Since its announcement last summer, the Array of Things (AoT) urban sensing project has been gradually refining its technology and strategy for its expected pilot launch this spring. Working towards the goal of installing hundreds of sensor nodes on Chicago streetlights to collect information about the environment, infrastructure, and activity of the city, the AoT researchers and designers -- at the CI's Urban Center for Computation and Data (UrbanCCD), School of the Art Institute of Chicago, and Argonne National Laboratory -- have reshaped the external and internal components of the boxes for the initial deployment. Meanwhile, the project formed new partnerships with scientists, industrial partners, and cities to expand the scope and mission of this ambitious project.
For the February edition of Inside the Discovery Cloud, project leaders Charlie Catlett and Douglas Pancoast provided the latest update on the status of AoT, tackling the design, community engagement, computer architecture, and scientific aspects of the project. In the first talk, Pancoast explains how they designed the AoT nodes to be interactive and aesthetically-pleasing members of the urban environment, with customizable faces, soft curves, and LED lighting capable of communicating useful information to passersby. Pancoast also talks about early efforts to engage Chicago residents in directing the types of sensors installed in the nodes, the kinds of research questions that will be asked, and how to get the most value out of the data the project will generate.
In the second talk, CI Senior Fellow and UrbanCCD Director Charlie Catlett takes us inside the AoT nodes to look at the innovative computational technology they will use, including the Waggle remote sensing architecture developed by Argonne scientists Peter Beckman and Rajesh Sankaran and the router, controller and server devices that will make the nodes tick. Catlett also describes a new partnership with wastecan manufacturer Bigbelly, lays out the new schedule for AoT rollout in Chicago, and discusses a use case for AoT data in helping residents find walking routes with the cleanest air.