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

Abstract

Mobile sensing has played a key role in providing digital solutions to aid with COVID-19 containment policies, primarily to automate contact tracing and social distancing measures. As more and more countries reopen from lockdowns, there remains a pressing need to minimize crowd movements and interactions, particularly in enclosed spaces. Many COVID-19 technology solutions leverage positioning systems, generally using Bluetooth and GPS, and can theoretically be adapted to monitor safety compliance within dedicated environments. However, they may not be the ideal modalities for indoor positioning. This paper conjectures that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions monitor and maintain safety compliance according to the public health guidelines. Using smartphones as a proxy for user location, our analysis demonstrates how coarse-grained WiFi data can sufficiently reflect the indoor occupancy spectrum when different COVID-19 policies were enacted. Our work analyzes staff and students’ mobility data from three university campuses. Two of these campuses are in Singapore, and the third is in the Northeastern United States. Our results show that online learning, split-team, and other space management policies effectively lower occupancy. However, they do not change the mobility for individuals transitioning between spaces. We demonstrate how this data source can be a practical application for institutional crowd control and discuss the implications of our findings for policy-making.

Impact of COVID-19 Pandemic Measures and Restrictions on Cellular Network Traffic in Malaysia

“…However, they may not be the ideal modalities for indoor positioning. The study [32] conjectures that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions monitor and maintain safety compliance according to the public health guidelines. Using smartphones as a proxy for user location, their analysis demonstrated how coarse-grained WiFi data can sufficiently reflect the indoor occupancy spectrum when different COVID-19 policies were enacted.…”

User-Centric Proximity Estimation Using Smartphone Radio Fingerprinting

“…Proximity/occupancy approaches can be efficiently exploited to count the number of people in a particular area, to identify them and track movement patterns. In [ 52 ], the movement patterns of people indoors, based on smartphone WiFi data, was captured. Similarly, the authors in [ 53 ] used a WiFi dataset to track the flow of people through buildings.…”

“…Similarly, the authors in [ 53 ] used a WiFi dataset to track the flow of people through buildings. During the pandemic, IoT sensors were used to measure human density, monitor crowd movement, and observe facility usage for crowd monitoring [ 52 ]. A mobile crowd monitoring application, which used data from occupancy sensors, cameras, and ticket validation to determine human density in specific areas, was tested in [ 54 ].…”

Simplicial temporal networks from Wi-Fi data in a university campus: The effects of restrictions on epidemic spreading

“…The work in [30] analyzes user occupancy and mobility via deployed WiFi infrastructure to help institutions to monitor and maintain safety compliance according to the public health guidelines. While the overall motivations and goals of this work are very similar to ours, there are some important differences with regard to the metrics derived from WiFi data.…”