Markus Lehner, M. Sc.
- Member of Scientific Staff
- Group: Prof. Stork
- Room: 2.27
CS 30.10 - Phone: +49 721 608-46501
- markus lehner ∂ kit edu
- Engesserstr. 5
76131 Karlsruhe
Curriculum vitae
Bachelor's degree in Electrical Engineering and Information Technology at FAU in Erlangen
- Master's degree in Electrical Engineering and Information Technology at KIT
- Employee at ITIV since August 2022
Distributed sensor networks
In the transformation to a data-driven economy, access to data becomes invaluable. To address the data shortage, easy-to-use solutions need to be developed to deploy resource and energy-efficient sensors. For this many types of distributed sensor networks are being developed depending on the required data rate. Many processes don’t require real-time monitoring, here the focus is on the lifetime and robustness of the sensors. Here we are researching novel network types based on the LoRa modulation to increase the range and reduce the power consumption.
Smart Farming - Soil sensors
With the earth's population expected to approach 10 billion, the current food production will not be sufficient. To increase the efficiency of farming the trend is now going towards IoT sensors and data analysis. The most important factors for crop growth are soil moisture, nutrient content and solar irradiation. We are developing novel soil sensors to monitor these parameters cost-effectively.
Objective analysis of coffee aroma using e-Nose
Most of us are drinking coffee every day, yet not much thought goes into how it tastes. Like with wine, we blindly believe the exotic fruits that are written on the label. With metal-organic-frameworks (MOFs) portable e-Noses are now possible. Using these to objectively fingerprint the aroma of coffee beans, we are working on an ML model to link them with the human perception of the coffee aroma.
Title | Type |
---|---|
Energy-saving LoRa Mesh Network through wake-on-receive | Masterarbeit |
Classification of coffee aromas using e-Nose and ML/KI | Bachelor- /Masterarbeit |
Anomaly detection in chaotic energy systems | Bachelorarbeit |