Modeling and Analysis of Wildfire Detection using Wireless Sensor Network with Poisson Deployment

TL;DR

This paper models wildfire detection using a wireless sensor network with sensors randomly deployed via a Poisson process, analyzing detection probability and system performance under various conditions to improve early fire detection.

Contribution

It introduces a novel framework combining Poisson deployment modeling and fire propagation analysis for wireless sensor networks in forest fire detection.

Findings

Sensor density requirements depend on fire spread dynamics.

Wind velocity impacts detection performance but not always negatively.

Poisson deployment effectively models sensor placement in forests.

Abstract

In many forest fire incidences, late detection of the fire has lead to severe damages to the forest and human property requiring more resources to gain control over the fire. An early warning and immediate response system can be a promising solution to avoid such massive losses. This paper considers a network consisting of multiple wireless sensors randomly deployed throughout the forest for early prompt detection of fire. We present a framework to model fire propagation in a forest and analyze the performance of considered wireless sensor network in terms of fire detection probability. In particular, this paper models sensor deployment as a Poisson point process (PPP) and models the forest fire as a dynamic event which expands with time. We also present various insights to the system including required sensor density and impact of wind velocity on the detection performance. We show that larger wind velocity may not necessarily imply bad sensing performance or the requirement of a denser deployment.