IoT Sensor Deployment for Industrial Monitoring

Course Overview

Learn to deploy IoT sensor networks for industrial monitoring applications. From sensor selection to data visualization, this course covers the complete IoT deployment lifecycle. Based on methodologies from leading IoT educators like Andreas Spiess and resources from AWS IoT, Azure IoT, and The Things Network.

What You’ll Learn

• Sensor Selection: Match sensors to monitoring requirements (temperature, vibration, gas, etc.)
• Network Protocols: Deep dive into LoRaWAN, WiFi, Zigbee, and cellular IoT
• Gateway Architecture: Design reliable sensor-to-cloud communication
• Data Management: Collect, store, and analyze sensor data using MQTT
• Platform Integration: Connect sensors to ThingsBoard, Node-RED, Grafana
• Power Optimization: Maximize battery life for remote deployments

Applications Covered

• Environmental Monitoring: Temperature, humidity, air quality (CO2, PM2.5)
• Equipment Condition Monitoring: Vibration analysis, motor current, runtime tracking
• Energy Management: Power consumption, solar output, fuel levels
• Structural Health: Strain gauges, crack detection, tilt monitoring
• Process Monitoring: Flow rates, pressure, tank levels

Hardware Platforms Covered

This course references popular IoT hardware:

• Microcontrollers: ESP32, Arduino, Raspberry Pi
• LoRaWAN: The Things Network, Dragino gateways, RAK Wireless
• Sensors: Temperature (DS18B20), Humidity (DHT22), Vibration (SW-420)
• Industrial: Modbus sensors, 4-20mA transmitters

Software & Platforms

Learn to work with:

• ThingsBoard: Open-source IoT platform
• Node-RED: Flow-based programming for IoT
• Grafana: Data visualization and dashboards
• InfluxDB: Time-series database for sensor data
• MQTT Brokers: Mosquitto, HiveMQ

Industry Standards

• MQTT 3.1.1 and 5.0 protocol
• LoRaWAN 1.0.3 specification
• Industrial sensor calibration best practices
• Cybersecurity for IoT devices (NIST guidelines)