Sensors can help us decide when to irrigate, track heat in urban areas, and monitor rainfall. But what does it take to build a sensor network and turn its measurements into useful information? This hands-on course gets you started with IoT sensor networks for environmental monitoring, taking you from choosing sensors to working with real data.
You will first explore sensors for soil moisture, air temperature, humidity, and rainfall, and compare connectivity options such as Wi-Fi, NB-IoT and other cellular technologies, LoRaWAN, and Bluetooth Low Energy (BLE). You will learn how these choices fit together when designing a network for your monitoring needs.
Next, you will follow real measurements from a live sensor at the UT FieldLab through The Things Network into your own ThingSpeak dashboard. You can work with your own sensor or use our live data, so you do not need to buy hardware to take part.
With data flowing into your dashboard, you will move on to analysing measurements in a notebook. You will practise cleaning and visualising sensor data, assessing its reliability, and deciding whether it is suitable for supporting an environmental decision.
Finally, you will bring these skills together by designing your own IoT sensor network concept for an environmental application of your choice. You will finish with practical experience, a network design to build on, and a certificate of completion.
Course content
Learning outcomes
After this course, you will be able to:
- Describe the core components of an IoT sensor data network for environmental and field applications, including sensors, connectivity and platforms, using appropriate terminology
- Select suitable sensor types and connectivity options for simple environmental scenarios, and justify these choices based on coverage, power use and data needs
- Use tools to access, export and visualize field sensor data from an IoT platform, and identify basic data quality issues
- Design a simple field sensor network concept for a chosen application, specifying variables, sensors, connectivity and how the resulting data supports decisions