Prototyping a Scalable and Evolvable Urban Sensing Platform for Smart Cities

Abstract: 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.