Use Cases

The S0 IoT Gateway is designed for diverse IoT applications across smart metering, industrial automation, agriculture, and edge computing. Here are detailed use cases demonstrating the platform's versatility.

Smart Metering

Residential Utility Monitoring

Challenge: A utility provider needs to remotely monitor electricity, gas, and water usage across hundreds of homes. Some homes have legacy wired M-Bus meters, while others use modern wireless M-Bus models.

Solution:

Deploy S0 gateway in each apartment block
DIN-rail mount inside electrical cabinets using Base Board
Use Wireless M-Bus (RC-S2LP) to read meters without rewiring
Use Wired M-Bus via Base Board for legacy devices
Backhaul readings to utility's cloud via NB-IoT (SIM7080G)
Periodic uploads to Magistrala using MQTT
Retry logic ensures resilience in poor cellular areas
Power from 24V control panel supply with battery backup

Benefits:

No truck rolls for meter reading
Real-time consumption monitoring
Leak detection and anomaly alerts
Support for mixed meter infrastructure
Reliable operation with battery backup

Technical Details:

Hardware:
  - S0 Gateway with NB-IoT module
  - Base Board with M-Bus transceiver
  - External antenna for basement installation

Connectivity:
  - Wireless M-Bus: 868 MHz, Mode S1
  - Wired M-Bus: 300-9600 baud
  - NB-IoT: 15-minute update interval

Data Flow: 1. Collect meter readings every 15 minutes
  2. Log to SD card for redundancy
  3. Aggregate and compress data
  4. Upload via NB-IoT to Magistrala
  5. Cloud processes and bills customers

District Heating Network

Challenge: Monitor temperature, flow, and energy consumption across a district heating network with hundreds of distributed heat meters.

Solution:

Install S0 gateways at strategic points throughout network
Collect data from wireless M-Bus heat meters
Real-time monitoring of network performance
Edge processing to detect anomalies
Cellular backhaul where wired connectivity unavailable

Benefits:

Optimize energy distribution
Rapid leak detection
Predictive maintenance
Improved customer billing accuracy

Industrial IoT

Predictive Maintenance Platform

Challenge: A manufacturing facility needs to monitor industrial machinery for early signs of failure to minimize downtime and maintenance costs.

Solution:

Deploy S0 gateways on critical equipment
Collect high-frequency sensor data:
- Vibration sensors via I2C
- Temperature sensors via SPI
- Pressure sensors via analog inputs
Local ML inference for real-time decisions
Send only alerts to central system
Historical data logging to SD card

Benefits:

Catch failures before they occur
Reduced network bandwidth usage
Real-time response to critical conditions
Reduced downtime and maintenance costs
Local operation during network outages

Technical Details:

Edge Processing:
  - Wasm workload: 50KB FFT analysis
  - Update rate: 10 Hz sensor sampling
  - ML inference: <50ms latency
  - Alert generation: Local threshold detection

Connectivity:
  - Primary: Ethernet via Base Board
  - Backup: WiFi
  - Emergency: NB-IoT SMS alerts

Data Management:
  - Local buffering: 7 days on SD card
  - Upload: Alerts immediately, trends hourly
  - Retention: 30 days cloud, 7 days local

Factory Energy Monitoring

Challenge: Monitor and optimize energy consumption across a production facility with hundreds of machines and systems.

Solution:

S0 gateways at electrical distribution points
M-Bus energy meters monitoring
Real-time power quality analysis
Integration with building management system

Benefits:

Identify energy waste
Peak demand management
Power quality monitoring
Cost optimization
Carbon footprint reduction

Agriculture & Environment

Precision Agriculture Gateway

Challenge: A farm operator wants to monitor soil moisture, weather conditions, and water flow across large fields far from any infrastructure.

Solution:

Deploy S0 in weatherproof enclosures
Solar panel with battery backup
Sensors via I2C/SPI/UART interfaces:
- Soil moisture sensors
- Weather station
- Water flow meters
GNSS (via SIM7080G) for geo-tagging
NB-IoT for data backhaul
SD card for offline storage
Local processing for irrigation control

Benefits:

Optimize water usage
Remote field monitoring
Automated irrigation control
Historical data analysis
Works in areas without infrastructure

Technical Details:

Deployment:
  - IP67 enclosure
  - 20W solar panel
  - 12V 20Ah battery
  - External LTE antenna on mast

Sensors:
  - 6x soil moisture (I2C)
  - 1x weather station (UART)
  - 1x flow meter (pulse counter)
  - 1x GNSS module (internal)

Operation:
  - Sample interval: 15 minutes
  - Upload interval: 1 hour
  - Emergency alerts: Real-time via SMS
  - Battery life: 7 days without sun

Environmental Monitoring Network

Challenge: Monitor air quality, noise levels, and weather across a city to provide public health data.

Solution:

S0 gateways at lamp posts and buildings
Multi-sensor integration
Edge data validation and filtering
Real-time public dashboard
Alert system for threshold violations

Benefits:

Public health awareness
Pollution source identification
Real-time alerts
Long-term trend analysis

Edge Computing

Edge AI Inference Platform

Challenge: Run AI inference at the edge for real-time decision making in security, quality control, or process monitoring applications.

Solution:

Camera or sensor array connected to BeagleV-Fire
S0 provides connectivity and power management
TensorFlow Lite inference on Linux
Results sent via optimal connectivity

Benefits:

Real-time inference (less than 100ms)
Privacy-preserving (data stays local)
Works offline
Reduced cloud costs
Low bandwidth usage

Technical Details:

Architecture:
  - BeagleV-Fire: ML inference (TFLite)
  - S0: Connectivity + data collection

Performance:
  - Inference: 50ms typical
  - Preprocessing: 20ms (Wasm)
  - Total latency: less than 100ms edge-to-decision

Models:
  - Object detection: MobileNet SSD
  - Classification: ResNet-18 (quantized)
  - Anomaly detection: Autoencoder