TY - GEN
T1 - Aqua Optimize Transformation of Water Management With Cloud-Powered Efficiency
AU - Ignisha Rajathi, G.
AU - Priya, L. R.
AU - Vedhapriyavadhana, R.
AU - Deepthyka, K.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Effective water quality monitoring is crucial to ensuring the sustainability and safety of water resources, particularly in fields such as municipal water management, aquaculture, and agriculture. Traditional methods often result in water wastage, environmental degradation, and public health risks due to delayed responses and manual inefficiencies. With the rising focus on sustainable resource management and the impacts of climate change, there is a pressing need for innovative solutions that enable real-time water quality assessment. This research presents an Internet of Things (IoT)-based water quality monitoring system that connects multiple sensors to cloud-based platforms for continuous data analysis. The system monitors key parameters - temperature, flow rate, pH, and water depth - and transmits data using the MQTT protocol to visualization tools like Grafana and Power BI. These tools provide stakeholders with interactive dashboards, displaying real-time water quality trends, anomalies, and alerts for immediate action. The system recommends scalability to adapt a range of industrial applications, providing a cost-effective solution for both small and large-scale water management needs, based on its modularity. Compared to traditional methods, the proposed system improves water quality assessment accuracy, reducing human error and manual inefficiencies by over 30%. Cloud-based data storage and visualization reduce processing times by 40%, while the automated alert system accelerates response times by 25%, allowing prompt interventions when water quality falls below critical thresholds. The system's adaptable, modular architecture allows for seamless integration with existing water management frameworks in urban water distribution, wastewater treatment, and agriculture. This research demonstrates the system's potential to enhance water resource management, lower operational costs, and promote environmental sustainability across diverse sectors.
AB - Effective water quality monitoring is crucial to ensuring the sustainability and safety of water resources, particularly in fields such as municipal water management, aquaculture, and agriculture. Traditional methods often result in water wastage, environmental degradation, and public health risks due to delayed responses and manual inefficiencies. With the rising focus on sustainable resource management and the impacts of climate change, there is a pressing need for innovative solutions that enable real-time water quality assessment. This research presents an Internet of Things (IoT)-based water quality monitoring system that connects multiple sensors to cloud-based platforms for continuous data analysis. The system monitors key parameters - temperature, flow rate, pH, and water depth - and transmits data using the MQTT protocol to visualization tools like Grafana and Power BI. These tools provide stakeholders with interactive dashboards, displaying real-time water quality trends, anomalies, and alerts for immediate action. The system recommends scalability to adapt a range of industrial applications, providing a cost-effective solution for both small and large-scale water management needs, based on its modularity. Compared to traditional methods, the proposed system improves water quality assessment accuracy, reducing human error and manual inefficiencies by over 30%. Cloud-based data storage and visualization reduce processing times by 40%, while the automated alert system accelerates response times by 25%, allowing prompt interventions when water quality falls below critical thresholds. The system's adaptable, modular architecture allows for seamless integration with existing water management frameworks in urban water distribution, wastewater treatment, and agriculture. This research demonstrates the system's potential to enhance water resource management, lower operational costs, and promote environmental sustainability across diverse sectors.
UR - https://www.scopus.com/pages/publications/85219632228
UR - https://www.scopus.com/pages/publications/85219632228#tab=citedBy
U2 - 10.1109/MoSICom63082.2024.10882034
DO - 10.1109/MoSICom63082.2024.10882034
M3 - Conference contribution
AN - SCOPUS:85219632228
T3 - IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024 - Proceedings
SP - 501
EP - 506
BT - IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024
Y2 - 9 December 2024 through 11 December 2024
ER -