Prototyping a Scalable and Evolvable Urban Sensing Platform for Smart Cities

为智慧城市打造可扩展、可进化的城市传感平台原型

• 批准号: 1528966
• 负责人: Charles Catlett
• 金额: $ 15万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-15 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1528966&HistoricalAwards=false
• 关键词: Prototyping; Scalable; Evolvable; Urban; Sensing

The concept of a "smart city" is ubiquitous with data; however, most urban data today lacks the spatial and temporal resolution to understand processes that unfold on timescales of seconds or minutes, such as the dispersion of pollutants. A better understanding of these dynamics can provide information to residents, cyclists or pedestrians who may wish to use air quality data as they navigate urban spaces. This project leverages existing street furniture, integrating air quality and environmental sensors into commercial solar powered, networked waste stations. Sensors embedded in BigBelly waste stations in Chicago and other cities will collect data that will allow researchers to explore critical questions that must be understood in order to begin to develop and drive policies, measurement strategies, and predictive computational models related to the feedback loop between traffic flow and air quality. The partnership with BigBelly, with nearly 30,000 waste stations in place globally, provides a channel through which sensors can be deployed in many cities.The project brings together computer science, cyber-physical systems, distributed systems, and sensor systems expertise to explore technical and societal challenges and opportunities of urban-scale embedded systems in the public sphere, initially related to understanding and ultimately managing urban air quality. Sensors embedded in BigBelly waste stations in Chicago and other cities will explore (1) the spatial and temporal dynamics of air quality in urban canyons, informing the sensor network resolution needed to drive traffic change policies and to provide healthy air quality routing information to cyclists and pedestrians; and (2) how urban topology (natural and built) affects these dynamics and associated required measurement resolutions. These are critical questions that must be understood in order to begin to develop and drive policies, measurement strategies, and predictive computational models related to the feedback loop between traffic flow and air quality. Critical challenges include (1) power management with respect to sensor sampling, in-situ processing, and transmission; (2) ensuring data quality; and (3) providing data in forms that are actionable and understandable to policy makers and the general public. All data will be published in near-real time with web-based analysis tools for use by scientists, educators, policy makers, and residents, and with application programming interfaces (API's) for application development. By developing an open source, readily deployed urban embedded systems infrastructure leveraging a widely deployed commercial platform, the project can enable science, education, and outreach in many cities, national parks, and educational institutions worldwide.

一个“智能城市”的概念无处不在数据。但是，当今大多数城市数据都缺乏空间和时间分辨率，无法理解在秒或分钟的时间尺度上（例如污染物的分散）的过程。 更好地了解这些动态可以为居民，骑自行车的人或行人提供信息，他们可能希望在城市空间时使用空气质量数据。 该项目利用现有的街道家具，将空气质量和环境传感器整合到商业太阳能的网络废物站中。 嵌入在芝加哥和其他城市的Bigbelly废物站中的传感器将收集数据，这些数据将使研究人员能够探索必须理解的关键问题，以便开始制定和推动政策，测量策略以及与交通流量和空气质量之间的反馈回路相关的预测计算模型。与Bigbelly的合作关系与全球范围的近30,000个废物站提供了一个渠道，可以通过该渠道将传感器部署在许多城市中。该项目将计算机科学，网络物理系统，分布式系统和传感器系统专业知识汇集在一起​​，以探索技术和社会挑战，并最终与公共相关的公共相关的既有质量，并最终都能掌握公共范围的嵌入式系统。 嵌入在芝加哥和其他城市的Bigbelly废物站中的传感器将探索（1）城市峡谷中空气质量的空间和时间动态，从而告知了推动交通变化的传感器网络分辨率，以推动交通变化的政策，并向骑自行车的人和行人提供健康的空气质量路线信息； （2）城市拓扑（自然和建筑）如何影响这些动态和相关的所需测量解决方案。 这些是必须理解的关键问题，以便开始制定和推动与交通流量和空气质量之间的反馈回路有关的政策，测量策略以及预测计算模型。 关键挑战包括（1）有关传感器采样，原位处理和传输的电源管理； （2）确保数据质量； （3）提供对政策制定者和公众可以采取且可以理解的表格的数据。 所有数据将在近乎真实的时间内使用基于Web的分析工具，用于科学家，教育者，政策制定者和居民，以及用于应用程序开发的应用程序编程界面（API）。 通过开发开源，很容易部署的城市嵌入式系统基础设施利用广泛部署的商业平台，该项目可以在全球许多城市，国家公园和教育机构中启用科学，教育和外展。