MSc thesis supervision: “Extending IIoT Capabilities: KPI Monitoring and Heterogeneous Sensor Integration”

Abstract

As industrial systems grow increasingly connected, the need to extend the functionality of existing IIoT infrastructures becomes increasingly important. This thesis investigates the feasibility of integrating a KPI monitoring system into an already-connected IoT environment, with a focus on the challenges of heterogeneous sensor integration. Two hardware architectures are explored: one combining a PLC with an Edge Gateway, and another using a standalone Edge Gateway. Both are evaluated based on their ability to support real-time data acquisition, preprocessing, and transmission using digital and Modbus sensors. To address limitations in physical testing, a simulation environment was developed to enable early validation and iterative development. The work demonstrates how edge-based data processing strategies, such as aggregation, sampling, and change detection, can effectively reduce bandwidth usage while maintaining data trueness. Results show that while both architectures nearly fulfill system requirements, the inclusion of a PLC improves determinism and measurement accuracy, whereas the standalone edge gateway simplifies integration and reduces hardware complexity at the cost of some accuracy. The simulation environment proved valuable in supporting development and mitigating risks. This thesis contributes practical insights into edge-layer architecture design and presents an approach for extending IIoT systems with new KPI monitoring capabilities.

My role as supervisor

I was the university supervisor of the two students at IDA, with Ahmed Rezine as examiner. I helped scope the research question and the method, asked questions that challenged the students' architecture and evaluation decisions, and reviewed and steered the scientific writing up to the final thesis.

My background in embedded telemetry at iData mapped closely onto this topic.