Sparse Mobile Crowdsensing for Gas Monitoring in Coal Mine Working Face

Gas disaster is one of the major disasters faced by coal mines, and more than 50 in the working face. However, due to the small number of gas monitoring sensors and the heavy workload of artificial inspection of the working face, it is very difficult to monitor the high coverage of the working face. Aiming at the problems of low coverage of monitoring data and high acquisition cost, this paper uses sparse mobile crowdsensing (MCS) to monitor the gas concentration, which specifically involves the cell selection of sensing area and the data inference of gas concentration in unaware area. First, this paper combines gas source distribution and working face air flow characteristics to efficiently divide the gas concentration sensing cells. We propose cell selection based on distributed weighted self-attention mechanism deep reinforcement learning (DWS-DQN). The cell selection algorithm utilizes attention mechanism capture the key states of reinforcement learning to assist in optimization and decisionmaking. Second, we propose gas concentration inference based on diffusion coefficient weighting (DCW). Based on the gas concentration diffusion coefficient of coal mine working face, we weighted the quantitative results of different characteristics to construct the gas concentration inference model. Finally, experiments on two real coal mines sensing datasets verify the effectiveness of our proposed algorithms. Compared to the baseline method, the DWS-DQN model and DCW model both exhibit good performance. The method based on the combination of DWS-DQN and DCW reduces the average MAPE result by 6.87%.