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.

3D models classification is a critical process of Building Information Modeling (BIM). A Deep Learning Approach is proposed to classify 3D models in BIM environment. The ray based feature extraction algorithm is used to extract features of 3D models and form features matrix. The Deep Belief Network constructed by Restricted Boltzmann Machines applies the features matrix and classifies the models adopting the effective training process. The process of training DBN is layer by layer. Experiments were taken on the public 3D model library of PSB model database. The results show that compared with several commonly used classification method, the proposed method of this paper has achieved good results in the 3D model classification for efficiently BIM.