Dynamic simulation method of toxic gas diffusion in complex geographic environment

Hu, Lujin; Qiu, Agen; Qian, Xinlin

doi:10.1109/agro-geoinformatics.2015.7248104

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…Also, the Gaussian plume model is the basis of the computer diffusion model, which is a normal model based on statistical theory. In [25], authors used geographic grid calculations to design a simulation method for gas diffusion in complex geographic environments such as wind fields, terrain surface, dry deposition and decay of the gas, etc. It makes the gas diffusion model more applicable to complex environments.…”

Section: Gas Diffusion Modementioning

confidence: 99%

Energy-Efficient Boundary Detection for Continuous Objects in Collaborative Edge Networks

Kang

Zhou

et al. 2021

IEEE Access

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Along with a large and increasing number of resource-constrained and battery-powered smart things being deployed in networks, an energy-efficient mechanism for continuous object boundary detection is fundamental for prolonging the network lifetime. Most of the existing studies focus on how to improve the boundary accuracy of continuous objects while ignoring the energy consumption of smart things in edge networks, which may lead to some smart things being in an unusable state. To address this issue, we propose an Energy-efficient Continuous object boundary Detection Mechanism, namely ECDM. This mechanism consists of two stages: (i) an edge-collaboration monitoring mechanism, where only sensory data relevant to toxic gas are transmitted among multi-edge networks, thus minimizing the number of exchanged sensory data involved in the detection, and reducing the transmission energy consumption of smart things; and (ii) a gas diffusion model is used to predict the diffusion of toxic gases, such that suspicious edge networks can be located quickly and efficiently, further reducing the energy consumption of smart things transmitting anomalous sensory data. Experimental results show that the energy consumption of ECDM is reduced by 34.5% compared to the latest boundary detection method that introduces a gas diffusion model while maintaining detection accuracy.INDEX TERMS Boundary detection, energy efficiency, gas diffusion model, edge networks.

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Section: Gas Diffusion Modementioning

confidence: 99%

Energy-Efficient Boundary Detection for Continuous Objects in Collaborative Edge Networks

Kang

Zhou

et al. 2021

IEEE Access

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“…So the gas concentration value at any time cannot be calculated. However, the Gaussian puff model has the setting of time t, which can simulate the concentration distribution of gas diffusion at any time [21], [22]. Therefore, in this paper, a dynamic diffusion model is established by using Gaussian puff model and combining with the theoretical basis of superposition model.…”

Section: Gas Dynamic Diffusion Modelmentioning

confidence: 99%

“…Considering the complex geographical environment, that is, the comprehensive effects of wind field, terrain surface, dry deposition and decay of gas on atmospheric diffusion concentration are fully considered. A dynamic visualization model of gas diffusion is achieved by improving the Gaussian model [22].…”

Section: Related Work and Comparison A Gas Diffusion Studymentioning

confidence: 99%

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

et al. 2020

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Internet of Things (IoT) has been widely used to facilitate environmental perception, where detecting the boundary regions for continuous objects with energy-efficient manner is a challenge to be explored to support domain applications. This paper proposes a novel approach for continuous objects boundary prediction and detection in IoT sensing network, which is called Cloud Model-IoT Sensing Network Collaborative (CM-IoTSNC). Specifically, when an event is potentially occurred, the atmospheric dynamic diffusion model deployed on the cloud is adopted to predict gas diffusion trend in ideal and complex environments, moreover prediction results are transmitted to the IoT devices in the real-time fashion for scheduling security plans in advance. One-hop neighbor nodes are activated by abnormal nodes to determine a more accurate boundary region. Compared our technique with two traditional methods, namely Wireless Sensor Monitoring and Activating One-hop Neighbor Nodes, experimental results show that our method has a good performance in reducing the energy consumption and prolonging the lifetime of the network. INDEX TERMS Boundary detection, continuous objects, IoT sensing networks, energy efficiency, gas dynamic diffusion model.

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“…When establishing the one‐way regression model and the multiple regression model, it is assumed that the fluid is a homogeneous flow, and the gas does not undergo a chemical reaction during the diffusion process; the ambient temperature is 300 K, the turbulence intensity is 10, the leakage velocity is constant at 2.5 m/s, and the surface roughness has no effect on gas diffusion [7–9].…”

Section: Regression Model Of Gas Diffusionmentioning

confidence: 99%

Visual prediction of gas diffusion concentration based on regression analysis and BP neural network

Wang

Fang

Zhao

2018

J. eng.

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Dynamic simulation method of toxic gas diffusion in complex geographic environment

Cited by 3 publications

References 2 publications

Energy-Efficient Boundary Detection for Continuous Objects in Collaborative Edge Networks

Energy-Efficient Boundary Detection for Continuous Objects in Collaborative Edge Networks

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

Visual prediction of gas diffusion concentration based on regression analysis and BP neural network

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