Acoustic Guardian: Real-Time AI Forest Threat Detection with BLE Alert System
Full build + code: Hackster project → https://www.hackster.io/bhushan-gopala-reddy/acoustic-guardian-26dca2
GitHub repo (firmware, schematics, BLE integration) → https://github.com/bhushan017/acoustic-guardian-thingy53
Why we built it
Forest rangers struggle with real-time threat detection across vast, remote areas. Illegal logging and poaching activities often go undetected for hours or days, allowing significant damage before response teams can intervene. Traditional acoustic monitoring systems cost $2000+ and require complex setup, making them inaccessible for many conservation organizations. Acoustic Guardian addresses this gap with an affordable, field-ready solution that provides immediate threat alerts via Bluetooth Low Energy.
How it works
Sensor Node (deployed in forest) A compact, battery-powered device featuring:
- VM3011 microphone optimized for outdoor acoustic capture with wind resistance
- Edge Impulse ML model running locally on Nordic nRF5340 for real-time threat detection
- Multi-threaded architecture handling audio capture, ML inference, and alert transmission simultaneously
- BLE advertising broadcasts threat alerts immediately without requiring connections
- 30+ hour battery life for extended forest deployment
Alert System When threats are detected:
- <200ms latency from audio detection to BLE alert transmission
- 30-second burst pattern with high-frequency advertising for reliable delivery
- Threat data payload includes type (gunshot/chainsaw), confidence level, timestamp, and sensor ID
- Range testing validated at 100m+ in forest conditions
Mobile Detection Rangers using smartphones with nRF Connect or custom apps can:
- Receive immediate alerts when within BLE range of triggered sensors
- View threat details including confidence levels and detection timestamps
- Track multiple sensors across forest deployment areas
What makes it different
Runs without infrastructure: No cellular, WiFi, or internet required—pure BLE broadcasting Edge AI processing: 87.1% accuracy detecting chainsaws and gunshots using on-device ML Cost-effective deployment: $200 total cost vs $2000+ commercial alternatives Open and reproducible: Complete hardware designs, firmware, and ML models published Field-tested reliability:
- Optimized power management for extended battery life
- Weather-resistant outdoor operation (-20°C to +60°C)
- False positive rate <2% with proper threshold tuning
What you can do with it (beyond gunshots)
Illegal logging detection: The ML model already detects chainsaw sounds with high accuracy Species monitoring: Retrain the Edge Impulse model for endangered species calls or biodiversity surveys Human-wildlife conflict: Detect vehicle engines or human voices near protected boundaries Research applications: Long-term acoustic data collection for ecosystem health monitoring
We include detailed documentation for retraining the Edge Impulse model with your own acoustic datasets, making customization straightforward for different environments and use cases.
Field notes and trade-offs
Power: Current design provides 30+ hours continuous operation; solar charging integration planned for permanent installations Range: BLE alerts effective to 100m+ in forest; multiple sensors can create coverage networks Environmental factors: Wind and rain can affect microphone sensitivity; weatherproof enclosure tested in outdoor conditions False positives: Any acoustic ML model can trigger incorrectly—collect local environmental sounds to fine-tune thresholds Privacy considerations: System focuses on threat-specific sounds, but consider data privacy protocols when deploying near communities
What we are asking from the WILDLABS community
- Field validation: Does this approach fit your terrain, patrol patterns, and operational workflows?
- Acoustic datasets: What environmental sounds or specific threat signatures should we include for your deployment sites?
- Hardware optimization: Feedback on enclosure design, mounting methods, or power strategies that work in your field conditions?
- Integration needs: Would you prefer data export to existing platforms (EarthRanger, custom databases, ranger communication systems)?
- Deployment scaling: How would multiple sensor networks best serve your conservation area coverage?
Please engage in the comments or open a GitHub issue with your feedback, deployment constraints, and adaptation ideas. We're especially interested in hearing from teams working in challenging acoustic environments or with specific threat detection requirements.
Try it or adapt it
Proof of concept: Start with a single unit to validate threat detection accuracy in your specific environment Local model training: Capture site-specific audio samples and retrain the Edge Impulse model for your soundscape Network deployment: Deploy multiple sensors for area coverage, with rangers carrying BLE-enabled devices for alert reception Extend functionality: Add environmental sensors, GPS tracking, or integration with existing ranger communication systems
[Include photos of your device, forest deployment, nRF Connect app screenshots, system diagrams]
Edge AI Earth Guardians Contest (Hackster.io)
I'm sharing Acoustic Guardian as part of the EarthGuardians challenge on Hackster.io. This contest focuses on building practical, open-source solutions that help protect ecosystems and empower conservation communities. Acoustic Guardian embodies this mission by combining edge AI, real-time processing, and cost-effective design into a deployable, field-ready system.
If you're interested in the broader conservation tech community effort, please check out the contest page. I'd love your feedback on how this acoustic monitoring approach could be adapted to real conservation workflows—whether for anti-poaching, illegal logging detection, species monitoring, or early threat response systems.
Thanks to the Edge Impulse community for ML tooling, Nordic Semiconductor for excellent development support, and the Zephyr RTOS community for embedded systems frameworks. And thanks in advance to the WILDLABS community for field-hardened feedback and collaboration opportunities.
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