NeTS: Medium: Networking Out of Thin Air

NeTS:媒介:凭空建立网络

基本信息

  • 批准号:
    1407583
  • 负责人:
  • 金额:
    $ 120万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-08-01 至 2018-07-31
  • 项目状态:
    已结题

项目摘要

This project proposes to lay the algorithmic foundations for ambient backscatter, a novel communication mechanism that transforms existing wireless signals (e.g., TV, cellular, and Wi-Fi transmissions) into both a source of power as well as a communication medium: thus, enabling communication without requiring additional bandwidth and/or power. Ambient backscatter networks have the potential to introduce a new practical model of communication; reduce world-wide battery usage and energy consumption of small computing devices; and reduce the human and economic costs of maintaining power. The proposed research, if successful, can open up ubiquitous communication applications in multiple domains including Internet-of-Things, wearable and mobile devices, as well as infrastructure-free localization. As small Internet-of-Things devices are increasingly embedded in objects and environments such as thermostats, books, furniture, and even implantable medical devices; a novel primitive like ambient backscatter would enable ubiquitous communication at unprecedented scales (without batteries or wires) and in location and times that were previously infeasible (day and night, indoors and outdoors). Further, since ambient backscatter consumes orders of magnitude lower power than traditional radio communication, it can enable always-on communication for battery-operated mobile devices such as smart phones and tablets as well as wearable devices such as head-on displays and smart watches. Finally, since with ambient backscatter, battery-free devices can communicate with each other, they are aware of their context and the devices around them; this enables missing-item localization in warehouses and medical facilities that are infeasible with existing RFID technologies, without an extensive deployment of power infrastructure.The proposed research will produce algorithms, designs, circuits, and system implementations that will enable communication and networking out of thin air. The key challenge in achieving this is that existing signals such as TV, cellular, and Wi-Fi transmissions already carry data and are fast-changing signals; transmitting additional information on top of these signals can be challenging. Further, since the proposed devices are powered using these ambient signals, circuit designs that can harvest energy from them efficiently are needed. This research will design algorithms for enabling communication without requiring additional bandwidth and/or power. This project will also design a complete network stack for this new communication primitive that will enable multiple such devices to co-exist with each other as well as with legacy receivers and leverage algorithms and techniques to enable new functionalities such as battery-free localization and demonstrate their applications in a number of domains including inventory, and healthcare.
该项目提出为环境反向散射奠定算法基础,这是一种新的通信机制,可以将现有的无线信号(例如,TV、蜂窝和Wi-Fi传输)到电源以及通信介质两者中:因此,使得能够在不需要额外带宽和/或功率的情况下进行通信。 环境后向散射网络有可能引入一种新的实用通信模型;减少全球范围内的电池使用和小型计算设备的能耗;并减少维持电力的人力和经济成本。 如果成功,拟议的研究可以在多个领域开辟无处不在的通信应用,包括物联网,可穿戴和移动的设备,以及无基础设施的本地化。随着小型物联网设备越来越多地嵌入到恒温器、书籍、家具甚至可植入医疗设备等物体和环境中,一种新颖的原始环境反向散射技术将以前所未有的规模(无需电池或电线)实现无处不在的通信,并且在以前不可行的位置和时间(白天和黑夜,室内和室外)。此外,由于环境反向散射消耗比传统无线电通信低几个数量级的功率,因此它可以实现电池供电的移动的设备(诸如智能电话和平板电脑)以及可穿戴设备(诸如头戴式显示器和智能手表)的始终在线通信。最后,由于环境反向散射,无电池设备可以相互通信,它们知道它们的上下文和它们周围的设备;这使得仓库和医疗设施中的丢失物品定位成为可能,而这在没有广泛部署电力基础设施的情况下是不可行的。拟议的研究将产生算法,设计,电路,以及能够凭空实现通信和联网的系统实现。实现这一目标的关键挑战是,现有的信号(如电视、蜂窝和Wi-Fi传输)已经携带数据,并且是快速变化的信号;在这些信号之上传输额外的信息可能具有挑战性。此外,由于所提出的设备是使用这些环境信号供电的,因此需要可以有效地从它们中收集能量的电路设计。这项研究将设计算法,使通信不需要额外的带宽和/或功率。该项目还将为这种新的通信原语设计一个完整的网络堆栈,使多个此类设备能够相互共存以及与传统接收器共存,并利用算法和技术实现新功能,如无电池定位,并展示其在库存和医疗保健等多个领域的应用。

项目成果

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Shyamnath Gollakota其他文献

Detecting clinical medication errors with AI enabled wearable cameras
利用人工智能可穿戴相机检测临床用药错误
  • DOI:
    10.1038/s41746-024-01295-2
  • 发表时间:
    2024-10-22
  • 期刊:
  • 影响因子:
    15.100
  • 作者:
    Justin Chan;Solomon Nsumba;Mitchell Wortsman;Achal Dave;Ludwig Schmidt;Shyamnath Gollakota;Kelly Michaelsen
  • 通讯作者:
    Kelly Michaelsen
ARTIFICIAL INTELLIGENCE-ENABLED PREDICTION OF ATRIAL FIBRILLATION EVENTS USING MOBILE ELECTROCARDIOGRAMS
  • DOI:
    10.1016/s0735-1097(22)03045-5
  • 发表时间:
    2022-03-08
  • 期刊:
  • 影响因子:
  • 作者:
    Dan Nguyen;Ananditha Raghunath;Matthew Schram;Dave Albert;Shyamnath Gollakota;Linda Shapiro;Arun Raghav Mahankali Sridhar
  • 通讯作者:
    Arun Raghav Mahankali Sridhar
Wireless earbuds for low-cost hearing screening
用于低成本听力筛查的无线耳塞
Poster: Contactless Infant Monitoring using White Noise
海报:使用白噪声进行非接触式婴儿监测
Real-Time Target Sound Extraction
实时目标声音提取

Shyamnath Gollakota的其他文献

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{{ truncateString('Shyamnath Gollakota', 18)}}的其他基金

SCH: INT: Opioid Overdose Detection and Reversal Using Sensors and Mobile Devices
SCH:INT:使用传感器和移动设备进行阿片类药物过量检测和逆转
  • 批准号:
    1914873
  • 财政年份:
    2019
  • 资助金额:
    $ 120万
  • 项目类别:
    Standard Grant
CSR: Small: Transforming Mobile Devices into Active Sonar Systems for Medical Applications
CSR:小:将移动设备转变为医疗应用的主动声纳系统
  • 批准号:
    1812559
  • 财政年份:
    2018
  • 资助金额:
    $ 120万
  • 项目类别:
    Standard Grant
NeTS: Small: MegaIoT: Enabling Thousands of Concurrent Transmissions in Low-Power Networks
NeTS:小型:MegaIoT:在低功耗网络中实现数千个并发传输
  • 批准号:
    1812554
  • 财政年份:
    2018
  • 资助金额:
    $ 120万
  • 项目类别:
    Standard Grant
CAREER: Internet Connectivity for the Next Billion Devices
职业:未来十亿设备的互联网连接
  • 批准号:
    1452494
  • 财政年份:
    2015
  • 资助金额:
    $ 120万
  • 项目类别:
    Continuing Grant
CSR: Small: Wireless Systems as Gesture Sensors
CSR:小型:作为手势传感器的无线系统
  • 批准号:
    1420654
  • 财政年份:
    2014
  • 资助金额:
    $ 120万
  • 项目类别:
    Standard Grant

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NeTS: Medium: Collaborative Research: Big Data Enabled Wireless Networking: A Deep Learning Approach
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  • 财政年份:
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  • 资助金额:
    $ 120万
  • 项目类别:
    Continuing Grant
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NeTS:媒介:协作研究:GOALI:自适应和灵活频谱光网络
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    1161964
  • 财政年份:
    2012
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    $ 120万
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  • 资助金额:
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