This undergraduate course offers a comprehensive introduction into Physical Computing and Sensing within the context of the Internet of Things (IoT). Students will gain insights into general concepts and recent developments related to IoT, including its diverse applications and trends. Specific focus lies on Data Acquisition using various microcontrollers such as Arduino, ESP, STM, and solutions from industry leaders like Siemens. Participants will learn about different types of sensors, their characteristics, and how to distinguish between digital and analogue sensor technologies. Furthermore, this course covers essential Communication Protocols used in IoT systems, both at the local level (I²C, I²S, SPI) and long-range wireless standards (LoRaWAN, Zigbee, LoRa, WiFi, BLE, ESP-NOW, IEEE 802.15.4). Additionally, students will explore relevant IoT Communication Protocols (MQTT, HTTP, CoAP), Standards (OGC Observation & Measurements, SensorObservationService, SensorThings API), and popular platforms like Azure IoT, AWS IoT, openSenseMap, and sensors.community. Special emphasis is given to Data Processing techniques with respect to cloud-, edge-, and embedded-based approaches. Tools and software required for Data Visualization, Geographic Information Systems (GIS), Dashboards, and Custom Live Data Applications are also discussed throughout the course. Overall, participants will acquire hands-on experience working with cutting-edge IoT devices while developing skills necessary for designing innovative sensing and actuation solutions.
Block course: 23. - 27.02., 9 - 16 Uhr
Obligatory introduction Meeting, 14.10.2025 17:00 Uhr Raum 211
In the obligatory introduction meeting we will distribute presentations amongst students that will be presented on the first day of the block course
During the block course we are planning an excursion to the Wupperverband (https://www.wupperverband.de/unsere-aufgaben/hochwassermanagement/daten/sensoren) in Wuppertal - most probably on Wednesday or Thursday. Please consider additional travel time.
- Lehrende/r: Thomas Bartoschek
- Lehrende/r: Benjamin Amir Karic