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.

“智慧城市”的概念在数据中无处不在;然而，今天的大多数城市数据缺乏空间和时间分辨率，无法理解以秒或分钟为时间尺度的过程，例如污染物的扩散。 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中使用空气质量数据。 该项目利用现有的街道设施，将空气质量和环境传感器集成到商业太阳能供电的联网垃圾站中。 芝加哥和其他城市的BigBelly垃圾站中嵌入的传感器将收集数据，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与交通流量和空气质量之间的反馈回路相关的政策，测量策略和预测计算模型。BigBelly在全球拥有近30，000个垃圾处理站，与该公司的合作为传感器在许多城市的部署提供了一个渠道。该项目汇集了计算机科学、网络物理系统、分布式系统和传感器系统的专业知识，以探索城市规模的嵌入式系统在公共领域的技术和社会挑战与机遇，首先是了解并最终管理城市空气质量。 芝加哥和其他城市的BigBelly垃圾站中嵌入的传感器将探索（1）城市峡谷中空气质量的时空动态，为推动交通变化政策所需的传感器网络分辨率提供信息，并为骑自行车的人和行人提供健康的空气质量路由信息;以及（2）城市拓扑（自然和建筑）如何影响这些动态和相关的测量分辨率。 这些都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相关的政策、测量策略和预测计算模型。 关键挑战包括：（1）传感器采样、现场处理和传输方面的电源管理;（2）确保数据质量;（3）以可操作和可理解的形式向决策者和公众提供数据。 所有数据都将通过基于网络的分析工具以近实时的方式公布，供科学家、教育工作者、决策者和居民使用，并通过应用程序编程接口（API's）进行应用程序开发。 通过利用广泛部署的商业平台开发开源、易于部署的城市嵌入式系统基础设施，该项目可以在全球许多城市、国家公园和教育机构实现科学、教育和推广。