IoT for Wastewater Monitoring in a Smart Environment

Ramesh, Lekhavani and Krishnamoorthy, Suguna and Subbarayalu, Ramalakshmi and Mohanavel, Vinayagam and Vijayakumar, Vishnu Raja and Kannan, Sathish and Thangarasu, Pavithiradevi and Azrour, Mourade (2026) IoT for Wastewater Monitoring in a Smart Environment. In: Internet of Things and Green Technologies for Smart Environment. CRC Press, Boca Raton, pp. 34-50. ISBN 9781003675426

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Abstract

The use of Internet of Things (IoT) techniques for wastewater management is expanding the possibilities for environmental monitoring by providing timely, data-driven information about water quality and operational performance. This chapter discusses the implementation of IoT-based solutions for continuous monitoring of wastewater characteristics, including pH, turbidity, chemical oxygen demand (COD), biological oxygen demand (BOD), and heavy metal content. This is most efficiently achieved by using sensors, wiring them to remote data transmission, and then integrating them using edge-cloud computing frameworks. Wastewater management using IoT is distinctive in its capacity to improve responsiveness, precision, and efficiency. A review of existing IoT architecture is provided by focusing on scalability, energy performance, failover/fault tolerance, and interoperability with traditional wastewater management systems. Particular focus will be placed on LPWAN, AI-enabled analytics, and cybersecurity challenges associated with decentralized water quality monitoring. As case studies reveal, Smart Sensors are deployed into various Municipal sewerage systems and industrial effluent treatment plants, providing alerts in real time to mitigate environmental risks and compliance issues. Therefore, the chapter aims to define an intelligent wastewater infrastructure framework that is ready for the future, embracing the use of IoTs, AI, and green technology, within the context of the sustainable development-smart city ecosystem.

Item Type: Book Section
Subjects: Computer Science Engineering > Machine Learning
Depositing User: Research 10 10
Date Deposited: 10 Mar 2026 08:30
Last Modified: 10 Mar 2026 08:30
URI: https://ir.vistas.ac.in/id/eprint/13112

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