TY - CONF A1 - Winkler, Nicolas P. T1 - Revisiting Environmental Sensing Nodes: Lessons Learned and Way Forward N2 - Setting up sensors for the purpose of environmental monitoring should be a matter of days, but often drags over weeks or even months, preventing scientists from doing real research. Additionally, the newly developed hardware and software solutions are often reinventing existing wheels. In this short paper, we revisit the design of our environmental sensing node that has been monitoring industrial areas over a span of two years. We share our findings and lessons learned. Based on this, we outline how a new generation of sensing node(s) can look like. T2 - SMSI 2023 Conference Sensor and Measurement Science International CY - Nuremberg, Germany DA - 08.05.2023 KW - Sensing node KW - Sensor network KW - Environmental monitoring KW - Low-cost KW - LoRaWAN PY - 2023 AN - OPUS4-57455 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. T1 - Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring N2 - A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown. T2 - SMSI 2021 Conference: Sensor and Measurement Science International CY - Online meeting DA - 03.05.2021 KW - Wireless sensing node KW - Air pollution KW - Environmental monitoring PY - 2021 AN - OPUS4-52609 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. T1 - DeepGDM: Deep Learning for Gas Distribution Mapping N2 - Gas distribution mapping (GDM) is a valuable tool for monitoring the distribution of gases in various applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This presentation gives an overview of the different approaches to GDM and motivate the use of a deep-neural network-based approach. As access to ground truth representations of gas distributions remains a challenge in GDM research, an approach for the simulation of realistic-shaped synthetic gas plumes is described, which was used for training Gas Distribution Decoder, a deep neural network for spatial interpolation of spatially sparse gas measurements. T2 - AASS Seminar CY - Örebro, Sweden DA - 08.06.2023 KW - Deep learning KW - Mobile robot olfaction KW - Environmental monitoring PY - 2023 AN - OPUS4-57635 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -