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Channel modeling and testing of wireless transmission for underground in-pipe leak and material loss detection

Author(s)
Mekid, S; Hussain, R; Wu, Dan; Youcef-Toumi, Kamal
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Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
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Abstract
A systematic real-time methodology is adopted for leak detection in underground buried pipes. The wireless communication system is used to analyze the system performance based on the received power by monopole antenna deployed at the receiving side. Instrumentation designed for underground measurement and control such as leak and materials loss detection needs wireless communications to aboveground in both ways and in real-time mode. This constitutes one of the timely and challenging issues of battery-operated systems. The purpose of this work is to characterize the radio transmission between underground buried pipes and base station using multi-layer media including both theoretical and experimental approaches by utilizing various modulation schemes. The objective is to identify the range of operating communication frequencies having lower energy loss, lower resulting bit error rate, and the power needed to transfer packets designed to carry data through the media. This will support the on-device power management to secure large autonomy operations. Experimental tests have shown that the overall received energy was mixed with ambient energy if the latter is sent at the same frequency and that the optimum frequency range used to transmit energy was rather at low frequency range of 100–200 MHz. Keywords: Wireless communication; in-pipe leak; material loss; sensor network; buried pipes
Date issued
2017-11
URI
http://hdl.handle.net/1721.1/120059
Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Journal
International Journal of Distributed Sensor Networks
Publisher
SAGE Publications
Citation
Mekid, S. et al. “Channel Modeling and Testing of Wireless Transmission for Underground in-Pipe Leak and Material Loss Detection.” International Journal of Distributed Sensor Networks 13, 11 (November 2017): 155014771774471 © 2017 The Author(s)
Version: Final published version
ISSN
1550-1477
1550-1329

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