Classical automation technology is still strongly tied to manufacturer-specific hardware and software stacks, meaning that modifications to a plant frequently require long planning times and costly shutdowns. To avoid shutdowns, Software-Defined Automation (SDA) has already enabled fast, shutdown-free updates through the decoupling of control logic and hardware by means of containerization. While the control layer has thus become more flexible, the sensor technology remains firmly bound to proprietary analog-to-digital conversion, noise filtering, characteristic curve corrections, and other signal processing functions that are executed exclusively on-site on specialized hardware. Software-Defined Sensing (SDS) extends this principle to sensor technology by keeping the physical measurement at the sensor hardware while outsourcing the subsequent mathematics and data processing into software-based, virtualized pipelines. 
As a result, algorithms such as calibrations, filters, or AI-supported interpretations can be easily updated or replaced without replacing the sensor, which improves maintainability, scalability, and the integration of heterogeneous sensors.

GOALS:

• Analysis of sensor types across the plant lifecycle with a focus on SDS decoupling; comparative evaluation in terms of maintainability and cost efficiency.
• Investigation of the hardware-software interface taking into account real-time requirements, reliability, and latency.
• Conception and implementation of a classification framework for sensor functionalities with validation through case studies.

HELPFUL PRIOR KNOWLEDGE:

• Interest in sensor technology in automation engineering
• Interest in containerized, cloud-based architectures
• Lectures IT/AT and MARO