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dc.contributor.authorKhan, Asiya
dc.contributor.authorPemberton, Richard
dc.contributor.authormomen, A
dc.contributor.authorbristow, D
dc.date.accessioned2019-02-08T10:10:39Z
dc.date.issued2019-08-24
dc.identifier.isbn9783030295127
dc.identifier.issn2194-5357
dc.identifier.issn2194-5357
dc.identifier.urihttp://hdl.handle.net/10026.1/13283
dc.description.abstract

Internet of Underwater Things (IoUT) is an emerging field within Internet of Things (IoT) towards smart cities. IoUT has applications in monitoring underwater structures as well as marine life. This paper presents preliminary work where sensor nodes were built on Arduino Uno platform with temperature and pressure sensors with wireless capability. The sensors nodes were then tested in the Flumes of the COAST laboratory to determine the maximum depth achievable in fresh water before the signal is lost as radio frequencies are susceptible to interference under water. Further, the received signals were de-noised using Wavelet Transform, Daubechies thresholding techniques at level 5. Preliminary results suggest that at a depth of 30 cm, signal was lost, de-noising of the signal was achieved with very small errors (a mean squared error of 0.106 and 0.000446 and Peak-Sign-to-Noise Ratios of 70.18 dB and 58.83 dB for the pressure and temperature signals, respectively. Results from this study will lay the foundation to further investigations in wireless sensor networks in IoUT integrating the de-noising techniques.

dc.format.extent1192-1198
dc.language.isoen
dc.publisherSpringer International Publishing
dc.subjectIoUT
dc.subjectSensors
dc.subjectArduino Uno
dc.subjectWavelet Transform
dc.titleDe-Noising Signals using Wavelet Transform in Internet of Underwater Things
dc.typeconference
dc.typeinproceedings
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000628838900085&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume1038
plymouth.conference-nameIntellysis 2019
plymouth.publication-statusPublished
plymouth.journalSpringer series "Advances in Intelligent Systems and Computing"
dc.identifier.doi10.1007/978-3-030-29513-4_85
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2019-01-07
dc.rights.embargodate2020-8-23
dc.identifier.eissn2194-5357
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1007/978-3-030-29513-4_85
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-08-24
rioxxterms.typeConference Paper/Proceeding/Abstract


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