式、基于视觉的农业决策和田间自动驾驶车辆的使用方面的改进将显著增加对田间连接的需求。农业网络需要适应作物生长阶段高度动态的影响，以及典型垂直基础设施(粮仓、农舍、灌溉系统、饲养场)的独特堵塞和分散特征。该项目提供了一个端到端的现场网络架构，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)以毫米波(MmWave)频谱表征农村移动频道，以推动最先进的技术超越传统的农村蜂窝环境和频谱限制。(Ii)开发深度学习技术以自主管理未来农业网络日益增长的频谱需求。(Iii)将毫米波和频谱管理解决方案紧密结合在一起，设计了一个自动化和安全的网络切片系统，以便在田间场所满足新出现的农业用例的各种需求。与农业科学专家合作，这些解决方案在现实的垂直和移动基础设施下进行评估，并考虑作物动态。该项目解决了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受益于先进的农村连接。与联合国大学计算学院扩大计算参与(BPC)计划相一致，该小组积极争取改善少数群体和女性的参与，主要是通过建筑招募和纳入多样性(Braid)活动，并在项目网站上传播项目结果、软件、数据和进展。该项目为其他农业领域提供了国家典范。该奖项反映了NSF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。

项目成果

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)
• 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
• 作者: Qiang Liu;Nakjung Choi;Tao Han

AgRIS: wind-adaptive wideband reconfigurable intelligent surfaces for resilient wireless agricultural networks at millimeter-wave spectrum

• DOI: 10.3389/frcmn.2023.1169266
• 发表时间: 2023-06
• 作者: Shuai Nie;M. Vuran

CoMap: Proactive Provision for Crowdsourcing Map in Automotive Edge Computing

• DOI: 10.1109/icc45041.2023.10278954
• 发表时间: 2023-02
• 期刊: ICC 2023 - IEEE International Conference on Communications
• 作者: Yongjie Xue;Yuru Zhang;Qian Liu;Dawei Chen;Kyungtae Han

ECHO: Empirical Characterization and Height Optimization of UAV-to-Underground Channels

• DOI: 10.1109/pimrc56721.2023.10293818
• 发表时间: 2023-09
• 期刊: 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
• 作者: Syed Muhammad Hashir;M. Vuran;J. Camp