Gas Sensor Network Research Articles

Gas Sensor Physically Unclonable Function-Based Lightweight Bidirectional Authentication Protocol for Gas Sensor Networks

In gas sensor networks, users can access the data collected by the sensor nodes, but there is a risk of data leakage during transmission. This paper proposes a lightweight bidirectional authentication protocol based on gas sensor physically unclonable functions (GS-PUFs) with authentication technology to guarantee the reliability of data from sensor nodes. A sensor PUF array is constructed by preparing gas sensors to enhance the data security of the physical layer and reduce hardware resource consumption. The authentication part of the protocol mainly uses lightweight encryption methods, consisting of PUF data, one-way cryptographic hash functions, and iso-or functions, to reduce the computational overhead of the authentication process. The protocol security is enhanced by encrypting the GS-PUF response as an irreversible hash value and verifying the hash value by the user, server, and sensor node to complete bidirectional authentication. The test results demonstrate that the protocol, verified through the ProVerif formal tool, can resist impersonation, replay, node tampering, and cloning attacks. Among the compared schemes, this protocol offers the highest security and the least resource overhead, making it effectively applicable in the Internet of Things and other fields.

FTM-GCN: A novel technique for gas concentration predicting in space with sensor nodes

Accurate and real-time prediction of gas concentrations is a critical component of intelligent prediction systems and holds significant importance for production safety and quality of life. However, due to the constraints of the network topology in gas sensor networks and the temporal nature of gas concentration variations, concentration prediction has always been considered a significant challenge. To simultaneously capture the spatiotemporal correlations in gas concentration data with spatiotemporal characteristics, this study proposes a fully-connected temporal multilayer graph convolutional network (FTM-GCN). This method combines multiple graph convolutional layers (MGC), fully connected layers, and gated recurrent units (GRU). FTM-GCN exhibits three prominent features: (1) It leverages MGC to learn the topology of gas sensor networks, enhancing the model's ability to graph data and capture spatial characteristics. MGC is incorporated to capture spatial features in the data. (2) GRU is employed to capture the dynamic changes in sensor network data and the temporal characteristics of the data. (3) Continuous fully-connected layers are introduced to enhance the model's performance. Experimental results demonstrate that FTM-GCN effectively captures the spatiotemporal correlations in spatiotemporal data across various prediction perspectives and outperforms GCN, GRU, T-GCN and STGCN.

MONITORING AND ALERTING OF HAZARDOUS GASES IN MUNICIPAL SOLID WASTE USING MACHINE LEARNING TECHNIQUE

Abstract-In recent times, the rate of urbanization is proliferating; thus, there is a need to find a sustainable way for an urban development plan. There is rapid growth in the concept of smart cities all around the globe. A smart city is always incomplete without a smart waste monitoring structure that can manage the waste collection of the entire city. This paper proposes a Forest Fire alerting system, which keeps track of the hazardous gases produced by dumped waste by integrating a network of gas sensors. Land fill sites collect tons of municipal solid waste (MSW) using an open dump mechanism, causing gases to emerge, which may cause disease and the greenhouse effect. Mainly, landfill environments are observed using a portable system that does not continuously monitor and measure emitted gas levels. It is also difficult to evaluate changes in landfill emissions over the long term unless they are monitored at regular intervals according to a detailed plan. This paper presents a new monitoring method to measure gas levels in landfill sites, which documents dynamic changes in gas composition concentrations over the long term. The system was placed in the middle area of the landfill and was charged using solar panels for convenience and greater efficiency during monitoring.

Preparing for the next pandemic: Minimizing airborne transmission in general inpatient wards through management practices

Existing infection control studies in hospitals focused on rooms with special ventilation requirements. Study on proper management practices and ventilation strategies in general inpatient wards are critical but currently lacking. To identify the simple operational practices that can limit airborne transmission within a general inpatient ward with the patient cubicle, nursing station and corridor, this study investigates the effects of infected patient locations, air change rates (ACH) and door opening angles on bioaerosol dispersion using a novel tracer gas sensor network. Experimental results show that the supply inlet and infected patient locations significantly affects the distribution and dispersion of the tracer gas within the ward. Using a higher ventilation rate to achieve a lower average airborne pathogen concentration can cause more mixing of air and a wider dispersion of airborne pathogens. Localization of bioaerosols near the source through ventilation controls, a low ACH and proper patient location near the exhaust can minimize the air turbulence and the spread and reduce the infection risks of the susceptibles. Using physical partitions or objects as shields against airborne contaminants can unpredictably influence the airflow patterns, airflow evaluations should hence be done on a case-by-case basis. The methodology established in this study puts forward an economical and fast way for evaluating airborne infection risk, and the experimental results can be useful references for building engineers and hospital facility managers to formulate proper strategies for risk assessment and infection control.

An IoT-Enabled E-Nose for Remote Detection and Monitoring of Airborne Pollution Hazards Using LoRa Network Protocol.

Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial intelligence, detect the presence of various VOCs, gases, and smokes onsite. Widespread monitoring of airborne hazards across many remote locations is possible by creating a network of gas sensors using Internet connectivity, which consumes significant power. Long-range (LoRa)-based wireless networks do not require Internet connectivity while operating independently. Therefore, we propose a networked intelligent gas sensor system (N-IGSS) which uses a LoRa low-power wide-area networking protocol for real-time airborne pollution hazard detection and monitoring. We developed a gas sensor node by using an array of seven cross-selective tin-oxide-based metal-oxide semiconductor (MOX) gas sensor elements interfaced with a low-power microcontroller and a LoRa module. Experimentally, we exposed the sensor node to six classes i.e., five VOCs plus ambient air and as released by burning samples of tobacco, paints, carpets, alcohol, and incense sticks. Using the proposed two-stage analysis space transformation approach, the captured dataset was first preprocessed using the standardized linear discriminant analysis (SLDA) method. Four different classifiers, namely AdaBoost, XGBoost, Random Forest (RF), and Multi-Layer Perceptron (MLP), were then trained and tested in the SLDA transformation space. The proposed N-IGSS achieved "all correct" identification of 30 unknown test samples with a low mean squared error (MSE) of 1.42 × 10-4 over a distance of 590 m.

Wireless Self-Powered High-Performance Integrated Nanostructured-Gas-Sensor Network for Future Smart Homes

The accelerated evolution of communication platforms including Internet of Things (IoT) and the 5th generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks to monitor the chemical safety of our surroundings and personal health in real time. However, this application scenario requires a very challenging combination of characteristics of gas sensors including small formfactor, low-cost, ultra-low power consumption, superior sensitivity, and high intelligence. Herein, we demonstrate self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network for the first time. The SINGOR features ultra-high sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10 ppb, 2 ppb, 1 ppb and 1 ppb, respectively, obtained under room temperature sensor operation. Therefore, ultra-low power consumption of 68.6 μW at sensor level and 1.71 mW at the system level has been achieved. As the result, an individual SINGOR system can be self-driven by indoor light with a Si solar cell. Furthermore, each SINGOR consists of an array of nanostructured sensors powered by machine learning algorithm and thus a SINGOR has the capability of gas pattern recognition and classification. And multiple SINGOR systems are wirelessly connected together to form a sensor network for smart home application demonstrations. Intriguingly, the sensor network has successfully demonstrated flammable gas leakage detection and alarm function when they are deployed in multiple rooms. And when the SINGOR network is deployed in one single room, it can achieve gas leakage localization with satisfactory precision. These successes open up the possibility of using nanostructured-gas-sensor network for wide range applications including smart home, smart building and future smart city.

Prediction of gas concentration evolution with evolutionary attention-based temporal graph convolutional network

Accurate prediction of gas concentration is of great importance in many safe-based systems and applications. However, prediction accuracy of gas concentration is limited by not only the temporal evolution of gas concentration but also the spatial characteristics of gas dispersion. To capture the spatial and temporal dependences simultaneously, an evolutionary attention-based temporal graph convolutional network (EAT-GCN) is proposed, which has three outstanding features: (1) graph convolutional network (GCN) is used to capture spatial dependence by learning topological structures of a gas sensor network; (2) gated recurrent unit (GRU) is adopted to retain temporal dependence by learning dynamic changes of gas concentration, and (3) evolutionary attention is introduced to improve the ability of GRU to pay different degrees of attention to the sub-window features within multiple time steps. Finally, kernel extrapolation distribution mapping algorithm is employed to visualize the predicted results of gas concentration and update the gas distribution map. Compared with CNN, GCN, GRU, T-GCN, A3T-GCN and EA-GRU models, the proposed EAT-GCN model improves the prediction accuracy by 13.46%, 124.21%, 33.92%, 23.39%, 46.63%, and 23.97%, respectively. Experiments demonstrate that the designed model captures spatiotemporal correlation from gas concentration data and achieves better prediction accuracy than state-of-the-art baseline methods.

Identification of Diabetes through Urine Using Gas Sensor and Convolutional Neural Network

Identification of Diabetes through Urine Using Gas Sensor and Convolutional Neural Network

Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References

There has been a wide interest in high-resolution air quality monitoring with low-cost gas sensor systems in the last years. Such gas sensors, however, suffer from cross-sensitivities, interferences with environmental factors, unit-to-unit variability, aging, and concept drift. Therefore, reliability and trustworthiness of the measurements in the low parts-per-billion (ppb) range remain a concern, particularly over the course of the lifetime of a sensor network in urban environments. In this simulation study, the possibility to continuously recalibrate a wireless sensor network with mobile references and stochastic gradients, computed from encounters, is explored. By using data collected in field experiments, encounters between static and mobile nodes are modeled as a probabilistic process. Moreover, the influence of a collection of design parameters such as base calibration, initial recalibration, choice of optimization algorithm, as well as encounter frequency are analyzed and discussed. With an optimized protocol, it can be shown that long-term reliable measurements with absolute errors of about 50 ppb for CO, 3 ppb for NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and 4 ppb for O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> could be achievable with a few mobile references in urban environments.

Wireless Self-Powered High-Performance Integrated Nanostructured-Gas-Sensor Network for Future Smart Homes.

The accelerated evolution of communication platforms including Internet of Things (IoT) and the fifth generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks for real-time monitoring chemical safety and personal health. However, this application scenario requires a challenging combination of characteristics of gas sensors including small formfactor, low cost, ultralow power consumption, superior sensitivity, and high intelligence. Herein, self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network are demonstrated here. The room-temperature operated SINGOR system can be self-driven by indoor light with a Si solar cell, and it features ultrahigh sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10, 2, 1, and 1 ppb, respectively. Each SINGOR consisting of an array of nanostructured sensors has the capability of gas pattern recognition and classification. Furthermore, multiple SINGOR systems are wirelessly connected as a sensor network, which has successfully demonstrated flammable gas leakage detection and alarm function. They can also achieve gas leakage localization with satisfactory precision when deployed in one single room. These successes promote the development of using nanostructured-gas-sensor network for wide range applications including smart home/building and future smart city.

Policy-Relevant Assessment of Urban CO2 Emissions.

Global fossil fuel carbon dioxide (FFCO2) emissions will be dictated to a great degree by the trajectory of emissions from urban areas. Conventional methods to quantify urban FFCO2 emissions typically rely on self-reported economic/energy activity data transformed into emissions via standard emission factors. However, uncertainties in these traditional methods pose a roadblock to implementation of effective mitigation strategies, independently monitor long-term trends, and assess policy outcomes. Here, we demonstrate the applicability of the integration of a dense network of greenhouse gas sensors with a science-driven building and street-scale FFCO2 emissions estimation through the atmospheric CO2 inversion process. Whole-city FFCO2 emissions agree within 3% annually. Current self-reported inventory emissions for the city of Indianapolis are 35% lower than our optimal estimate, with significant differences across activity sectors. Differences remain, however, regarding the spatial distribution of sectoral FFCO2 emissions, underconstrained despite the inclusion of coemitted species information.

Optimized deployment method and performance evaluation of gas sensor network based on field experiment

In order to prevent gas leakage, gas sensors are routinely deployed in chemical industrial parks to monitor it. However, because of the effects of weather conditions, such as wind speed and direction, these gas sensors may not be able to accurately monitor the concentration of the gas leak. Toxic and harmful gases not only do harm to the surrounding environment, but also to the health of nearby residents. In order to effectively monitor the occurrence of gas leakage, in this paper, a plan of setting up wireless sensor network on site is designed, which is based on the meteorological and gas characteristics. What’s more, the plan includes the rectangle plan, the fan plan and the annular plan. Also, it stipulates the optimization standard of sensor node position and deployment scheme and field experiments were carried out in the chemical industrial park, whereby the monitoring values of gas concentration in different schemes were obtained. According to the standards, the optimal deployment scheme and the optimal location of sensor nodes in different schemes are also procured. The results show that the method is feasible and effective. It can optimize the deployment position of different elevation under certain wind speed and wind direction as well as optimize different solutions.

Development and evaluation of a robust temperature sensitive algorithm for long term NO2 gas sensor network data correction

Abstract Low-cost sensors (LCS) for air-quality monitoring have shown huge potential in enhancing spatial and temporal resolutions at a lower cost, greater flexibility in use and with less maintenance than fixed-air quality monitoring stations (AQMS). There are numerous reports showing sensor-based systems perform well under laboratory conditions. However, the accuracy of LCS operated under field conditions has been reported to deviate from their use in these controlled environments. Previous studies have employed various mathematical techniques to improve system performance. These include classic multivariate regression model (MLR) to incorporate ambient factors (e.g. temperature & relative humidity) and machine learning (ML) models for integrating complex non-linear sensor responses for gaseous pollutant concentration predictions. However, routine field calibrations may cause data inconsistency during measurement periods. This study first illustrates the limitations of short-term routine calibration by comparing performances of existing MLR and ML models. Further, based on a long-term evaluation period, a newly principle-based method named Temperature Look-Up (TLU) model was built and compared with the existing MLR and ML models. Measurements were taken for 8 months (August 2017 to March 2018) in SanMenXia (SMX) city, China, using 8 sets of sensor systems (Mini Air Stations, MAS). One MAS was co-located with AQMS to provide data to assess the principle, whereas the other 7 MAS were used for verification. The TLU model showed the best performance for long term validation (4 months) and high model inner coherence (R2 > 0.91). Meanwhile, the TLU model also shows the fast convergence (40 days) to reach stable prediction performance, and spatial representativeness in the sensor network. Recommendations on sensor network deployment and strategy for data maintenance based on our observations are discussed.

Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments.

Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) are a variant of recurrent neural networks (RNN) that can extract spatial features in addition to classifying or making predictions from sequential data. In this paper, we analyzed the use of CNN-LSTM for gas source localization (GSL) in outdoor environments using time series data from a gas sensor network and anemometer. CNN-LSTM is used to estimate the location of a gas source despite the challenges created from inconsistent airflow and gas distribution in outdoor environments. To train CNN-LSTM for GSL, we used temporal data taken from a 5 × 6 metal oxide semiconductor (MOX) gas sensor array, spaced 1.5 m apart, and an anemometer placed in the center of the sensor array in an open area outdoors. The output of the CNN-LSTM is one of thirty cells approximating the location of a gas source. We show that by using CNN-LSTM, we were able to determine the location of a gas source from sequential data. In addition, we compared several artificial neural network (ANN) architectures as well as trained them without wind vector data to estimate the complexity of the task. We found that ANN is a promising prospect for GSL tasks.

A Wireless Gas Sensor Network to Monitor Indoor Environmental Quality in Schools.

Schools are amongst the most densely occupied indoor areas and at the same time children and young adults are the most vulnerable group with respect to adverse health effects as a result of poor environmental conditions. Health, performance and well-being of pupils crucially depend on indoor environmental quality (IEQ) of which air quality and thermal comfort are central pillars. This makes the monitoring and control of environmental parameters in classes important. At the same time most school buildings do neither feature automated, intelligent heating, ventilation, and air conditioning (HVAC) systems nor suitable IEQ monitoring systems. In this contribution, we therefore investigate the capabilities of a novel wireless gas sensor network to determine carbon dioxide concentrations, along with temperature and humidity. The use of a photoacoustic detector enables the construction of long-term stable, miniaturized, LED-based non-dispersive infrared absorption spectrometers without the use of a reference channel. The data of the sensor nodes is transmitted via a Z-Wave protocol to a central gateway, which in turn sends the data to a web-based platform for online analysis. The results show that it is difficult to maintain adequate IEQ levels in class rooms even when ventilating frequently and that individual monitoring and control of rooms is necessary to combine energy savings and good IEQ.

Evolution of Wireless Sensor Network for Air Quality Measurements

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).

Gas sensors for climate research

Abstract. The availability of datasets providing information on the spatial and temporal evolution of greenhouse gas concentrations is of high relevance for the development of reliable climate simulations. However, current gas detection technologies do not allow for obtaining high-quality data at intermediate spatial scales with high temporal resolution. In this regard the deployment of a wireless gas sensor network equipped with in situ gas analysers may be a suitable approach. Here we present a novel, non-dispersive infrared absorption spectroscopy (NDIR) device that can possibly act as a central building block of a sensor node to provide high-quality data of carbon dioxide (CO2) concentrations under field conditions at a high measurement rate. Employing a gas-based, photoacoustic detector we demonstrate that miniaturized, low-cost, and low-power consuming CO2 sensors may be built. The performance is equal to that of standard NDIR devices but at a much reduced optical path length. Because of the spectral properties of the photoacoustic detector, no cross-sensitivities to humidity exist.

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

Gas sensors have been widely reported for industrial gas detection and monitoring. However, the rapid detection and identification of industrial gases are still a challenge. In this work, we measure four typical industrial gases including CO2, CH4, NH3, and volatile organic compounds (VOCs) based on electronic nose (EN) at different concentrations. To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE) to reduce the dimensionality and extract the features of high-dimensional data. Combining the Euclidean distance (ED) formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases. We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms. In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data. It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.

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 disaster

A Deterministic Sensor Deployment Method for Target Coverage

In order to monitor the gas leakage, the gas sensors are deployed conventionally in chemical industry park, with little considerations given to the gas characteristics and weather conditions, which give rise to the problems of coverage hole and coverage repetition. To solve the problems, this paper proposes a deterministic sensor deployment method with the gas diffusion models which takes into account wind speed and direction and then studies the influence of wind speed and direction on the monitoring error of gas sensors. Then, we research the deterministic deployment method of gas sensors in condition of the main wind speed and direction somewhere. Firstly, we use the CFD theory to simulate the gas diffusion situation so as to obtain the concentration value of the relevant points. Secondly, we put forward a new optimization criterion, namely, the more alarm concentration points covered by gas sensors, the coverage performance is better, and the deployment method is better. Accordingly, a new objection function is built. Thirdly, we obtain the weight values of the function using entropy estimation method. Finally, we deploy the gas sensors determinately using particle swarm optimization (PSO) algorithm. The simulation results show that the proposed method can improve the monitoring efficiency and the coverage performance of gas sensor network.