DragonFly: Drone-Assisted High-Rise Monitoring for Fire Safety

In this paper, we propose DragonFly, a drone-based data collection framework to enhance real-time situational awareness in high-rise buildings, focusing specifically on mission-critical high-rise fire scenarios. The goal of our proposed solution is to use multiple drones with visual sensors to collect reliable and timely data for monitoring the exterior of a high-rise building. Drones are especially useful in obtaining data from hard-to-access regions in high-rise fires that are used to monitor fire/smoke that might have propagated to higher floors, detect the presence of humans requiring assistance near windows, and determine window open/close states which can have a significant impact on the speed and direction of fire spread. Given a dynamically evolving set of events and multiple drones, the core challenge addressed is to develop a plan for multiple drones to gather a set of observations that can improve both the coverage (identify more events) and accuracy (obtain fine-grained for improved event detection). We develop a solution for the Multi-drone Waypoint scheduling problem (NP-hard) in two steps: 1) allocation of monitoring tasks (AMT) to individual drones and 2) dynamic waypoint scheduling (DWS) that determines the waypoint sequence for each drone to visit. We evaluate our proposed approach using a simulated high-rise fire scenario with a realistic fire spread model and study the applicability and efficiency of the proposed algorithms compared to baseline techniques. The simulation results demonstrate the superior performance of the proposed AMT-DWS algorithms. DragonFly achieve 33% fewer missing events and up to 39 times gain in accuracy, captured as the minimum weighted AUC (Area Under Curve) as compared to baseline algorithms. DragonFly delivers over 85% missing events and about 1.3 times the minimum weighted AUC in comparison to current approaches.