EcoWild: Reinforcement Learning for Energy-Aware Wildfire Detection in Remote Environments

TL;DR

EcoWild is a solar-powered system using reinforcement learning to detect wildfires in remote areas while managing energy efficiently.

Contribution

EcoWild introduces a novel energy-adaptive wildfire detection system using reinforcement learning for sustainable operation in remote environments.

Findings

EcoWild achieves 2.4× to 7.7× faster wildfire detection compared to static baselines.

The system avoids battery depletion across 125 deployment scenarios while maintaining responsiveness.

It uses a reinforcement learning agent to dynamically adjust sensing and communication strategies.

Abstract

Early wildfire detection in remote areas remains a critical challenge due to limited connectivity, intermittent solar energy, and the need for autonomous, long-term operation. Existing systems often rely on fixed sensing schedules or cloud connectivity, making them impractical for energy-constrained deployments. We introduce EcoWild, a reinforcement learning-driven cyber-physical system for energy-adaptive wildfire detection on solar-powered edge devices. EcoWild combines a decision tree-based fire risk estimator, lightweight on-device smoke detection, and a reinforcement learning agent that dynamically adjusts sensing and communication strategies based on battery levels, solar input, and estimated fire risk. The system models realistic solar harvesting, battery dynamics, and communication costs to ensure sustainable operation on embedded platforms. We evaluate EcoWild using real-world solar, weather, and fire image datasets in a high-fidelity simulation environment. Results show that EcoWild consistently maintains responsiveness while avoiding battery depletion under diverse conditions. Compared to static baselines, it achieves 2.4× to 7.7× faster detection, maintains moderate energy consumption, and avoids system failure due to battery depletion across 125 deployment scenarios.

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