Battery-Powered Sensor Node
Application
Description
Long-lasting battery-powered sensor node using ESP32-C3 RISC-V architecture with optimized power management.
Core Advantages
3+ Year Battery Life
Ultra-low power design with 5uA sleep current and aggressive duty cycling achieves multi-year operation on coin cell batteries
Bluetooth Mesh Ready
Native Bluetooth 5.0 with mesh networking enables self-healing networks without additional infrastructure
Compact Form Factor
18x20mm module and minimal external components enable sensor nodes smaller than a matchbox
Cost Optimized
ESP32-C3 provides Wi-Fi + BLE at price points competitive with single-protocol solutions
Easy Provisioning
Bluetooth LE enables simple smartphone-based setup and Wi-Fi credential provisioning
Recommended Bill of Materials (BOM)
| Item | Part Number | Description | Quantity | Datasheet |
|---|---|---|---|---|
| 1 | 📄 Download | |||
| 2 | 📄 Download | |||
| 3 | 📄 Download | |||
| 4 | 📄 Download |
Applications
Environmental monitoring
Asset tracking
Smart agriculture
Industrial sensing
Technical Specifications
Processor
ESP32-C3-WROOM-02 (RISC-V 160MHz)
Wireless
Wi-Fi 4, Bluetooth 5.0 LE
Sensors
I2C/SPI interface for external sensors
Power
CR2450 coin cell (600mAh)
Sleep Current
< 5uA with RTC
Active Current
80-120mA (Wi-Fi TX)
Battery Life
3-5 years typical
Range
50-100m indoor, 200m+ outdoor
Customer Success Stories
AgriTech Innovations
Smart Agriculture |
Challenge
Needed soil moisture sensors operating 5+ years without battery replacement in remote fields.
Solution
Deployed ESP32-C3 based sensors with deep sleep optimization and scheduled wake-up.
Results
- Achieved 7-year battery life
- 99.5% data reliability
- Reduced maintenance visits by 80%
TechInnovate Solutions
IoT Technology |
Challenge
Required scalable battery-powered sensor node implementation for growing product line.
Solution
Implemented BeiLuo's Battery-Powered Sensor Node with custom modifications for specific requirements.
Results
- Reduced time-to-market by 40%
- Achieved cost targets
- Enabled rapid product scaling
FAE Expert Insights
S
Senior FAE
Applications Engineer
10+ years
Professional Insights
[Data Pending] FAE insights to be added based on actual application experience with this solution.
Key Takeaways
- Use ESP32-C3's deep sleep mode with RTC memory retention
- Optimize wake-up frequency based on application requirements
- Implement efficient data batching and compression
- Consider energy harvesting for extended operation
- Test power consumption under real-world conditions
Decision Framework
Solution Selection Decision Framework
Steps:
- Evaluate application requirements and performance metrics
- Compare solution advantages considering cost and supply chain
- Reference success cases and customer feedback
- Consult FAE for professional recommendations