Catlett C, Beckman P, Ferrier N, et al.Measuring Cities with Software-Defined Sensors.Journal of Social Computing, 2020, 1(1): 14-27.https://doi.org/10.23919/JSC.2020.0003
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.
University of Illinois Discovery Partners Institute, Chicago, IL60606, USA.
Northwestern University, Evanston, IL60208, USA.
University of Chicago, Chicago, IL60637, USA.
Northern Illinois University, DeKalb, IL60115, USA.
Argonne National Laboratory
Abstract
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.
M. Potosnak, P. Banerjee, R. Sankaran, R. Kotamarthi, R. Jacob, P. Beckman, and C. Catlett, Array of things: Characterizing low-cost air quality sensors for a city-wide instrument, presented at American Geophysical Union (AGU) Fall 2018 Meeting, 2018.
C. Catlett, P. Beckman, R. Sankaran, N. Ferrier, S. Park, and Y. Kim, Software-defined sensors: Using edge computing to revolutionize sensing, AGUFM, vol. 2019, p. IN34A-01, 2019.
C. Catlett, P. Beckman, R. Sankaran, and K. K. Galvin, Array of things: A scientific research instrument in the public way: Platform design and early lessons learned, in Proc. of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, 2017, pp. 26–33.
P. J. Napier, A. R. Thompson, and R. D. Ekers, The very large array: Design and performance of a modern synthesis radio telescope, Proceedings of the IEEE, vol. 71, no. 11, pp. 1295–1320, 1983.
C. Catlett, P. Beckman, R. Sankaran, and K. K. Galvin, Array of things: A scientific research instrument in the public way: Platform design and early lessons learned, in Proc. of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, 2017, pp. 26–33.
P. Beckman, R. Sankaran, C. Catlett, N. Ferrier, R. Jacob, and M. Papka, Waggle: An open sensor platform for edge computing, presented at 2016 IEEE SENSORS, Orlando, FL, USA, 2016, pp. 1–3.
P. Beckman, C. Catlett, I. Altintas, E. Kelly, S. Collis, N. Ferrier, M. Papka, J. Olds, D. Reed, and R. Sankaran, Sage: Cyberinfrastructure for AI at the edge, https://sagecontinuum.org/, 2019.
J. D. Marshall, E. Nethery, and M. Brauer. Within-urban variability in ambient air pollution: Comparison of estimation methods, Atmospheric Environment, vol. 42, no. 6, pp.1359–1369, 2008.
D. Schwela, Air pollution and health in urban areas, Reviews on Environmental Health, vol. 15, nos. 1 &2, pp. 13–42, 2000.
[15]
T. Münzel, T. Gori, W. Babisch, and M. Basner, Cardiovascular effects of environmental noise exposure, European Heart Journal, vol. 35, no. 13, pp. 829–836, 2014.
J. P. Bello, C. Silva, O. Nov, R. L. DuBois, A. Arora, J. Salamon, C. Mydlarz, and H. Doraiswamy, Sonyc: A system for monitoring, analyzing, and mitigating urban noise pollution, Communications of the ACM, vol. 62, no. 2, pp. 68–77, 2019.
B. Goldstein and L. Dyson, Beyond Transparency: Open Data and the Future of Civic Innovation. Code for America Press, 2013.
[20]
T. J. L. Van Rompay, D. J. Vonk, and M. L. Fransen, The eye of the camera: Effects of security cameras on prosocial behavior, Environment and Behavior, vol. 41, no. 1, pp. 60–74, 2009.
A. Adams, K. Avila, J. Basney, D. Brunson, R. Cowles, J. Dopheide, T. Fleury, E. Heymann, F. Hudson, C. Jackson, et al., Trusted ci experiences in cybersecurity and service to open science, in Proc. of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning), New York, NY, USA, 2019, pp. 1–8.
P. Beckman, C. Catlett, I. Altintas, E. Kelly, and S. Collis, Mid-scale RI-1: Sage: A software-defined sensor network (NSF OAC 1935984), https://sagecontinuum.org/, 2019.
[30]
S. C. Van Hedger, H. C. Nusbaum, S. L. M. Heald, A. Huang, H. P. Kotabe, and M. G. Berman, The aesthetic preference for nature sounds depends on sound object recognition, Cognitive Science, 2019, vol. 43, no. 5, p. e12 734, 2019.
Y. Suhara, Y. Xu, and A. Pentland, Deepmood: Forecasting depressed mood based on self-reported histories via recurrent neural networks, in Proc. of the 26th International Conference on World Wide Web, 2017, pp. 715–724.
[32]
Y. Yuan and A. Alabdulkareem, An interpretable approach for social network formation among heterogeneous agents, Nature Communications, vol. 9, no. 1, p. 4704, 2018.
A. Zimprich, L. Garrett, J. M. Deussing, C. T. Wotjak, H. Fuchs, V. Gailus-Durner, M. H. de Angelis, W. Wurst, and S. M. Hölter, A robust and reliable non-invasive test for stress responsivity in mice, Frontiers in Behavioral Neuroscience, vol. 8, p. 125, 2014.
K. R. Lezak, G. Missig, and W. A. Carlezon Jr, Behavioral methods to study anxiety in rodents, Dialogues in Clinical Neuroscience, vol. 19, no. 2, p. 181, 2017.
L. Bettencourt, J. Lobo, D. Helbing, C. Kühnert, and G. West, Growth, innovation, scaling, and the pace of life in cities, Proceedings of the National Academy of Sciences, vol. 104, no. 17, pp. 7301–7306, 2007.
H. J. M. Steeneken and J. H. L. Hansen, Speech under stress conditions: Overview of the effect on speech production and on system performance, presented at 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No. 99CH36258), volume 4, 1999, pp. 2079–2082.
[39]
L. A. Streeter, N. H. Macdonald, W. Apple, R. M. Krauss, and K. M. Galotti, Acoustic and perceptual indicators of emotional stress, The Journal of the Acoustical Society of America, vol. 73, no. 4, pp. 1354–1360, 1983.
S. Wang, X. Zhang, Y. Zhang, L. Wang, J. Yang, and W. Wang, A survey on mobile edge networks: Convergence of computing, caching and communications, IEEE Access, vol. 5, pp. 6757–6779, 2017.
M. G. Berman, J. Jonides, and S. Kaplan, The cognitive benefits of interacting with nature, Psychological Science, vol. 19, no. 12, pp. 1207–1212, 2008.
M. G. Berman, O. Kardan, H. P. Kotabe, H. C. Nusbaum, and S. E. London, The promise of environmental neuroscience, Nature Human Behaviour, vol. 3, pp. 414–417, 2019.
E. Stern and J. Portugali, Environmental cognition and decision making in urban navigation, in Wayfinding Behavior: Cognitive Mapping and Other Spatial Processes, R. G. Golledge, ed. Washingion, DC, USA: JHU Press, 1999, pp. 99–119.
[45]
H. Kotabe, O. Kardan, and M. G. Berman, Can the high-level semantics of a scene be preserved in the low-level visual features of that scene? A study of disorder and naturalness, presented at the 38th Annual Meeting of the Cognitive Science, Philadelphia, PA, USA, 2016.
[46]
KotabeH., KardanO., and BermanM. G., The nature-disorder paradox: A perceptual study on how nature is disorderly yet aesthetically preferred, Journal of Experimental Psychology: General, vol. 146, no. 8, p. 1126, 2017.
A. Coburn, O. Kardan, H. Kotabe, J. Steinberg, M. Hout, A. Robbins, J. MacDonald, G. Hayn-Leichsenring, and M. G. Berman, Psychological responses to natural patterns in architecture, Journal of Environmental Psychology, vol. 62, pp. 133–145, 2019.
K. Schertz, S. Sachdeva, O. Kardan, H. Kotabe, K. Wolf, and M. G. Berman, A thought in the park: The influence of naturalness and low-level visual features on expressed thoughts, Cognition, vol. 174, pp. 82–93, 2018.
K. Schertz and M. G. Berman, Understanding nature and its cognitive benefits, Current Directions in Psychological Science, vol. 28, no. 5, pp. 496–502, 2019.
P. J. Brantingham, M. Valasik, and G. O. Mohler, Does predictive policing lead to biased arrests? Results from a randomized controlled trial, Statistics and Public Policy, vol. 5, no. 1, pp. 1–6, 2018.
T. R. Meyer, D. Balague, M. Camacho-Collados, H. Li, K. Khuu, P. J. Brantingham, and A. L. Bertozzi, A year in madrid as described through the analysis of geotagged twitter data, Environment and Planning B: Urban Analytics and City Science, vol. 46, no. 9, pp. 1724–1740, 2019.
Z. Y. Meng, J. Sánchez, J. M. Morel, A. L. Bertozzi, and P. J. Brantingham, Ego-motion classification for body-worn videos, presented at International Conference on Imaging, Vision and Learning based on Optimization and PDEs, Cham, Germany, 2016, pp. 221–239.
[53]
K. Potdar and P. Torrens, Modelling spatio-temporal patterns in pedestrian behavior at the edge with Jetson SOMs, presented at NVIDIA 2019 GPU Technology Conference (GTC), Washington, DC, USA, 2019.
A. M. Wenner, Sound production during the waggle dance of the honey bee, Animal Behaviour, vol. 10, nos. 1 &2, pp. 79–95, 1962.
[56]
V. Forgione and C. Catlett, Documentation for the plenario api to access time series sensor data from the array of things and other waggle platform deployments, https://plenario.docs.apiary.io/#, 2018.
[57]
S. P. Mackinnon, C. H. Jordan, and A. E. Wilson, Birds of a feather sit together: Physical similarity predicts seating choice, Personality and Social Psychology Bulletin, vol. 37, no. 7, pp. 879–892, 2011.
C. Catlett, P. Beckman, K. Cagney, D. Work, and M. Papka, MRI: Development of an urban-scale instrument for interdisciplinary research, Report NSF 1532133, US National Science Foundation, Alexandria, VA, USA, 2015.
The AoT project was funded by the US National Science Foundation’s Major Research Instrumentation program supporting the development of novel instruments (No. NSF-1532133)[60]. Scientists from the University of Chicago, Northwestern University, Argonne National Laboratory, Northern Illinois University, and the University of Illinois Discovery Partners Institute led the project. AoT would not have been possible without the support from the City of Chicago Mayor’s Office, Department of Transportation (including cost-sharing in the form of installation and electrical power), and Department of Innovation and Technology. Cost-sharing was provided by the City of Chicago, the University of Chicago, Northwestern University, Northern Illinois University, Intel, Microsoft, AT&T, Cisco, Crown Castle Communications, and Schneider Electric. The Waggle platform design was supported through Argonne National Laboratory’s Laboratory-Directed Research and Development program. Social Science research utilizing AoT is funded in part through NSF (No. S&CC-1952050). The SAGE project is funded through the US National Science Foundation’s Mid-Scale Research Infrastructure program (No. NSF-OAC-1935984)[29].
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