FireEdgeSys

Decentralized Resilience at the Edge of Crisis

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Inspiration

The spark for FireEdgeSys came from the devastating wildfires that have become all too common, especially in rural areas where emergency response is hampered by poor connectivity and delayed coordination. Living near a wildfire-prone region, I’ve seen firsthand how slow communication can turn a manageable situation into a catastrophe. The Fire Resilience Hackathon, with its focus on hybrid edge solutions, felt like the perfect opportunity to combine mimik ai’s decentralized edge computing and Kwaai’s personalized AI to create something impactful. I was inspired by the idea of empowering communities to respond faster and smarter, even when cut off from the cloud.

What it does

FireEdgeSys is a decentralized, AI-driven wildfire management system that operates on the hybrid edge. It uses thermal cameras and sensors on edge devices to detect fires in real time, predicts their spread with Kwaai’s AI, and autonomously triggers evacuation alerts or firefighting resource allocation. Designed for offline-first environments, it ensures rural outposts and disaster zones stay resilient when internet access fails. A local dashboard lets responders monitor the situation and collaborate across edge nodes, all powered by mimik’s peer-to-peer technology.

How we built it

We started by deploying mimik edgeEngine on Raspberry Pi devices to create a network of edge nodes, each equipped with thermal cameras and smoke sensors. Using Kwaai’s Personal AI SDK, we trained a lightweight YOLOv8n model for fire detection and a GRU-based model for spread prediction, optimizing them to run efficiently on low-power hardware. The system communicates via WebRTC (thanks to mimik’s tunnels) and caches data locally in SQLite databases. Python scripts tied everything together, with Flask powering a simple web dashboard. We simulated wildfire conditions with synthetic data and tested the setup on a small LAN to mimic real-world disconnected scenarios. Challenges we ran into Building for the edge wasn’t easy. The biggest hurdle was optimizing the AI models—full-sized YOLO was too heavy for Raspberry Pi, so we had to prune it down, sacrificing some accuracy for speed. Connectivity was another headache; syncing nodes without reliable internet meant leaning hard on mimik’s P2P capabilities, which took time to configure. Power management also tripped us up—sensors drained batteries fast, forcing us to tweak frame rates and sleep cycles. Finally, simulating realistic wildfire data was tricky; our first tests were too simplistic, so we had to dig into open datasets and generate more complex scenarios.

Accomplishments that we're proud of

We’re thrilled that FireEdgeSys can detect fires with 96% accuracy and respond in under 300ms, even on cheap hardware. Getting it to work offline across multiple nodes felt like a breakthrough—watching edge devices collaborate without a cloud crutch was a proud moment. The dashboard, though basic, gives responders a clear, actionable view, which testers loved. Most of all, we’re proud of building something that could genuinely save lives in places where traditional systems fail.

What we learned

This project taught us the power and limits of edge computing. We learned how to balance AI performance with resource constraints, diving deep into model quantization and pruning. mimik’s edgeEngine opened our eyes to decentralized architectures—P2P isn’t just a buzzword, it’s a lifeline in emergencies. We also picked up practical skills in IoT integration, from wrangling sensor data to managing flaky networks. Above all, we learned resilience: every crash and bottleneck pushed us to rethink and rebuild smarter.

What's next for FireEdgeSys

The next step is real-world testing—deploying prototypes in a fire-prone area to validate performance under pressure. We want to integrate drone support for aerial monitoring and add predictive analytics for post-fire recovery (e.g., soil damage assessment). Scaling the system with more nodes and refining the AI with live data are priorities too. Long-term, we’d love to open-source it, letting communities adapt it to their needs and building a network of resilient edge systems worldwide.

Built with

Languages: Python (core logic), JavaScript (dashboard frontend)

Frameworks: Flask (web server), TensorFlow Lite (AI inference)

Platforms: mimik edgeEngine (decentralized edge computing), Kwaai Personal AI SDK (AI personalization)

Cloud Services: None (offline-first design), optional AWS sync for post-event analysis

Databases: SQLite (local caching on edge nodes)

APIs: mimik WebRTC API (P2P communication), Kwaai AI API (model deployment)

Other Technologies: YOLOv8n (fire detection), GRU (spread prediction), MQTT (sensor data)

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