| Autor: |
Charlie Catlett, Pete Beckman, Nicola Ferrier, Howard Nusbaum, Michael E. Papka, Marc G. Berman, Rajesh Sankaran |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Journal of Social Computing, Vol 1, Iss 1, Pp 14-27 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2688-5255 |
| DOI: |
10.23919/JSC.2020.0003 |
| Popis: |
The Chicago Array of Things (AoT) project, funded by the US National Science Foundation, created an experimental, urban-scale measurement capability to support diverse scientific studies. Initially conceived as a traditional sensor network, collaborations with many science communities guided the project to design a system that is remotely programmable to implement Artificial Intelligence (AI) within the devices—at the "edge" of the network—as a means for measuring urban factors that heretofore had only been possible with human observers, such as human behavior including social interaction. The concept of "software-defined sensors" emerged from these design discussions, opening new possibilities, such as stronger privacy protections and autonomous, adaptive measurements triggered by events or conditions. We provide examples of current and planned social and behavioral science investigations uniquely enabled by software-defined sensors as part of the SAGE project, an expanded follow-on effort that includes AoT. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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