CRII: NeTS: Power Efficient Millimeter Wave Data Delivery for Remote Invasive Species Monitoring
CRII: NeTS: Power Efficient Millimeter Wave Data Delivery for Remote Invasive Species Monitoring
批准号:
1948568
负责人:
Yao Zheng
金额:
$17.49万
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:
2020-05-01 至 2023-04-30
中文摘要
遥感数据分辨率的提高促使能量收集设备采用毫米波(mmWave)通信模块,以增加数据传输带宽。这些设备上的能量条件并不总是足以启动或维持毫米波链路,并且要求系统能够预测通信故障并采取先发制人的行动以最大限度地减少能量消耗。幸运的是,遥感器提供的环境信息包含了足够的知识,能够设计这样的系统。该项目的目标是开发算法和工具来利用这些信息,并通过毫米波数据传输能力增强夏威夷州的太阳能收集远程入侵物种监测系统。该项目的工作将使研究人员、工业界和学生能够在实际应用中使用功率受限的设备实现高带宽实时遥感。这项研究的结果将影响科学、工业和军事领域,包括农业、生态、气象、基础设施和公用事业监测等,在这些领域,高分辨率传感数据的及时通信至关重要。提出的方法的基本直觉是,环境因素,如天气条件,信号阻塞,可以通过远程传感器之间的固有能力或相互作用来识别。这些因素的知识可以用来优化设备唤醒,波束扫描,信号放大等,在物理层。三个互补的研究重点是:1)提取太阳能收集条件与毫米波信号衰减之间的相关性;开发模型和电路来估计毫米波信号在特定太阳条件下的衰减;2)利用现有系统实现的低功耗分米波段通信,设计一种分布式传感架构来检测毫米波波束阻塞并加速波束对准;3)为自主航空数据采集器在传感器群和飞行限制区域之间机动时优化毫米波信号接收,制定并解决了受限航路布置问题。这项工作的所有产品将与文档和教程一起免费提供给研究社区。在项目期间建立的实验室测试平台将提供在线远程测试。传感器平台的硬件原理图、部署概况、数据轨迹和重要的元数据将在网上发布,以促进进一步的使用、测试和研究,从而推动该领域的发展。该奖项反映了美国国家科学基金会的法定使命,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 growing data resolution in remote sensing spurs the adoption of millimeter-wave (mmWave) communication modules on energy-harvesting devices to increase the data delivery bandwidth. The energy conditions on these devices are not always satisfiable to initiate or maintain the mmWave links and require systems to be capable of anticipating communication failures and take preemptive actions to minimize energy expenditures. Fortunately, the environmental information provided by remote sensors contains sufficient knowledge to enable the design of such systems. The goal of this project is to develop algorithms and tools to exploit this information and augment the solar-harvesting remote invasive species monitoring system in the State of Hawaii with mmWave data delivery capability. The work in this project will enable researchers, industry, and students to realize high bandwidth real-time remote sensing with power-constrained devices in real-world applications. The results of this research will impact fields across scientific, industrial, and military interests, including agriculture, ecology, meteorology, infrastructure, and public utility monitoring, etc., where timely communication of high-resolution sensory data is essential.The fundamental intuition of the proposed approach is that environmental factors, such as weather conditions, signal blockages, can be recognized via the inherent capability or interactions between the remote sensors. Knowledge of these factors can be utilized to optimize device awakening, beam scanning, and signal amplification, etc., at the physical layer. Three complimentary research thrusts are pursued: 1) extracting the correlation between solar harvesting conditions and mmWave signal attenuations; developing models and circuits to estimate the mmWave signal attenuations at specific solar conditions; 2) designing a distributed sensing architecture to detect mmWave beam blockage and accelerate beam alignment, by exploiting the low-power decimeter band communication implemented by the existing system; 3) formulating and solving a constrained route placement problem for an autonomous aerial data collector to optimize its mmWave signal reception as it maneuvers between sensor clusters and flight restricted regions. All products of this work will be made freely available to the research community, along with documentation and tutorials. The in-lab testbed to be established during the project will be made available online for remote testing. The hardware schematic of the sensor platform, deployment profiles, data traces, and important meta-data will be posted online to spur further use, test, and research to advance the field.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(2)
专著(0)
科研奖励(0)
会议论文
Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns
跨模态连续用户身份验证和设备与呼吸模式配对
DOI:
10.1109/jiot.2023.3275099
发表时间:
2023
期刊:
IEEE Internet of Things Journal
影响因子:
10.6
作者:
[Islam, Shekh M. M., Zheng, Yao, Pan, Yanjun, Millan, Marionne, Chang, Willy, Li, Ming, Borić-Lubecke, Olga, Lubecke, Victor, Sun, Wenhai]
通讯作者:
Sun, Wenhai
Advances in High-dimensional Time Series Modeling and Its Interface with Deep Learning
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批准号:2311178
-
项目类别:Continuing Grant
-
资助金额:$17.42万
-
财政年份:2023
-
负责人:Yao Zheng
-
依托单位:
国内基金
海外基金
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