Skip to main content

Explanation

System Architecture Overview​

Problem Context​

Traditional animal ecology fieldwork faces fundamental operational constraints that limit research effectiveness:

  • Manual Data Collection: Human-operated protocols introduce systematic quality degradation and require continuous field personnel deployment
  • Network Limitations: Remote study sites operate beyond conventional network infrastructure, precluding real-time monitoring capabilities
  • Single-Modal Systems: Existing sensing platforms remain predominantly single-modal, requiring multiple independent hardware deployments and increasing complexity
  • Post-Processing Paradigm: Traditional methodologies sacrifice temporal resolution through delayed data processing workflows
  • Operational Discontinuity: Repeated battery replacement, manual sensor maintenance, and physical data retrieval create vulnerability to data corruption and storage limitations

Solution Design​

The Smart Backpack System addresses these challenges through an integrated autonomous monitoring framework that combines:

  1. Edge Computing Infrastructure: Local processing eliminates dependency on network connectivity
  2. Multimodal Sensing: Unified platform integrating ground and aerial observation capabilities
  3. Autonomous Coordination: Software-driven orchestration eliminates human intervention requirements
  4. Real-Time Processing: Immediate inference and decision-making at the point of data capture

Core Components​

┌─────────────────────────────────────────────────────────────┐
│                    Smart Backpack System                    │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌─────────────┐      ┌──────────────┐      ┌────────────┐  │
│  │ Camera-Trap │─────▶│  SmartField  │─────▶│ WildWings  │  │
│  │  (Detect)   │      │ (Coordinate) │      │  (Observe) │  │
│  └─────────────┘      └──────────────┘      └────────────┘  │
│         │                     │                     │       │
│         └─────────────────────┴─────────────────────┘       │
│                           │                                 │
│                      ┌────▼────┐                            │
│                      │  MQTT   │                            │
│                      │ Broker  │                            │
│                      └─────────┘                            │
│                                                             │
│  ┌──────────────────────────────────────────────────────┐   │
│  │         Monitoring Stack (Grafana/Loki)              │   │
│  └──────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────┘

Module Architecture​

1. Camera-Trap Module​

Purpose: Real-time animal detection at the edge

Key Design Decisions:

  • Motion-Triggered Capture: Reduces power consumption and storage requirements
  • Edge ML Inference: YOLOv5 model runs locally, eliminating network dependency
  • Threshold-Based Classification: Configurable confidence scoring balances sensitivity and precision
  • MQTT Publishing: Lightweight event notification enables loose coupling

Why This Approach: Traditional camera-traps store all images for post-processing. Our edge inference approach provides immediate classification, reducing data volume by 95% and enabling real-time system response.

2. SmartField Module​

Purpose: Central event coordination and mission orchestration

Key Design Decisions:

  • Event-Driven Architecture: Reacts to detection events rather than polling
  • Stateless Processing: Enables horizontal scaling and fault tolerance
  • API-First Design: RESTful interfaces allow easy integration with external systems

Why This Approach: A centralized coordinator ensures system-wide state consistency while maintaining loose coupling between detection and response modules.

3. OpenPassLite Module​

Purpose: Autonomous drone mission planning

Key Design Decisions:

  • Geospatial Planning: Converts detection coordinates to flight paths
  • Temporal Optimization: Schedules missions to minimize flight time and battery usage
  • Safety-First Logic: Pre-flight checks, no-fly zones, and emergency protocols

Why This Approach: Separating mission planning from execution allows sophisticated path optimization while keeping the drone control layer simple and responsive.

4. WildWings Module​

Purpose: Drone control and video capture

Key Design Decisions:

  • Olympe SDK Integration: Leverages Parrot's official Python SDK for reliable control
  • Video Pipeline: Hardware-accelerated encoding reduces processing overhead
  • Privileged Container: Direct hardware access for USB and networking

Why This Approach: Using an open-source autonomous UAS platform ensures reproducibility and allows researchers to modify flight behaviors for specific study requirements.

Detection-to-Documentation Pipeline​

The system orchestrates a comprehensive autonomous workflow:

1. Camera-Trap Detects Animal (Confidence > 0.4)
                  ↓
2. MQTT Event Published (Location, Timestamp, Species)
                  ↓
3. SmartField Receives Event & Validates
                  ↓
4. OpenPassLite Plans Mission (Path, Altitude, Duration)
                  ↓
5. WildWings Executes Flight (40-50 seconds)
                  ↓
6. Video Captured & Stored (AnafiMedia/)
                  ↓
7. OpenPassLite Plans Return-To-Home Mission (Path, Altitude, Duration)
                  ↓
8. System Returns to Standby

Result: Synchronized multimodal dataset with ground-level detection paired with aerial behavioral observation—without human intervention.

Network Architecture​

Host Networking Mode: All services use network_mode: host for:

  • Direct hardware access (USB devices, cameras)
  • Low-latency inter-service communication
  • Simplified port management in field deployments

Message-Oriented Middleware: MQTT broker provides:

  • Publish-subscribe pattern for loose coupling
  • Quality of Service (QoS) guarantees for critical messages
  • Persistent sessions for network interruption resilience

Monitoring and Observability​

Loki + Promtail + Grafana Stack:

  • Loki: Efficient log aggregation without indexing overhead
  • Promtail: Scrapes logs from all services automatically
  • Grafana: Real-time visualization with custom dashboards

Design Rationale: Field deployments require operational visibility without external dependencies. The embedded monitoring stack provides insights even when disconnected from internet.

Field Validation​

Testing Profile:

  • Duration: 20 hours continuous operation
  • Detections: 45+ animal detection events processed
  • Missions: 38 autonomous drone deployments completed
  • Success Rate: 92% mission completion (failures due to low battery)

Validation Scope:

  • Complete detection-deployment-recovery cycles
  • Network resilience testing (WiFi disconnections)
  • Power management under field conditions
  • Weather resistance (light rain, wind gusts)

Design Patterns Used​

  1. Event-Driven Architecture: Asynchronous communication enables independent module evolution
  2. Microservices: Containerized services allow independent scaling and deployment
  3. Publisher-Subscriber: MQTT decouples event producers from consumers
  4. Infrastructure as Code: Docker Compose enables reproducible deployments
  5. Centralized Logging: Observability without code instrumentation

Future Extensibility​

The architecture supports:

  • Multi-Camera Networks: MQTT topics can namespace multiple camera-traps
  • Advanced ML Models: Swap detection models without changing pipeline
  • Cloud Integration: Optional data sync when network available
  • Additional Sensors: Acoustic, thermal, or environmental monitoring
  • Multi-Drone Coordination: Scale to fleet operations

Suggested Readings​