A WiFi-based occupancy sensing, modelling, and simulation method to ensure COVID‑19 ventilation and social distancing norms at workplaces
As provinces are planning for return-to-work, a new challenge that we will face is to practice safe social distancing indoors. Policies to define acceptable occupant densities and interior design configurations are needed. Unlike the safe distancing policies for outdoors, these policies should also consider per-person ventilation requirements to minimize the risk of exposure to infectious aerosols. These policies need to be developed upon high resolution measured occupancy data from shared office spaces. To this end, occupancy sensing solutions offer an untapped opportunity to develop policy recommendations and to regulate these policies.
In this project, a team of researchers will partner with two firms specialized in occupancy sensing solutions and study high-resolution occupancy data from real office buildings. The team will develop an agent-based occupancy modelling approach to simulate indoor occupancy patterns for a variety of office layouts. The results of this analysis will lead to policy recommendations for occupant densities and an online interactive tool to simulate occupancy patterns for different occupant densities and office layouts. The team will also conduct a case study demonstrating the use of WiFi-based occupancy sensing technology to regulate occupant density policies in an administrative office building at Carleton University.
The proposed research will contribute to our efforts to restart the economy while minimizing the risk of new outbreaks starting at workplaces. The methods developed, once adopted by our partners, will contribute to our knowledge-based economy. One postdoctoral researcher and one Ph.D. student will conduct multidisciplinary research and develop widely sought-after skills in data mining, visualization, and occupant modelling and simulation.
