The National Joint Engineering Laboratory of Internet Applied Technology of Mines, Xuzhou 221000, China.
IOT Perception Mine Research Center, China University of Mining and Technology, Xuzhou 221000, China.
Sensors (Basel). 2018 May 28;18(6):1732. doi: 10.3390/s18061732.
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.
作为煤矿物联网（IoT）的应用，移动测量设备，如智能矿灯，导致移动测量数据增加。如何有效地传输这些大量的移动测量数据已成为一个紧迫的问题。本文提出了一种基于多跳网络和全变差的煤矿物联网海量移动测量数据压缩感知算法。以移动测量数据中的瓦斯数据为例，深入研究了瓦斯数据流传输的两种网络模型，即单跳和多跳传输模式，并基于多跳网络构建了瓦斯数据压缩感知采集模型。为了利用瓦斯数据的稀疏特性，引入了全变差的概念，提出了一种基于多跳的基于全变差稀疏的高效瓦斯数据压缩和重建方法（TVS-MH）。根据仿真结果，使用所提出的方法可以有效地采集和传输来自地下分布式移动网络的移动测量数据流。
