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CNS Core: Small: Low-Power Wide-Area Networks for Industrial Automation

CNS Core: Small: Low-Power Wide-Area Networks for Industrial Automation
CNS 核心:小型:用于工业自动化的低功耗广域网
批准号:
2006467
负责人:
Abusayeed Saifullah
金额:
$50.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-10-01 至 2022-06-30
关键词:

项目摘要

项目成果

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中文摘要
翻译
物联网(IoT)的发展正在将包括过程控制和智能制造在内的工业自动化领域转变为工业物联网(IIoT)的重要类别。如今,用于工业自动化的无线解决方案基于短距离无线技术(例如,WirelessHART,ISA 100)。为了覆盖大量设备的大区域,它们以牺牲能源、成本和复杂性为代价形成多跳网状网络,这对支持当今工业物联网的规模和广域构成了巨大挑战。例如,东德克萨斯州的油田面积超过74 x8平方公里,需要数万个传感器进行自动化管理。此外,在过程工业中,许多筒仓、储罐和工厂通常位于远离中心、困难地形或海上的不方便位置。管道可能长达数百英里,并穿过困难的地形,因此很难实时监测气体和化学品泄漏。该项目建议采用低功耗广域网(LPWAN)技术用于工业自动化。由于长距离,LPWAN可以在没有复杂配置的情况下采用,并且与多跳解决方案相比,广域IIoT应用的成本仅为一小部分。该项目将开发使用LPWAN实现工业自动化的理论基础和系统。其重要结论将与标准机构和行业分享。开发的技术将开源。该项目将特别考虑LoRa,一种领先的LPWAN技术。采用LoRa进行工业自动化带来了一些进化挑战。任何工业自动化系统的基本构建块都是反馈控制回路,这些回路在很大程度上依赖于实时通信。由于严重的能源限制,LoRa使用简单的媒体访问控制协议,该协议不适合实时通信。它需要在几个区域采用低占空比(例如,欧洲)。此外,为了优化性能,工业自动化需要实时调度和控制的协同设计。这种协同设计在LoRa中变得特别具有挑战性,因为它是大规模的,并且具有能量限制。该项目将解决这些挑战并做出以下贡献:(1)使用LoRa需求约束函数理论的自主实时调度技术和分析;(2)联合动态确定控制输入和采样率的可扩展的调度-控制协同设计;(3)通过采用状态自触发和偶触发控制的组合来最大化设备的休眠时间的高能效协同设计,该奖项反映了NSF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。
英文摘要
The evolution of Internet of Things (IoT) is transforming the field of industrial automation including process control and smart manufacturing into an important class of Industrial IoT (IIoT). Today, wireless solutions for industrial automation are based on short-range wireless technologies (e.g., WirelessHART, ISA100). To cover a large area with numerous devices, they form multi-hop mesh networks at the expense of energy, cost, and complexity, posing a big challenge to support the scale and wide-area of today’s IIoT. For example, the East Texas oil-field extends over 74x8 square kilometers requiring tens of thousands of sensors for automated management. Also, in process industries, many silos, tanks, and plants are often positioned far from the center, at inconvenient locations in difficult terrain or offshore. Pipelines can be hundreds of miles long and pass through difficult terrains, making it difficult to monitor gas and chemical leaks in real-time. This project proposes to adopt the Low-Power Wide-Area Network (LPWAN) technologies for industrial automation. Due to long-range, LPWANs can be adopted without complex configuration and at a fraction of costs for wide-area IIoT applications, compared to multi-hop solutions. This project will develop theoretical foundations and systems for enabling industrial automation using LPWANs. Its important findings will be shared with the standards bodies and industries. The developed technologies will be made open-source.This project will particularly consider LoRa, a leading LPWAN technology. Adopting LoRa for industrial automation poses some evolutionary challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. Due to severe energy-constraints, LoRa uses a simple media access control protocol that is unsuited for real-time communication. It needs to adopt low duty-cycling in several regions (e.g., Europe). In addition, to optimize performance, industrial automation needs a codesign of real-time scheduling and control. Such a codesign becomes specially challenging in LoRa because it is large-scale and has energy-limitations. This project will address these challenges and make the following contributions: (1) an autonomous real-time scheduling technique and analysis using the demand bound function theory for LoRa; (2) a scalable scheduling-control codesign that jointly and dynamically determines control input and sampling rates; (3) a highly energy-efficient codesign by maximizing the sleeping times of the devices through a combination of self-triggered and even-triggered control adopting state-aware communication; and (4) an evaluation of the results through experiments using industrial process control use-cases on a physical testbed.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.
期刊论文(4)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1145/3464429
发表时间: 2021-07
期刊: ACM Transactions on Embedded Computing Systems (TECS)
影响因子: --
作者: [V. P. Modekurthy;Abusayeed Saifullah;S. Madria]
通讯作者: V. P. Modekurthy;Abusayeed Saifullah;S. Madria
Low-Latency In-Band Integration of Multiple Low-Power Wide-Area Networks
多个低功耗广域网的低延迟带内集成
DOI: 10.1109/rtas52030.2021.00034
发表时间: 2021
期刊: 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS
影响因子: --
作者: [Modekurthy, Venkata P., Ismail, Dali, Rahman, Mahbubur, Saifullah, Abusayeed]
通讯作者: Saifullah, Abusayeed
CAREER: Protocols for Low-Power Wide-Area Networks in White Spaces
  • 批准号:
    2306486
  • 项目类别:
    Standard Grant
  • 资助金额:
    $55.05万
  • 财政年份:
    2022
  • 负责人:
    Abusayeed Saifullah
  • 依托单位:
CNS Core: Small: Low-Power Wide-Area Networks for Industrial Automation
  • 批准号:
    2301757
  • 项目类别:
    Standard Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2022
  • 负责人:
    Abusayeed Saifullah
  • 依托单位:
Collaborative Research: CNS Core: Medium: Parallel and Real-Time Multicore Scheduling for an Efficiently-Used Cache (PARSEC)
  • 批准号:
    2211642
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $27.5万
  • 财政年份:
    2022
  • 负责人:
    Abusayeed Saifullah
  • 依托单位:
Collaborative Research: CNS Core: Medium: Parallel and Real-Time Multicore Scheduling for an Efficiently-Used Cache (PARSEC)
  • 批准号:
    2306745
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $27.5万
  • 财政年份:
    2022
  • 负责人:
    Abusayeed Saifullah
  • 依托单位:
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    22303037
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  • 负责人:
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基于合成致死策略搭建Core-matched前药共组装体克服肿瘤耐药的机制研究
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  • 项目类别:
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