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Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring

A Correction to this article was published on 24 March 2022

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Water is the most important natural element present on earth for humans, yet the availability of pure water is becoming scarce and decreasing. An increase in population and rise in temperatures are two major factors contributing to the water crisis worldwide. Desalinated, brackish water from the sea, lake, estuary, or underground aquifers is treated to maximize freshwater availability for human consumption. However, mismanagement of water storage, distribution, or quality leads to serious threats to human health and ecosystems. Sensors, embedded and smart devices in water plants require proactive monitoring for optimal performance. Traditional quality and device management require huge investments in time, manual efforts, labour, and resources. This research presents an IoT-based real-time framework to perform water quality management, monitor, and alert for taking actions based on contamination and toxic parameter levels, device and application performance as the first part of the proposed work. Machine learning models analyze water quality trends and device monitoring and management architecture. The results display that the proposed method manages water monitoring and accessing water parameters efficiently than other works.

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Change history


  • A concise guide for IOT based Water Quality Monitoring. An End-To-End IoT Solutions Provider. (2021). Retrieved November 3, 2021, from

  • Alshehri M, Bhardwaj A, Kumar M, Mishra S, Gyani J (2021) Cloud and IoT based smart architecture for desalination water treatment. Elsevier Environmental Research 195

  • Amazing facts about water! Retrieved February 19, 2021, from

  • Application performance monitoring and management. Retrieved January 19, 2021, from

  • Baidukova O, Wang Q, Chaiwaree S, Freyer D, Prapan A, Georgieva R, Zhao L, Bäumler H (2018) Antioxidative protection of haemoglobin microparticles (HbMPs) by PolyDopamine. Taylor & Francis Artificial Cells, Nanomedicine, and Biotechnology 46:693–701

    Article  Google Scholar 

  • Beach water quality - automated sensors - dataset by CityofChicago. Retrieved February 25, 2021, from

  • Benedict S (2020) Serverless blockchain-enabled architecture for IoT societal applications. IEEE Transactions on Computational Social Systems 7(5):1146–1158

    Article  Google Scholar 

  • Borelli F, Biondi G (2020) Kamienski C (2020) BIoTA: A Buildout IoT Application Language. IEEE Access 8:126443–126459

    Article  Google Scholar 

  • Clean drinking water. (2021). Retrieved April 21, 2021, from

  • Curry E, Hasan S, Kouroupetroglou C, Fabritius W, Hassan U, Derguech W (2018) Internet of things enhanced user experience for smart water and energy management. IEEE Internet Comput 22(1):18–28

    Article  Google Scholar 

  • Deep B, Mathur I, Joshi N (2020) Coalescing IoT and Wi-Fi technologies for an optimized approach in urban route planning. Environ Sci Pollut Res 27:34434–34441

    CAS  Article  Google Scholar 

  • Demissie H, Lu S, Jiao R, Liu L, Xiang Y, Ritigala T, Ajibade F, Mihiranga F, An G, Wang D (2021) Advances in micro interfacial phenomena of adsorptive micellar flocculation: principles and application for water treatment. Water Research, 202

  • Dhanwani R, Prajapati A, Dimri A (2021) Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow. Environ Sci Pollut Res 28:35406–35428.

    CAS  Article  Google Scholar 

  • Dong W, Yang Q (2020) Data-driven solution for optimal pumping units scheduling of smart water conservancy. IEEE Internet Things J 7(3):1919–1926

    Article  Google Scholar 

  • Facts and statistics about water and its effects. Retrieved January 2, 2021, from

  • Li M, He P, Zhao L (2017) Dynamic load balancing applying water-filling approach in smart grid systems. IEEE Internet Things J 4(1):247–257

    Article  Google Scholar 

  • López et al (2020) Implementation of smart buoys and satellite-based systems for the remote monitoring of harmful algae bloom in inland waters. IEEE Sens J 21(5):6990–6997

    Article  Google Scholar 

  • Luccio D et al (2020) Coastal marine data crowdsourcing using the internet of floating things: improving the results of a water quality model. IEEE Access 8:101209–101223

    Article  Google Scholar 

  • Menasalvas E, Swoboda N, Moreno A, Metzger A, Rothweiler A, Pavlopoulou N, Curry E (2021) Recognition of Formal and Non-formal Training in Data Science. The Elements of Big Data Value, pp 311

  • Minoli D, Sohraby K, Occhiogrosso B (2017) IoT considerations, requirements, and architectures for smart buildings — energy optimization and next-generation building management systems. IEEE Internet Things J 4(1):269–283

    Article  Google Scholar 

  • Nasser A, Rashad M, Hussein S (2020) A two-layer water demand prediction system in urban areas based on micro-services and LSTM neural networks. IEEE Access 8:147647–147661

    Article  Google Scholar 

  • Olatinwo S, Joubert T (2019) Enabling communication networks for water quality monitoring applications: a survey. IEEE Access 7:100332–100362

    Article  Google Scholar 

  • Priya S, Shenbagalakshmi G, Revathi T (2018) IoT Based Automation of Real Time In-Pipe Contamination Detection System in Drinking Water. International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 1014–1018

  • Roy S, Misra S, Raghuwanshi N, Das S (2021) AgriSens: IoT-based dynamic irrigation scheduling system for water management of irrigated crops. IEEE Internet Things J 8(6):5023–5030

    Article  Google Scholar 

  • Serra et al. (2019) 0.9-V Analog-to-Digital Acquisition Channel for an IoT Water Management Sensor Node. IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 10, pp. 1678–1682

  • Tracking down three billion liters of lost water. Retrieved January 11, 2021, from

  • Wang D (2019) Xiang H (2019) Composite control of post-chlorine dosage during drinking water treatment. IEEE Access 7:27893–27898

    Article  Google Scholar 

  • UNICEF Water, Sanitation & Hygiene (2020), Retrieved February 15, 2021.

  • WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation. "Progress on Sanitation and Drinking Water 2010." Available at

  • Woźniak M, Zielonka A, Sikora A, Piran M, Alamri A (2021) 6G-enabled IoT home environment control using fuzzy rules. IEEE Internet Things J 8(7):5442–5452

    Article  Google Scholar 

  • Yadav S, Luthra S, Garg D (2021) Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks. Environ Sci Pollut Res 28:16633–16654.

    Article  Google Scholar 

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The authors express their gratitude to the editorial board and reviewer for the efforts for suggestion and reviewing this paper. The authors also appreciate the editor for his cooperation during the review process.

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Manuscript writing and data analysis Akashdeep Bhardwaj, Akarsh Aggarwal, Supervision and conceptualization Muhammad Owais Khan, Vishal Dagar, Rafael Alvarado; Proofreading and reviewing Manoj Kumar, Muhammad Irfan, Ram Proshad. All authors have read and approved this manuscript.

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Correspondence to Muhammad Owais Khan.

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The authors declare no competing interests.

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Responsible Editor: Xianliang Yi

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Bhardwaj, A., Dagar, V., Khan, M.O. et al. Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring. Environ Sci Pollut Res 29, 46018–46036 (2022).

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  • Water quality monitoring
  • Real-time
  • IoT
  • Sensor
  • AI
  • Wireless
  • Embedded
  • Microcontroller