2019
DOI: 10.1109/access.2019.2949100
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Optimal Multi-Base-Station Selection for Belt-Area Wireless Sensor Networks

Abstract: Since underground environments, such as urban subways, tunnels, underground pipe corridors, and mine roadways, etc., are complex and changeable, the software functions of the sensor nodes in underground spaces must be updated regularly to satisfy the requirements of the monitoring center. Software updates cannot be traditionally conducted due to the large number of nodes, the wide distribution range and the poor underground environment; they can only be made using wireless reprogramming. Current reprogramming … Show more

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Cited by 2 publications
(3 citation statements)
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“…Z. Duan et al (2019) the author explored the underground wireless sensor network using multiple base station activated belt-area network. The system is developed with gradient descent location mapping algorithm.…”
Section: Background Studymentioning
confidence: 99%
See 1 more Smart Citation
“…Z. Duan et al (2019) the author explored the underground wireless sensor network using multiple base station activated belt-area network. The system is developed with gradient descent location mapping algorithm.…”
Section: Background Studymentioning
confidence: 99%
“…The coverage is increased and node failure is reduced with the presented method. The drawback of the presented approach is handling the dynamic nodes in the network [9]. The soil moisture level is detected through uncrewed aircraft system(UAS).…”
Section: Background Studymentioning
confidence: 99%
“…The power beam splitter, which can distribute the energy of incident light evenly to multiple output directions, is called a multi-port beam splitter [1][2][3][4][5]. These beam splitters are very common optical elements that can be used in couplers [6][7][8][9], quantum teleportation [10], polarizers [11][12][13][14] and polarization detection systems [15,16]. For gratings, the energies of splitting orders can be modulated for various applications [17][18][19][20].…”
Section: Introductionmentioning
confidence: 99%