AI Article Synopsis
- Indoor spaces with low air exchange can become contaminated by harmful volatile compounds, highlighting the need for effective monitoring.
- A new system using Machine Learning processes data from a low-cost wearable VOC sensor within a Wireless Sensor Network (WSN), which includes fixed anchor nodes for mobile device localization.
- Testing in a 120 m indoor area showed over 99% localization accuracy, enabling the mapping of ethanol distribution and confirming the sensor's effectiveness in detecting and locating VOC sources.
Article Abstract
Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007132 | PMC |
| http://dx.doi.org/10.3390/s23052457 | DOI Listing |
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