CompDrone: Towards Integrated Computational Model and Social Drone Based Wildfire Monitoring

Forest fires cause irreversible damages worldwide every year. Monitoring wildfire propagation is thus a vital task in mitigating forest fires. While computational model-based wildfire prediction methods provide reasonable accuracy in monitoring wildfire behavior, they are often limited due to the lack of constant availability of real-time meteorological data. In contrast, social-media-driven drone sensing (SDS) is emerging as a new sensing paradigm that detects the early signs of forest fires from online social media feeds and drives the drones for reliable sensing. However, due to the scarcity of social media data in remote regions and limited flight times of drones, SDS solutions often underperform in large-scale forest fires. In this paper, we present CompDrone, a wildfire monitoring framework that exploits the collective strengths of computational wildfire modeling and SDS for reliable wildfire monitoring. Two critical challenges exist to integrate computational modeling and SDS together: i) limited availability of social signals in the regions of a forest fire; and ii) predicting the regions of fire where the drones should be dispatched to. To solve the above challenges, the CompDrone framework leverages techniques from cellular automata, constrained optimization, and game theory. The evaluation results using a real-world wildfire dataset show that CompDrone outperforms the state-of-the-art schemes in effectively predicting wildfire propagation.