Most IoT teaching fails in the same way: students wire a sensor to a microcontroller, print a value to a serial monitor, and never see why any of it matters. Our IoT training equipment is built around complete chains — sensor to node to gateway to cloud to dashboard — so the exercise ends with data someone could act on.
Sensor nodes with temperature, humidity, vibration, current, gas and proximity sensing; microcontroller and SBC platforms; wireless communication over Wi-Fi, Bluetooth Low Energy, Zigbee, LoRa and NB-IoT; gateway hardware; MQTT and HTTP protocol exercises; cloud dashboards with alerting and historical trends; and edge processing so students see the difference between sending everything and sending what matters.
Condition monitoring on a motor with vibration and current trending, energy metering across a distribution board, cold chain telemetry, water quality and level monitoring, and predictive maintenance exercises where a trend rather than a threshold triggers the alert. These map directly onto the applications graduates will be asked about in interview.
State the number of student workstations, the wireless protocols required, whether LoRa gateway hardware is in scope, which cloud platform the curriculum uses and whether accounts or licences are included, and whether the sensors must be industrial grade or development grade. Confirm site Wi-Fi and firewall policy early — a laboratory that cannot reach the cloud service teaches half the syllabus.
