We present the development of a simulator that approximates the behaviour of a wireless network of temperature sensors deployed in the area affected by a wildfire. It is based on a novel signal processing approach in which the temperature experienced at a sensor due to a spreading fire front is modelled as the mixture of two-dimensional Gaussian distributions. We present the justification, development and validation of the proposed model. Our work enables the in-silico generation of synthetic, yet realistic, sensor temperature data sets. Due to the lack of real data, such data sets can be useful in deriving optimal sensor network deployment strategies for fire detection and progress monitoring in a specific area, under different scenarios for the expected wind conditions, and for calibrating fire spread prediction models.
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