WiFi Sensing System Deployment in Vehicular Tunnels for Environmental Safety Monitoring

Traffic infrastructure safety is vital to society, and critical traffic corridor breakdowns could halt cities. Particularly, vehicular tunnels are notable due to the constrained space and challenges to emergency response. Existing tunnel monitoring solutions, such as cameras and thermal sensors, are prone to false positives and usually expensive. To address these problems, we investigate the efficacy of Internet of Things (IoT) enabled WiFi sensing technology for environmental safety monitoring using versatile, low-cost embedded devices. As a proof-of-concept, we deployed our WiFi sensing devices in an urban underwater tunnel to monitor the tunnel temperature and fire accidents. Specifically, we set up one long-term deployment for temperature monitoring and conducted two fire detection experiments in the tunnel using a small-scaled bonfire and an actual vehicle fire. Our experiments show that the proposed WiFi sensing system could accurately monitor the ambient tunnel temperature to an error of ${\leq } 0.4~^{\circ }{\mathrm { C}}$ during the long-term deployment. In the small-scaled bonfire experiment, we developed a novel empirical model to characterize fire intensity using WiFi signals. This proposed linear regression model has a coefficient of determination of 0.76. Finally, we validate the potential of our wireless environment vision system for detecting fire events from ambient environments using a low-complexity machine learning classifier enabling in situ processing locally on IoT devices.