Sentinel Forest Network

Huge problem:

Wildfires represent a global environmental and socio-economic catastrophe, growing in frequency and scale due to climate change. Current management systems are largely reactive; by the time a fire is detected by traditional means, it has often reached a size that is nearly impossible to contain, leading to massive ecological damage, CO2 emissions, and loss of life.

Radical solution:

The Sentinel Forest Network shifts the paradigm from suppression to absolute prevention. It proposes an integrated, AI-enabled multi-agent system designed to anticipate, detect, and neutralize every forest fire before it can spread.

By deploying a dense grid of ground-based "FireBoxes" alongside autonomous drones and satellite monitoring, the system creates a continuous surveillance shield. This allows for the immediate, autonomous deployment of localized suppression measures at the exact moment of ignition, effectively making large-scale wildfires a thing of the past.

Breakthrough technology:

The project utilizes the "FireBox" unit: an autonomous node equipped with a multi-sensor array (CO2, temperature, and humidity) and long-range communication (LoRaWAN). These units use machine learning to differentiate between false alarms and actual ignitions. When a fire is confirmed, the system triggers a coordinated response between ground rovers and aerial drones for precision-targeted suppression using eco-friendly retardants.

Team:

Erdfarb Alex Stephan, Hung Hsiang-Yu, Ji Chu, Shastri Aniruddh. (Mentors: Carlos Gonzalez De Miguel, Andreas Sumper).