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ForestFireDetection

This repository contains the work and source files associated with the Master's thesis of Dorian Schrobiltgen. The project builds upon the previous work conducted in ForestMEv2-public.

Objective

The aim of this thesis is to design and evaluate a low-power, wireless sensor node capable of detecting the early signs of forest fires. The system combines gas sensing, RF communication and optimized embedded hardware/software to ensure early and reliable detection in forest environments, while minimizing the environmental impact of each end-node.

Project Structure

The repository is devided into four main parts:

Hardware

  • new_prototype/: Contains PCB designs, schematics, and Bill of Material (BoM) for the new hardware prototype.
  • test_antenna/: Designs and schematics related to PCBs used for antenna impedance testing.

Measurements

  • Gas_sensor/: Includes raw data, analysis script and plots related to CO_2 concentration profile testing.

  • Power_consumption/: Detailed power consumption for the initial and new prototype.

  • RF_network/: Results from RF performance tests performed using the antenna impedance testing PCBs

Simulations

  • Analysis/: A collection of Python scripts for power consumption modeling, supercapacitor sizing, and generation of plots used in the thesis report.
  • FWI/: Contains the optimized C implementation of the Fire Weather Index (FWI), along with a repository (by Reid Sawtell in pyFWI) used for validation.
  • LCA/: Life Cycle Assessment files used to quantify the environmental impact of each end-node.

Software

This directory contains the full STM32 projects used to program the boards. Each folder includes all necessary files to build, flash, and debug the corresponding prototype using STM32CubeIDE.

  • initial_prototype/: Complete project and source code of the optimized software for the initial hardware prototype.
  • new_prototype/: Complete project and source code of the optimized software for the new hardware prototype.