Wireless Telemetry System for Gas Production

Prakhova, M. Yu.; Khoroshavina, E. A.; Krasnov, A. N.

doi:10.1007/978-3-030-39225-3_2

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…When the scope of chemical hazard area is above the medium scale, considering the risk of toxic and harmful gas leakage, long-distance telemetry of dangerous gases has become one of the important research directions of gas monitoring to ensure the life safety of relevant practitioners. Gas telemetry has been developed for decades and does not require direct contact with the target to track, detect, identify, and estimate concentrations in regions [14,15]. The long-distance telemetry technology developed at this stage is divided into passive telemetry technology, such as Fourier transform infrared spectroscopy [16][17][18][19], and active telemetry technology, such as differential absorption lidar (DIAL) technology [20][21][22][23], Raman scattering spectrum technology [24][25][26][27][28], etc.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Deployment of Mesoscale Chemical Hazard Area Monitoring Points by Combining Weighting and Fireworks Algorithms

Shi¹,

Zhang²,

Chen³

et al. 2023

Sustainability

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In order to address the problems of redundancy and waste of resources in the deployment of monitoring points in mesoscale chemical hazard areas, we propose a method for the deployment of monitoring points in mesoscale chemical hazard areas by combining weight and fireworks algorithms. Taking the mesoscale chemical hazard monitoring area as the research background, we take the probabilistic sensing model of telemetry sensor nodes as the research object, make a reasonable grid division of the mesoscale monitoring area, calculate the importance of each grid and perform clustering, utilize the diversity of the fireworks algorithm and the rapidity of the solution to solve the monitoring point deployment model and discuss the relevant factors affecting the deployment scheme. The simulation results show that the proposed algorithm can achieve the optimal coverage monitoring for monitoring areas with different importance and reduce the number of monitoring nodes and redundancy; meanwhile, the relevant factors such as the grid edge length, the number of clusters, and the average importance of monitoring areas have different degrees of influence on the complexity of the algorithm and the deployment scheme.

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