TY - CONF A1 - Winkler, Nicolas P. T1 - Using Redundancy in a Sensor Network to Compensate Sensor Failures N2 - Sensors can fail. Redundancy should therefore be a design driver of wireless sensor networks. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the network can compensate for the outage of sensors. T2 - IEEE SENSORS 2021 CY - Online meeting DA - 31.10.2021 KW - Environmental monitoring KW - Wireless sensor network KW - Sensor placement KW - Machine learning PY - 2021 AN - OPUS4-53940 LA - deu 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 - Robot-Assisted Air Quality Monitoring N2 - This presentation gives an introduction to robot-assisted air quality monitoring based and shows results of the research project RASEM. T2 - The 6th Meeting of the Investigation Committee on Measurement of Odors and Applications of Their Psychological/Physiological Effects CY - Online meeting DA - 21.01.2022 KW - Environmental Monitoring KW - Air Pollution KW - Air Quality KW - Wireless Sensor Network KW - Mobile Robot Olfaction PY - 2022 AN - OPUS4-54251 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. T1 - RASEM - Robot-Assisted Environmental Monitoring for Air Quality Assessment N2 - This presentation was held at the SAF€RA symposium and gives an overview to the research project RASEM and its results. T2 - 2022 SAF€RA Symposium CY - Rome, Italy DA - 19.05.2022 KW - Air Pollution KW - Air Quality KW - Environmental Monitoring KW - Mobile Robot Olfaction KW - Wireless Sensor Network PY - 2022 AN - OPUS4-54953 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. T1 - Super-Resolution for Gas Distribution Mapping: Convolutional Encoder Decoder Network N2 - Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques. T2 - 19th International Symposium on Olfaction and Electronic Nose CY - Aveiro, Portugal DA - 29.05.2022 KW - Gas Distribution Mapping KW - Spatial Interpolation KW - Deep Learning KW - Super-Resolution KW - Sensor Network PY - 2022 AN - OPUS4-54954 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory N2 - The Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation. T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Poros, Greece DA - 20.09.2022 KW - Low-cost KW - Sensor network KW - Dust sensor KW - Sensor drift PY - 2022 AN - OPUS4-55908 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 - TY - CONF A1 - Johann, Sergej A1 - Winkler, Nicolas P. A1 - Bartholmai, Matthias ED - Kourkoulis, S. K. T1 - Multi-sensor system for long-term monitoring with WiFi and LoRaWAN technology N2 - In many cases, science falls back on self-developed prototype systems, which are used and developed for the measurement and execution of the tasks. About 80 % of the development is based on the same hardware design, which is used in only one application scenario and then discarded. For the most part, there are also uncalibrated sensors, since it is costly to calibrate a complicated sensor measurement system or the entire measurement chain since access to the sensor systems is not always available. This paper describes a conceptual design to implement a versatile sensor system with the motivation to fuse the data recording and data reception, which can cover large areas with the help of LoRaWAN (Long Range Wide Area Network) technology. To overcome disadvantages of LoRaWAN, namely the slow data rates, the proposed sensor system can also cover smaller areas with the widespread WiFi technology. An enormous advantage over individual complete systems in the form of a prototype, is the rapid expansion, uncomplicated calibration of the individual sensors and the ecological relief. A modular design is used, where individual stacks with sensors and peripherals can be added separately. The stacks are standalone low-power systems and can be calibrated, maintained, and replaced separately and do not require the entire measurement chain. The measured and sent values are stored locally on the main stack and sent to the data collector (gateway) and evaluated by means of the automated selection between WiFi and LoRaWAN. T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Athens, Greece DA - 20.09.2022 KW - Multisensor system KW - LoRaWAN KW - WiFi KW - Long term monitoring PY - 2022 SP - 1 EP - 2 AN - OPUS4-56462 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. T1 - Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping N2 - To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model. T2 - ROBOT2022: Fifth Iberian Robotics Conference CY - Zaragoza, Spain DA - 22.11.2022 KW - Gas Distribution Mapping KW - Spatial Interpolation KW - Sequential Learning KW - Convolutional LSTM PY - 2022 AN - OPUS4-56415 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -