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Mobile sensing has played a key role in providing digital solutions to aidwith COVID-19 containment policies. These solutions include, among otherefforts, enforcing social distancing and monitoring crowd movements in indoorspaces. However, such solutions may not be effective without mass adoption. Asmore and more countries reopen from lockdowns, there remains a pressing need tominimize crowd movements and interactions, particularly in enclosed spaces.This paper conjectures that analyzing user occupancy and mobility via deployedWiFi infrastructure can help institutions monitor and maintain safetycompliance according to the public health guidelines. Using smartphones as aproxy for user location, our analysis demonstrates how coarse-grained WiFi datacan sufficiently reflect indoor occupancy spectrum when different COVID-19policies were enacted. Our work analyzes staff and students' mobility data fromthree different university campuses. Two of these campuses are in Singapore,and the third is in the Northeastern United States. Our results show thatonline learning, split-team, and other space management policies effectivelylower occupancy. However, they do not change the mobility for individualstransitioning between spaces. We demonstrate how this data source can be put topractical application for institutional crowd control and discuss theimplications of our findings for policy-making.

Analyzing the Impact of Covid-19 Control Policies on Campus Occupancy and Mobility via Passive WiFi Sensing