De-noising signals using wavelet transform in internet of underwater things

Asiya Khan; Richard Pemberton; Abdul Momen; Daniel Bristow

doi:10.1007/978-3-030-29513-4_85

De-noising signals using wavelet transform in internet of underwater things

Asiya Khan^*
, Richard Pemberton
, Abdul Momen
, Daniel Bristow

^*Corresponding author for this work

University of Plymouth

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings published in a book › peer-review

22 Downloads (Pure)

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.

Original language	English
Title of host publication	Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2
Publisher	Springer Verlag
Pages	1192-1198
Number of pages	7
ISBN (Print)	9783030295127
DOIs	https://doi.org/10.1007/978-3-030-29513-4_85
Publication status	Published - 24 Aug 2019
Event	Intelligent Systems Conference, IntelliSys 2019 - London, United Kingdom Duration: 5 Sept 2019 → 6 Sept 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1038
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Intelligent Systems Conference, IntelliSys 2019
Country/Territory	United Kingdom
City	London
Period	5/09/19 → 6/09/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Keywords

Arduino Uno
IoUT
Sensors
Wavelet Transform

Access to Document

10.1007/978-3-030-29513-4_85

IntelliSys_PAPER_FORMAT short paper camera readyAccepted author manuscript, 1.2 MB

Cite this

Khan, A., Pemberton, R., Momen, A., & Bristow, D. (2019). De-noising signals using wavelet transform in internet of underwater things. In Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2 (pp. 1192-1198). (Advances in Intelligent Systems and Computing; Vol. 1038). Springer Verlag. https://doi.org/10.1007/978-3-030-29513-4_85

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title = "De-noising signals using wavelet transform in internet of underwater things",

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.",

keywords = "Arduino Uno, IoUT, Sensors, Wavelet Transform",

author = "Asiya Khan and Richard Pemberton and Abdul Momen and Daniel Bristow",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; Intelligent Systems Conference, IntelliSys 2019 ; Conference date: 05-09-2019 Through 06-09-2019",

year = "2019",

month = aug,

day = "24",

doi = "10.1007/978-3-030-29513-4\_85",

language = "English",

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address = "Germany",

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Khan, A, Pemberton, R, Momen, A & Bristow, D 2019, De-noising signals using wavelet transform in internet of underwater things. in Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2. Advances in Intelligent Systems and Computing, vol. 1038, Springer Verlag, pp. 1192-1198, Intelligent Systems Conference, IntelliSys 2019, London, United Kingdom, 5/09/19. https://doi.org/10.1007/978-3-030-29513-4_85

De-noising signals using wavelet transform in internet of underwater things. / Khan, Asiya; Pemberton, Richard; Momen, Abdul et al.
Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2. Springer Verlag, 2019. p. 1192-1198 (Advances in Intelligent Systems and Computing; Vol. 1038).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings published in a book › peer-review

TY - GEN

T1 - De-noising signals using wavelet transform in internet of underwater things

AU - Khan, Asiya

AU - Pemberton, Richard

AU - Momen, Abdul

AU - Bristow, Daniel

PY - 2019/8/24

Y1 - 2019/8/24

N2 - 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.

AB - 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.

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KW - IoUT

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UR - https://pearl.plymouth.ac.uk/secam-research/1402/

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BT - Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2

PB - Springer Verlag

T2 - Intelligent Systems Conference, IntelliSys 2019

Y2 - 5 September 2019 through 6 September 2019

ER -

De-noising signals using wavelet transform in internet of underwater things

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

Keywords

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