Bengt Oelmann

Bengt Oelmann is a Professor of Electrical Engineering at Mid Sweden University, working within the Department of Computer and Electrical Engineering (DET) and affiliated with the STC Research Centre. He holds a PhD in Engineering and is based in Sundsvall. His academic career spans several decades with continuous research output from the 1990s through to 2025.

Professor Oelmann's research interests focus on energy harvesting technologies, wireless sensor networks, and instrumentation systems. His work bridges theoretical modeling with practical implementation, particularly in the context of Internet of Things (IoT) applications. He has made significant contributions to variable reluctance energy harvesting, vibration-based power generation, and indoor photovoltaic systems for powering wireless sensors. His research often involves developing self-powered monitoring systems that can operate autonomously in industrial and environmental settings.

His publication record shows a clear trend toward increasingly sophisticated embedded systems that integrate machine learning for on-device processing, reducing the need for data transmission and enabling truly autonomous sensor networks. Recent work demonstrates applications in structural health monitoring, agricultural monitoring, and industrial IoT systems.

Professor Oelmann collaborates extensively with S. Bader and other researchers across multiple institutions. His work appears in high-impact journals including IEEE Transactions on Instrumentation and Measurement, Sensors, and Applied Energy. His research projects include HydroSense, ASIS (Autonomous Sensors for Industrial Wireless Sensor Networks), and SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle).

As an educator, Professor Oelmann has supervised numerous research projects and has contributed to the development of hardware platforms for rapid prototyping of wireless sensor networks (SENTIO). His work bridges the gap between theoretical electrical engineering and practical implementation in real-world monitoring scenarios.