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Technology, 21(3), 899–905. https://doi.org/10.1109/TCST.2012.2190070.
72. Ferri, G., Manzi, A., Fornai, F., Ciuchi, F., & Laschi, C. (2015). The HydroNet ASV, a
small-sized autonomous catamaran for real-time monitoring of water quality: From design to
missions at sea. IEEE Journal of Oceanic Engineering, 40(3), 710–726. https://doi.org/10.
1109/JOE.2014.2359361.
73. Liu, J., Wu, Z., & Yu, J. (2016). Design and implementation of a robotic dolphin for water
quality monitoring. In: 2016 IEEE International Conference on Robotics and Biomimetics
(ROBIO) 2016 (pp. 835–840). IEEE.
74. Wu, Z., Liu, J., Yu, J., & Fang, H. (2017). Development of a novel robotic dolphin and its
application to water quality monitoring. IEEE/ASME Transactions on Mechatronics, 22(5),
2130–2140. https://doi.org/10.1109/TMECH.2017.2722009.
75. Ravalli, A., Rossi, C., & Marrazza, G. (2017). Bio-inspired fish robot based on chemical
sensors. Sensors and Actuators B: Chemical, 239, 325–329. https://doi.org/10.1016/j.snb.
2016.08.030.
76. Zhang, F., Ennasr, O., Litchman, E., & Tan, X. (2016). Autonomous sampling of water
columns using gliding robotic fish: Algorithms and harmful-algae-sampling experiments.
IEEE Systems Journal, 10(3), 1271–1281. https://doi.org/10.1109/JSYST.2015.2458173.
77. Felemban, E., Shaikh, F. K., Qureshi, U. M., Sheikh, A. A., & Qaisar, S. B. (2015).
Underwater sensor network applications: A comprehensive survey. International Journal of
Distributed Sensor Networks, 11(11), 896832. https://doi.org/10.1155/2015/896832.
78. Charef, A., Ghauch, A., Baussand, P., & Martin-Bouyer, M. (2000). Water quality
monitoring using a smart sensing system. Measurement, 28(3), 219–224. https://doi.org/10.
1016/S0263-2241(00)00015-4.
79. Yifan, K., & Peng, J. (2008). Development of data video base station in water environment
monitoring oriented wireless sensor networks. In: 2008 International Conference on
Embedded Software and Systems Symposia 2008 (pp. 281–286). IEEE.
80. Jiang, P., Xia, H., He, Z., & Wang, Z. (2009). Design of a water environment monitoring
system based on wireless sensor networks. Sensors, 9(8), 6411–6434. https://doi.org/10.
3390/s90806411.
81. Parra, L., Rocher, J., Escrivá, J., & Lloret, J. (2018). Design and development of low cost
smart turbidity sensor for water quality monitoring in fish farms. Aquacultural Engineering,
81, 10–18. https://doi.org/10.1016/j.aquaeng.2018.01.004.
82. Dunkels, A., Osterlind, F., Tsiftes, N., & He, Z. (2007). Software-based on-line energy
estimation for sensor nodes. In: Proceedings of the 4th Workshop on Embedded Networked
Sensors 2007 (pp. 28–32). ACM.
83. Wu, R., Salim, W. W., Malhotra, S., Brovont, A., Park, J., Pekarek, S., et al. (2013).
Self-powered mobile sensor for in-pipe potable water quality monitoring. In: Proceedings of
the 17th International Conference on Miniaturized Systems for Chemistry and Life Sciences
(pp. 14–16).
84. Mao, S., Chang, J., Zhou, G., & Chen, J. (2015). Nanomaterial-enabled rapid detection of
water contaminants. Small (Weinheim an der Bergstrasse, Germany), 11(40), 5336–5359.
https://doi.org/10.1002/smll.201500831.
85. Willner, M. R., & Vikesland, P. J. (2018). Nanomaterial enabled sensors for environmental
contaminants. Journal of Nanobiotechnology, 16(1), 95.
86. Grieshaber, D., MacKenzie, R., Voeroes, J., & Reimhult, E. (2008). Electrochemical
biosensors-sensor principles and architectures. Sensors, 8(3), 1400–1458. https://doi.org/10.
3390/s80314000.
87. Link, S., & El-Sayed, M. A. (1999). Spectral properties and relaxation dynamics of surface
plasmon electronic oscillations in gold and silver nanodots and nanorods. ACS
Publications.
Emerging Techniques and Materials for Water Pollutants Detection
295