## 1 Introduction  
During the application of both a coal mine Internet of Things (IoT) mine and many mobile measurement devices (e.g., intelligent miner's lamp), a large amount of moving measurement data is generated, and transferring these amounts of monitoring data efficiently is a challenge. The moving measurement data in mine IoT include mobile gas measurement data, mine equipment multi-channel measurement data, distributed mine monitoring data and mobile target location data. Different types of mobile data transmission and processing technologies have similarities. This paper presents a compressed sensing algorithm for coal mine moving measurement data based on a multi-hop network and total variation by taking gas data in the mobile measurement data as an example. Underground gas is a kind of gas resource stored in coal and rocks; it gushes out during the mining of coal and causes a gas outburst under certain conditions. Gas explosion poses grave threats to coal's safe production. The coal mine safety monitoring system needs to monitor the environmental parameters such as gas concentrations, wind speed, dust concentrations and so on. The traditional gas sensors are basically fixed-point detection sensors, so the amount of monitoring data is not great. With the development of mine IoT, a great number of intelligent miner's lamps are put into use. For example, in a large mine, there are approximately a thousand people who work underground every day. The miner's lamp and cap are necessary for workers to work underground and gas sensors are in the cap and move with the miners. It can be regarded as a mobile measurement system for underground gas. Compared with the fixed gas sensors, the application of the mobile gas sensors not only expands the monitoring range, but also can obtain the variation curve of gas with time in different positions along the coal mine roadway. In a smart cities environment, a fog-supported smart city network (FOCAN) architecture for management of everything is proposed [1], which can provide services with low energy usage. [PERSON] et al. studied the barrier coverage in wireless sensor networks for intrusion detection and border surveillance [2]. The network in an underground coal mine is an industrial Ethernet, which is a relatively closed network, and the underground access users are authorized users. Miners work underground for no more than 10 h, and every miner's lamp will be fully recharged before he goes down the mine, so energy conservation is not the main problem. At the same time, the coal mine roadway is a long linear structure, which makes the network structure different from that in ground application. As intelligent miner's lamps became widespread in mines, the amount of moving measurement data increased dramatically. How to achieve high-efficiency acquisition and transmission of gas data flow appears to be an urgent problem that needs to be solved for underground distributed mobile gas detection systems. For scalable distributed in-network computation, data compression proves to be an effective solution for achieving high-efficiency transmission and storage of gas monitoring data. The SensorScope project undertaken by the Lausanne Federal Institute, Switzerland, used a wireless sensor network for monitoring environmental information [3]. [PERSON] et al. developed a gas data compression method for gas time series based on prediction and run-length coding [4]. To overcome the inefficiency of intelligent algorithms induced by the high-dimensional characteristics of coal gas data, [PERSON] et al. proposed a graph-based parameter-free dimensionality reduction algorithm and a locality-preserving semi-supervised dimensionality reduction algorithm [5]. A sub-band energy adaptive data compression (SEADC) method in wireless network was proposed by [PERSON] et al. [6]. According to the data-streaming characteristics in the sensor network, [PERSON] et al.