Project Kickoff & System Architecture Definition
Formulated the technical problem statement, studied the shortcomings of traditional camera traps, and mapped out the cyber-physical architecture for NETH.
Cyber-Physical Systems Project (CO3060)
Intelligent Wildlife Monitoring.
Intelligent Wildlife Monitoring.
Smarter Conservation.
NETH (Networked Ecological Telemetry Hub) is a low-power cyber-physical system integrating motion-triggered camera capture, synchronized micro-climatic sensing, and edge telemetry for remote ecological observation.
Ultra-Low Power
Deep-Sleep Cycles (< 50µA)
PIR Motion Capture
Sub-Second Trigger Response
Dual Sensing
Temp & Humidity Telemetry
LoRa Ready
Long-Range Hub Broadcast
Project Foundation
About NETH
About NETH
Bridging Camera Traps and Real-Time Ecological Context
Wildlife conservation depends on continuous, accurate observation of animal activities in natural habitats. Standard camera traps can photograph wildlife, but manual data collection in remote wilderness areas is labor-intensive and hazardous. Furthermore, traditional cameras lack synchronized environmental measurements and device health telemetry.
NETH solves this by creating an autonomous, intelligent monitoring node that couples PIR motion detection with high-speed camera acquisition, ambient temperature, relative humidity, and battery health telemetry.
Automated capture with zero disturbance to natural wildlife corridors.
Precise correlation between animal sightings and micro-climate fluctuations.
Remote device health reporting preventing unrecorded downtime.
Capabilities
Key System Features
Intelligent Image Capture
Fast hardware wake-up triggers high-resolution image recording upon animal motion detection while suppressing false foliage triggers.
Synchronized Metadata
Every captured frame is matched with millisecond timestamps, ambient temperature, relative humidity, battery level, and node ID.
Low-Power Endurance
Optimized deep-sleep current budgeting combined with solar charging ensures continuous, unattended operation across changing seasons.
Local Resilient Storage
Industrial FAT32 file system journaling protects images and telemetry logs even during sudden voltage dips or weather disruptions.
Edge AI Classification
Planned TinyML inference pipeline to filter empty frames and categorize wildlife species directly on the edge node.
Long-Range LoRa Hub
Sub-GHz LoRa RF telemetry transmits detection digests and node health heartbeats across multi-kilometer wilderness expanses.
System Blueprint
Solution Architecture
A layered cyber-physical architecture engineered for ultra-low power consumption, deterministic sensing, and long-range telemetry.
Core Operational Workflow
View Full-Size Workflow ↗Comprehensive 8-stage execution pipeline covering on-node sensing (Sleep, PIR Trigger, Sensor Read, Image Capture, SD Storage), Edge AI inference, LoRa mesh broadcasting, and remote cloud dashboard monitoring:
System Stack
Architectural Layers
1. Perception Layer
Dual-element PIR motion detector, CMOS image sensor, calibrated ambient temperature & relative humidity sensor, and battery voltage divider.
2. Embedded & Edge Layer
Low-power microcontroller unit, FreeRTOS event management, DMA-assisted camera pipeline, and optimized TinyML classification runtime.
3. Telemetry & Transport Layer
Sub-GHz LoRa modulation (SX127x/SX126x) operating in regional ISM band with error checking and heartbeat pinging.
4. Application & Gateway Layer
Base-station gateway receiving node packets, cloud ingestion pipeline, and ecological telemetry dashboard for conservation scientists.
Engineering Details
Hardware & Software Design
System specifications detailing component selection, energy budgets, firmware states, and telemetry schemas.
| Subsystem | Component / Module | Functional Role | Technical Specifications |
|---|---|---|---|
| Main Controller | Low-Power Embedded MCU / SoC | Coordinates peripherals, image acquisition & logging | Deep sleep < 50µA, fast wake-up (< 20ms), 240MHz dual-core |
| Vision Module | CMOS Camera (OV2640 / OV5640) | Captures JPEG frames upon trigger | Configurable resolution up to UXGA (1600x1200), low-light mode |
| Motion Detection | Passive Infrared (PIR) Sensor | Detects animal thermal displacement | Adjustable sensitivity, 100° cone angle, 5-7 meter detection range |
| Climatic Sensing | Digital Temp & Humidity (DHT22 / SHT3x) | Logs micro-climate variables | Temp: -40 to 80°C (±0.3°C), Humidity: 0-100% RH (±2% RH) |
| Local Storage | MicroSD Card Interface | Stores images and CSV telemetry registers | FAT32 filesystem, SPI/SDIO high-speed bus |
| Power Subsystem | Li-ion 18650 Pack + Solar MPPT Controller | Maintains node autonomous field operation | 3.7V 3000mAh cells, 5V/6V solar input, reverse current protection |
| Telemetry Radio | LoRa Transceiver Module (SX1278 / SX1262) | Long-range wireless packet broadcasting | 433/868/915 MHz, +20 dBm PA, link budget up to 148 dB |
Firmware State Transition
Event-driven state machine ensuring optimal sleep duty cycles:
[STATE 0: DEEP SLEEP]
- Disable high-draw peripheral power rails
- Configure external PIR GPIO interrupt & RTC timer
- Current consumption: < 50 µA
[STATE 1: WAKE & VALIDATE]
- Woken by: PIR Interrupt or RTC Periodic Ping
- Check battery voltage & threshold integrity
[STATE 2: SENSE & CAPTURE]
- Power up CMOS sensor rail
- Trigger camera capture & compress JPEG
- Read temperature & relative humidity via digital bus
- Sample battery ADC voltage divider
[STATE 3: COMMIT TO DISK]
- Mount SD card & write /images/NODE01_TIMESTAMP.jpg
- Append telemetry record to telemetry_log.csv
- Power down camera rail
[STATE 4: TELEMETRY DISPATCH]
- Construct compact binary LoRa packet
- Transmit beacon to central receiver
- Re-arm PIR interrupt & transition back to [STATE 0]
Telemetry Packet Schema
Standardized data format logged to disk and summarized over LoRa:
{
"node_id": "NETH-01-FOREST",
"timestamp_utc": "2026-10-18T17:45:00Z",
"trigger": "PIR_MOTION",
"environment": {
"temperature_c": 24.8,
"humidity_pct": 78.4
},
"power": {
"battery_mv": 3950,
"battery_pct": 87,
"solar_mv": 5120
},
"image": {
"filename": "IMG_20261018_174500.JPG",
"resolution": "UXGA"
},
"edge_ai": {
"classified": true,
"label": "wildlife",
"confidence": 0.91
}
}
Verification & Benchmarks
Testing & Evaluation
Evaluation criteria ensuring hardware reliability, trigger timeliness, sensor precision, and power longevity.
| Evaluation Area | Target Engineering Benchmark | Measurement Protocol | Milestone Status |
|---|---|---|---|
| Camera Trigger Latency | < 800 ms from motion detection to frame capture | Optical trigger flash timing benchmarked with digital oscilloscope | In Progress |
| Sensor Accuracy | Temperature: ±0.4°C | Humidity: ±3% RH | Cross-referenced against calibrated meteorological reference chamber | Planned |
| Power Profile | Sleep: < 50 µA | Active peak: < 180 mA | Current profiling with precision digital multimeter and power analyzer | In Progress |
| Data Synchronization | 100% correlation between image & sensor record | Continuous simulated trigger test over 72 uninterrupted hours | Planned |
| LoRa Telemetry Range | Packet delivery rate > 95% at 2+ km | Field trials across line-of-sight and dense canopy environments | Planned |
Progress
Development Roadmap
Phase 1: Completed
Research & Requirements
Literature review on camera traps, requirement analysis, power budget estimation, and system architecture blueprint.
Phase 2: In Progress
Sensor & Camera Breadboarding
Interfacing PIR sensor with hardware interrupts, camera bus initialization, and environmental sensor driver validation.
Phase 3: Upcoming
Deep-Sleep & Storage Integration
Implementing low-power sleep transitions, SD card FAT32 journaling, and solar battery charging circuitry.
Chronicles
Project Blog & Notes
Technical insights, development updates, and engineering decisions from the team.
Motion Sensing Trade-Offs: PIR vs Microwave Radar
A comparative study on energy consumption: evaluating why passive infrared sensors remain ideal for ultra-low-power wildlife deployments over radar.
Safeguarding MicroSD Data Against Sudden Voltage Drops
Designing file journaling and buffer flushing protocols to prevent file corruption during cold-night battery drops in the field.
Deployment
How to Use NETH
Guidelines for setting up, mounting, and operating a NETH node in wildlife observation corridors.
1
Preparation
Insert a formatted FAT32 MicroSD card. Ensure rechargeable lithium cells are charged and solar panel surfaces are clear.
2
Mounting
Attach the weatherproof node enclosure to a tree or stake 0.5–1.5m above ground level angled toward active wildlife tracks.
3
Diagnostic Check
Power on the node. The diagnostic indicator flashes green to confirm camera, sensors, and SD card are healthy before deep sleep.
4
Data Retrieval
Retrieve logged high-res images from the SD card periodically, or monitor continuous detection digests over the LoRa receiver.
Field Configuration Template (neth_config.ini)
Placed on the SD card root to easily adjust operational parameters without reflashing firmware:
[node_settings]
node_id = NETH-NODE-01
capture_resolution = UXGA ; Options: QVGA, VGA, SVGA, UXGA
burst_count = 3 ; Frames to capture per trigger
pir_cooldown_seconds = 10 ; Prevent redundant re-triggering
deep_sleep_enabled = true ; Maximize battery runtime
lora_frequency_mhz = 868.1 ; ISM band frequency
heartbeat_interval_hours = 6 ; Periodic telemetry ping interval
The Team
Project Contributors
Undergraduate Engineers from the Department of Computer Engineering, Faculty of Engineering, University of Peradeniya.
Bhagya Karunanayake
E/22/184
Embedded Systems & Firmware
Imaadh Ifthikar
E/22/151
System Architecture & Sensors
Daniru Chandrawansha
E/22/056
Hardware & Power Optimization
Didulana Lokugamage
E/22/214
Telemetry & Software Integration
Academic Supervisor
Ms. Yasodha Vimukthi
Department of Computer Engineering, University of Peradeniya • yasodhav@eng.pdn.ac.lk
Financial Planning
Project Budget
Estimated bill of materials (BOM) and component acquisition planning for the NETH prototype.
21 Items
Component Categories
LKR 43,400
Total Proposed Budget
CO3060
Course Allocation
Component Budget Sheet Breakdown
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