We describe the work behind a privacy-preserving, crowdsensing approach that promotes social distancing upon the return of students to University. Our main motivation is enabling visualizations that predict room occupancy based on the number of connected devices to particular access points, via anonymous reports about these predictions, and via an unenforced booking system that allows users to communicate their intents about room use.
Tumedei G., Boschi F., Prandi C., Gomes L., Calheno R., Abreu R., et al. (2021). Promoting a safe return to university campuses during the COVID-19 pandemic: Crowdsensing room occupancy. Association for Computing Machinery, Inc [10.1145/3462203.3475911].
Promoting a safe return to university campuses during the COVID-19 pandemic: Crowdsensing room occupancy
Tumedei G.;Prandi C.;
2021
Abstract
We describe the work behind a privacy-preserving, crowdsensing approach that promotes social distancing upon the return of students to University. Our main motivation is enabling visualizations that predict room occupancy based on the number of connected devices to particular access points, via anonymous reports about these predictions, and via an unenforced booking system that allows users to communicate their intents about room use.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
