A secure and energy-efficient asset tracking system leveraging LoRaWAN technology for long-range communication. Built using the STM32WLE5JC (Wio-E5) and Quectel L89 GNSS module, the system offers real-time location tracking, geo-fencing alerts, emergency button support, and anomaly detection using Mahalanobis Distance.
- 📍 Real-time GPS tracking using Quectel L89 R2.0
- 🌐 LoRaWAN-based long-range, low-power communication
- 🛑 Geo-fencing with virtual boundary breach alerts
- 🚨 Emergency alert system with panic button support
- 📊 Anomaly detection using Mahalanobis Distance (fall detection, irregular movement)
- 🔋 Power-efficient design with MP2667 Power Management IC
- ☁️ Data visualization via The Things Stack V3 & Firebase
- 🧱 Modular SMD hardware for scalable deployment
| Component | Description |
|---|---|
| Wio-E5 Dev Board | LoRaWAN module with STM32WLE5JC |
| Quectel L89 R2.0 | GNSS module for GPS |
| LIS3DHTR | 3-axis accelerometer |
| MP2667 | Power management IC |
| Raspberry Pi 4B | Gateway host system |
| WM1302 LoRa Gateway | LoRaWAN gateway module |
| 3.6V Li-Po Battery | Power source for node |
- Embedded C (STM32CubeIDE) – Firmware for sensor node
- Python & Node.js – Backend data handling and visualization
- JavaScript (TTN Payload Formatter) – Payload decoding
- The Things Stack (TTN) – LoRaWAN network server
- Firebase – Real-time database & alert system
-
Sensor Node
- Collects GPS & accelerometer data
- Detects anomalies using Mahalanobis Distance
- Sends LoRa payload to TTN
-
Gateway
- WM1302 with Raspberry Pi 4
- Forwards packets to The Things Stack V3
-
Cloud
- TTN decodes and forwards data to Firebase
- Firebase triggers alerts and dashboard updates
The payload sent from the sensor node over LoRa follows a comma-separated string format:
| Field | Description |
|---|---|
latitude |
GNSS latitude in decimal degrees |
longitude |
GNSS longitude in decimal degrees |
altitude |
Altitude in meters |
emergency_status |
1 = Panic button pressed, 0 = Normal |
anomaly_status |
1 = Anomaly detected, 0 = Normal |
Anomaly detection is based on the Mahalanobis Distance algorithm to identify irregular patterns in motion, such as:
- Free fall
- Excessive vibration
- Sudden orientation changes
- Logistics & supply chain
- Fleet & vehicle management
- Smart agriculture
- Construction equipment tracking
- Coastal vessel monitoring
- Urban infrastructure monitoring
- Geo-fence breach → Email/SMS/Telegram
- Emergency button press → Real-time alert
- Anomaly detection → Logged in dashboard
- Unique DevEUI, AppKey, and OTAA authentication
- Encrypted communication via LoRaWAN security stack
- Firebase HTTPS endpoints for secure data handling
📺 Watch the full project overview here:
https://youtu.be/-xaInsjvY7k?si=eaUaM28JZm8MWVSY
- Adhikesavan M
- Daniel Raj C
- Dibesh S
- Hrithik Kumar J
Department of Electronics and Communication Engineering
Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College
Anna University, Chennai
This project is licensed under the MIT License.
- Guided by Dr. A. Karthikeyan, M.E.., Ph.D., Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College.
- Thanks to NEEVEE Technologies for hardware guidance
- Supported by Anna University curriculum and ECE department, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College.