Improved spatial organisation of sensor networks to reduce wildfire impact

Abstract

Wildfires are particularly dangerous in areas where communities colocate with regions of dense vegetation. Early detection helps minimise response time and community impact, with networks of wireless sensors widely accepted as the best available early warning solution. However, financial constraints often cause sensors to be spatially distributed in a sparse and random (or pseudouniform) manner. This paper presents a new approach to sensor placement by employing maps of wildfire impact. Such maps pinpoint ignition loci that lead to more destructive fires and hence, locations where early identification is essential. We leverage IBM evacuation planner (EVA) to generate these maps from a pipeline of simulation components including: fire progression, evacuee behaviour and traffic simulation. Accordingly, these yield insights into potential community impact, and from them, we propose and evaluate two algorithms for sensor placement. The effectiveness of our approach is demonstrated through a case study in Mount Dandenong, Victoria, Australia.