AI and IoT: Supported Sixth Generation Sensing for Water Quality Assessment to Empower Sustainable Ecosystems
Suparna Das, Kamil Reza Khondakar, Hirak Mazumdar, Ajeet Kaushik, Yogendra Kumar Mishra
Abstract
The integration of artificial intelligence (AI) and the Internet-of-Things (IoT) heralds the advent of sixth-generation sensing technologies, offering transformative potential for water quality assessment and the empowerment of sustainable ecosystems. This approach offers efficient monitoring through predictive analysis, risk assessment, and timely decision-making. However, this approach requires diverse expertise and requires connecting multiple dots. This paper explores the convergence of AI and IoT in developing advanced sensor networks capable of real-time monitoring and data analysis, providing comprehensive insights into water quality. AI algorithms can predict pollution events, optimize resource management, and enhance decision-making processes. IoT-enabled sensors provide extensive coverage and connectivity, facilitating continuous monitoring and immediate reporting of water conditions. This synergy ensures accurate detection of contaminants and supports proactive environmental management, aligning with global sustainability goals. Implementing AI and IoT in water quality assessment is crucial for maintaining healthy aquatic ecosystems, fostering biodiversity, and ensuring safe water resources for communities. The paper highlights the effectiveness and scalability of AI and IoT-supported sensing technologies, underscoring their critical role in a sustainable future.