Gas Leakage Source Localization and Boundary Estimation using Mobile Wireless Sensor Network

Kumar, Subhash; Chouksey, Sameer

doi:10.1109/iccakm50778.2021.9357732

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…Simultaneous transfer of data to the cloud for simulation, which takes more time compared to simulation at the edge. When detecting toxic gases, we need to quickly understand the dispersion of the gas to help the relevant departments and reduce unnecessary casualties and property damage [3].…”

Section: ) Compare With Other Methodsmentioning

confidence: 99%

“…With the rapid development of the Internet of Things (IoT) technology, smart things have been widely deployed in edge networks. Smart things can cooperate for environmental monitoring and emergency prevention [1]- [3]. Massive sensory data such as temperature, humidity, and gas concentration can be captured by heterogeneous smart things [4].…”

Section: Introductionmentioning

confidence: 99%

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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: ) Compare With Other Methodsmentioning

confidence: 99%