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
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
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.
KW - Arduino Uno
KW - IoUT
KW - Sensors
KW - Wavelet Transform
UR - https://www.scopus.com/pages/publications/85072820236
UR - https://pearl.plymouth.ac.uk/secam-research/1402/
U2 - 10.1007/978-3-030-29513-4_85
DO - 10.1007/978-3-030-29513-4_85
M3 - Conference proceedings published in a book
AN - SCOPUS:85072820236
SN - 9783030295127
T3 - Advances in Intelligent Systems and Computing
SP - 1192
EP - 1198
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 -
