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Collaborative Research: CNS Core: Medium: Real-Time Liquid Wireless Networking for Data-Intensive Rural Applications

Collaborative Research: CNS Core: Medium: Real-Time Liquid Wireless Networking for Data-Intensive Rural Applications
合作研究:CNS 核心:媒介:数据密集型农村应用的实时液体无线网络
批准号:
2212573
负责人:
Hongwei Zhang
金额:
$30.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-10-01 至 2025-09-30

项目摘要

项目成果

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中文摘要
翻译
农村宽带是强大的农村经济和生活质量的基础,许多农村应用需要实时数据密集型通信。无线网络是农村宽带的重要组成部分;然而,农村无线受天气、地形、树叶以及作物类型和密度等环境因素的影响,农村无线网络需要以低于城市网络的密度覆盖更大的区域。为了支持实时数据密集型农村应用,本项目将研究实时液态无线网络(RT-LWN)。RT-LWN框架有望成为农村宽带解决方案的基础组件,农业自动化和沉浸式在线教育等启用的实时数据密集型农村应用预计将对农村行业和社区产生革命性影响。该项目将产生首个农村接入和回程连接的真实世界测量数据和模型,它们将被研究和教育界广泛使用。该项目将为扩大对计算机的参与创造令人兴奋的机会,它将有助于丰富本科生和研究生的研究和教育以及K-12的推广。项目成果将广泛传播。在RT-LWN框架中,应用程序数据使用喷泉码进行编码,然后通过无线接入和回程网络进行传输,并提供概率实时分组传输保证。喷泉编码数据的流动性,再加上无线接入和回程的概率实时通信保证的现场可部署方法,实现了每个源块的高效、实时交付,同时在存在快速变化的动态和不确定性的情况下充分利用了各种无线网络的聚合能力。RT-LWN框架有效地将喷泉编码的流动数据与可预测的无线网络设计相结合。特别是,通过在链路、网络和液体传输层对通信可靠性、及时性和吞吐量进行可预测控制,RT-LWN实现了“设计上的可预测性”,同时解决了农村无线的弹性和性能挑战,以实现变革性的实时数据密集型应用。RT-LWN将液态数据网络嵌入到农村无线网络架构中,特点是1)新颖、有效地将液态传输层集成到终端中,2)基于端到端原则跨液态传输层和更低层的功能分解,以及3)现场可部署的整体设计,以应对复杂、快速变化的无线动态和不确定性。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。
英文摘要
Rural broadband is a foundation for a strong rural economy and quality of life, and many rural applications require real-time data-intensive communications. Wireless networks are essential building blocks of rural broadband; however, rural wireless is subject to environmental factors such as weather, terrain, foliage, and crop types and densities, and rural wireless networks need to provide coverage to much larger areas with less density than urban networks. To support real-time data-intensive rural applications, this project will investigate Real-Time Liquid Wireless Networking (RT-LWN). The RT-LWN framework is expected to become a foundational component of rural broadband solutions, and the enabled real-time data-intensive rural applications such as agriculture automation and immersive online education are expected to have a transformative impact on rural industries and communities. This project will generate first-of-its-kind real-world measurement data and models of rural access and backhaul links, and they will be of broad use by the research and education communities. This project will create exciting opportunities for broadening participation in computing, and it will help enrich undergraduate and graduate research and education as well as K-12 outreach. Project results will be broadly disseminated. In the RT-LWN framework, application data are encoded using fountain codes and then delivered across wireless access and backhaul networks with probabilistic real-time packet delivery guarantees. The liquidity of fountain-encoded data, together with a field-deployable approach to probabilistic real-time communication guarantees across wireless access and backhaul, enables efficient, real-time delivery of each source block while fully leveraging the aggregate capacity of heterogeneous wireless networks in the presence of fast-varying dynamics and uncertainties. The RT-LWN framework effectively integrates fountain-encoded liquid data with the design of predictable wireless networking. In particular, with predictable control of communication reliability, timeliness, and throughput at the link, network, and liquid transport layers, RT-LWN enables “predictability by design”, and it tackles the resiliency and performance challenges of rural wireless at the same time, to enable transformative real-time data-intensive applications. RT-LWN embeds liquid data networking into a rural wireless network architecture featuring 1) novel, effective integration of the liquid transport layer into end-points, 2) functional decomposition across the liquid transport layer and lower layers based on the end-to-end principle, and 3) field-deployable, holistic designs for addressing complex, fast-varying wireless dynamics and uncertainties.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.
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