Metal oxide semiconductor (MOS) gas sensors offer several advantages, including low cost, high accuracy, and ease of miniaturization. Thus, they are excellent candidates for environmental monitoring and food spoilage detection applications, particularly in the safe Internet of Things field or for portable instruments. Typically, there are two general routes for realizing low-power-consumption MOS gas sensors: room-temperature MOS gas sensors or MEMS MOS gas sensors. The review focuses on the detection of four typical gases, namely methane, hydrogen, carbon monoxide, and nitrogen dioxide, systematically summarizing and analyzing the most recent results of low-power-consumption MOS gas sensors. The 2D materials, MOS composites, and 3D structured composites exhibit excellent room-temperature gas detection capabilities. The mechanism of the room-temperature gas sensors is also discussed in detail. Another route is MEMS MOS gas sensors. First, the progress of the micro-hotplate research is introduced. Then, several of the latest reported MEMS MOS gas sensors are shown and compared. The gas sensing mechanism of these MEMS MOS gas sensors is also given. The paper will provide a valuable guide for researchers in the MOS gas sensor field, particularly for those working towards low-power-consumption MOS gas sensors.

As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH) is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.

Feature selection is to obtain effective features from data, also known as feature engineering. Traditional feature selection and predictive model learning are separated, and there is a problem of inconsistency of criteria. This paper presents an end-to-end feature selection and diagnosis method that organically unifies feature expression learning and machine prediction learning into one model. The algorithm first combines the prediction model to calculate the mean impact value (MIVs) of the feature and realizes primary feature selection for the prediction model by selecting the feature with a larger MIV. In order to take into account the performance of the feature itself, the within-class and between-class discriminant analysis (WBDA) method is proposed, and combined with the feature diversity strategy, the feature-oriented secondary selection is realized. Eventually, feature vectors obtained by two selections are classified using a multi-class support vector machine (SVM). Compared with the modified network variable selection algorithm (MIVs), the principal component analysis dimensionality reduction algorithm (PCA), variable selection based on compensative distance evaluation technology (CDET), and other algorithms, the proposed method MIVs-WBDA exhibits excellent classification accuracy owing to the fusion of feature selection and predictive model learning. According to the results of classification accuracy testing after dimensionality reduction on rotating machinery status, the MIVs-WBDA method has a 3% classification accuracy improvement under the low-dimensional feature set. The typical running time of this classification learning algorithm is less than 10 s, while using deep learning, its running time will be more than a few hours.

High-performance microelectromechanical system (MEMS) oxygen sensors were realized by successful preparation of Au nanofilms over TiO₂ thin films through successive sputtering on commercial MEMS microhotplates. Oxygen sensing performance of 3 and 6 nm thick Au over TiO₂ thin films were compared with that of pure TiO₂ thin films. It was shown that 6 nm thick Au over TiO₂ thin films have the best sensitivity toward oxygen. The prepared TiO₂ thin films were characterized using SEM, EDS, XPS, and a gas testing instrument. The results show that Au decoration has little influence on the surface morphologies of TiO₂ thin films. However, Au decoration has a strong influence on the surface properties of the composite films. The favorable performance of 6 nm Au-doped TiO₂ thin films is attributed to factors such as catalytical performance, height of Schottky contact, and number of oxygen vacancies. This work makes contributions to low power consumption and high-performance oxygen sensors for Internet of Things applications.

Due to the increasing deployment of the Internet of Things (IoT) in the mining industry, portable gas monitoring devices have been widely used. Sensor calibration of large-scale portable gas monitoring devices is becoming an urgent problem to be solved. An online sensor calibration algorithm based on n-tuple and opportunistic communication is proposed based on the specific characteristics (i.e., ‘single-sensor, multi-position’ and ‘multi-sensor, single-position’) of each portable gas monitoring device employed. In this paper, data collected from portable and fixed sensors were defined as multi-dimensional data points and gas monitoring data pairs, respectively. The cluster-based self-adaptive weighted data fusion algorithm and multi-period single sensor reliability fusion algorithm were proposed and used for overall judging. The overall judgments were broadcast to each wireless access point by network, and the reliability of the calibration information transmission was enhanced by opportunistic communications. The simulation results revealed that efforts required for the calibration of portable sensors were reduced significantly, and their reliability was improved.