Abstract
Industrial water consumption monitoring is a crucial task. The quality of intake and effluent of the water from industry mainly influences the production and environmental pollution. The absence of equilibrium in the running water makes pH measurement more complicated and unstable in nature. The water qualities like pH, dissolved oxygen, temperature, and turbidity are measured. The apparatus is self-cleaned using the acetone and freshwater jet stream. The industrial standard processors are used to monitor the sensors and quantity of the water flow in the pipes. The self-cleaning mechanism prevents scaling formation and ensures continues monitoring system. The scaling effects in pipes are observed due to hard water flow. A self-cleaning mechanism is also equipped to clean and ensure equipment reliability. The methodology is implemented in lab setup and it is tested. The processors are programmed with sensor saturation time and the equipment requires auto-cleaning for every 8 h. The entire system is designed with industrial NXP processor. A self-healing IoT enabled water buoy is designed in the section which provides maintenance-free monitoring of water quality in aquaculture ponds.
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References
S. Rani, R. Maheswar, G.R. Kanagachidambaresan, P. Jayarajan, Integration of WSN and IoT for smart cities (Springer, Cham, 2020)
G.R. Kanagachidambaresan, R. Maheswar, V. Manikandan, K. Ramakrishnan, Internet of Things in smart technologies for sustainable urban development (Springer, Cham, 2020)
Advanced deep learning for engineers and scientists: a practical. S.l.: Springer Nature (2021)
A. Saad, A.E.H. Benyamina, A. Gamatie, Water management in agriculture: a survey on current challenges and technological solutions. IEEE Access 8, 38082–38097 (2020). https://doi.org/10.1109/ACCESS.2020.2974977
D.V. Cruz, M.R.G. de Oliveira, M.C. Filho, D.V. da Cruz, Monitoring pH with quality control based on Geostatistics Methodology. IEEE Lat. Am. Trans. 14(12), 4787–4791 (2016). https://doi.org/10.1109/TLA.2016.7817012
S.O. Olatinwo, T.-H. Joubert, Energy efficient solutions in wireless sensor systems for water quality monitoring: a review. IEEE Sens. J. 19(5), 1596–1625 (2019). https://doi.org/10.1109/JSEN.2018.2882424
S.O. Olatinwo, T.-H. Joubert, Enabling communication networks for water quality monitoring applications: a survey. IEEE Access 7, 100332–100362 (2019). https://doi.org/10.1109/ACCESS.2019.2904945
N.A. Cloete, R. Malekian, L. Nair, Design of smart sensors for real-time water quality monitoring. IEEE Access 4, 3975–3990 (2016). https://doi.org/10.1109/ACCESS.2016.2592958
D. Madeo, A. Pozzebon, C. Mocenni, D. Bertoni, A low-cost unmanned surface vehicle for pervasive water quality monitoring. IEEE Trans. Instrum. Meas. 69(4), 1433–1444 (2020). https://doi.org/10.1109/TIM.2019.2963515
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Kanagachidambaresan, G.R. (2021). Industry 4.0 for Smart Factories. In: Role of Single Board Computers (SBCs) in rapid IoT Prototyping. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-72957-8_11
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DOI: https://doi.org/10.1007/978-3-030-72957-8_11
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