Evolution of wireless sensor network for air quality measurements

Este estudio aborda el desarrollo de una red de sensores de gas inalámbricos con nodos de bajo costo, tamaño pequeño y bajo consumo para aplicaciones ambientales y detección de la calidad del aire. A lo largo del artículo se presenta la evolución del diseño y desarrollo del sistema, describiendo cuatro prototipos diseñados. El nodo prototipo final propuesto tiene la capacidad de conectar hasta cuatro sensores de gas de óxido metálico (MOX), y tiene una gran autonomía gracias al uso de paneles solares, además de contar con un sistema de muestreo indirecto y de pequeño tamaño. El protocolo ZigBee se utiliza para transmitir datos de forma inalámbrica a una nube de datos de desarrollo propio. La capacidad de discriminación del dispositivo se comprobó con los compuestos orgánicos volátiles benceno, tolueno, etilbenceno y xileno (BTEX). Se logró una mejora del sistema para obtener tasas de éxito óptimas en la etapa de clasificación con el prototipo final. El procesamiento de los datos se llevó a cabo utilizando técnicas de reconocimiento de patrones e inteligencia artificial, como las redes de base radial y el análisis de componentes principales (PCA).This study addresses the development of a wireless gas sensor network with low cost, small size, and low consumption nodes for environmental applications and air quality detection. Throughout the article, the evolution of the design and development of the system is presented, describing four designed prototypes. The final proposed prototype node has the capacity to connect up to four metal oxide (MOX) gas sensors, and has high autonomy thanks to the use of solar panels, as well as having an indirect sampling system and a small size. ZigBee protocol is used to transmit data wirelessly to a self-developed data cloud. The discrimination capacity of the device was checked with the volatile organic compounds benzene, toluene, ethylbenzene, and xylene (BTEX). An improvement of the system was achieved to obtain optimal success rates in the classification stage with the final prototype. Data processing was carried out using techniques of pattern recognition and artificial intelligence, such as radial basis networks and principal component analysis (PCA).• Ministerio de Economía y Competitividad. Proyecto TEC2013-48147-C6-5-R • Junta de Extremadura. Proyecto IB16048peerReviewe

Design and implementation of a wearable gas sensor network for oil and gas industry workers

Industrial environment usually involves some types of hazardous substances including toxic and/or flammable gases. Accidental gas leakage can cause potential dangers to a plant, its employees and surrounding neighborhoods. Around 64% of accidents that happen in the oil fields are due to combustibles and/or toxic gases. The safety plan of most industries includes measures to reduce risk to humans and plants by incorporating early-warning devices, such as gas detectors. Most existing tools for monitoring gases are stationary and incapable of accurately measuring individual exposures that depend on personal lifestyles and environment. This paper provides a design and implementation of a wearable gas sensor network by building sensor nodes with wireless communication modules which communicate their data along the network. The system is designed to be flexible, low cost, low maintenance and with accurate performance to detect toxic gases in a timely fashion to warn employees before an existence of a disaste

An iot-based smart building solution for indoor environment management and occupants prediction

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939)

Continuous monitoring of volatile organic compounds through sensorization. Automatic sampling during pollution/odour/nuisance episodic events

Volatile organic compounds (VOCs) are a highly diverse class of chemical contaminants and between 50 and 300 of them may be found in ambient air. In urbanized areas, VOCs are emitted from industrial activities, as well as from vehicle-related and combustion sources. VOCs outdoors can be detected in a broad range of concentrations, usually varying seasonally. The presence of VOCs at relatively high concentrations has been related to poor air quality, discomfort and odorous nuisances. Additionally, they can have negative health effects to the human organism. Hence, in locations where recurrent sporadic situations of high VOCs levels take place, episodic samples' evaluation is necessary instead of 24 h or longer sampling period's evaluations. The use of commercially available metal oxide semiconductor gas sensors for a continuous monitoring of VOCs concentrations in outdoor air is an interesting and innovative technology. Additionally, the use of these sensors for the activation of a VOCs sampler when episodic events of nuisance/odorous annoyance occur was successfully evaluated. The sensor activation is induced by higher VOCs concentrations from a wide number of VOC chemical families. Two sensor stations, developed at our laboratory and provided with sampling pumps, were located in the municipality of Santa Margarida i els Monjos (Catalunya, Spain) in January 2021. The stations started recording data continuously from two different types of VOCs sensors, temperature, relative humidity and pressure in 1.5-min periods. Automatic VOCs sampling was conducted, using multi-sorbent bed tubes, during the months of June–July when the sensors electronic values exceeded a set point value. Samples were analysed through TD-GC/MS. TVOC concentrations in episode samples ranged between 78-669 and 12–159 µg m-3 in Site 1 and Site 2, respectively. Although TVOC concentrations were not high in all cases, relevant concentrations of chloroform were observed, especially in Site 1, with concentrations ranging from 19 to 159 µg m-3.Postprint (published version

Realtime gas emission monitoring at hazardous sites using a distributed point-source sensing infrastructure

This paper describes a distributed point-source monitoring platform for gas level and leakage detection in hazardous environments. The platform, based on a wireless sensor network (WSN) architecture, is organised into sub-networks to be positioned in the plant’s critical areas; each sub-net includes a gateway unit wirelessly connected to the WSN nodes, hence providing an easily deployable, stand-alone infrastructure featuring a high degree of scalability and reconfigurability. Furthermore, the system provides automated calibration routines which can be accomplished by non-specialized maintenance operators without system reliability reduction issues. Internet connectivity is provided via TCP/IP over GPRS (Internet standard protocols over mobile networks) gateways at a one-minute sampling rate. Environmental and process data are forwarded to a remote server and made available to authenticated users through a user interface that provides data rendering in various formats and multi-sensor data fusion. The platform is able to provide real-time plant management with an effective; accurate tool for immediate warning in case of critical events

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected