TY - GEN
T1 - Cloud-Integrated IoT Framework for Real-Time Monitoring in Water Treatment Plants
AU - Poveda-Sotomayor, Paula
AU - Roa, Henry N.
AU - Loza-Aguirre, Edison
AU - Guaña-Moya, Javier
AU - Salgado-Reyes, Nelson
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Water treatment facilities require efficient and continuous monitoring to ensure safe drinking water. Traditional Supervisory Control and Data Acquisition (SCADA) systems often face limitations in scalability, cost-effectiveness, and real-time data accessibility, prompting interest in alternative technologies such as the Internet of Things (IoT). This study proposes a cloud-integrated IoT framework explicitly designed for real-time monitoring of key water quality parameters, including pH, turbidity, and flow rate. The framework securely transmits data using MQTT protocol to AWS IoT Core, leveraging AWS Timestream and Grafana dashboards for advanced analytics, visualization, and proactive anomaly detection. The system's feasibility and performance were validated through simulation, guided by the Design Science Research (DSR) methodology. Results indicate significant potential benefits, including improved real-time responsiveness, reduced manual monitoring requirements and enhanced operational decision-making. This framework provides a scalable and secure solution that addresses existing SCADA limitations, laying the groundwork for future integration into smart water management practices.
AB - Water treatment facilities require efficient and continuous monitoring to ensure safe drinking water. Traditional Supervisory Control and Data Acquisition (SCADA) systems often face limitations in scalability, cost-effectiveness, and real-time data accessibility, prompting interest in alternative technologies such as the Internet of Things (IoT). This study proposes a cloud-integrated IoT framework explicitly designed for real-time monitoring of key water quality parameters, including pH, turbidity, and flow rate. The framework securely transmits data using MQTT protocol to AWS IoT Core, leveraging AWS Timestream and Grafana dashboards for advanced analytics, visualization, and proactive anomaly detection. The system's feasibility and performance were validated through simulation, guided by the Design Science Research (DSR) methodology. Results indicate significant potential benefits, including improved real-time responsiveness, reduced manual monitoring requirements and enhanced operational decision-making. This framework provides a scalable and secure solution that addresses existing SCADA limitations, laying the groundwork for future integration into smart water management practices.
KW - AWS IoT Core
KW - Cloud computing
KW - Internet of Things (IoT)
KW - MQTT
KW - Smart water management
KW - Water treatment monitoring
UR - https://www.scopus.com/pages/publications/105028352055
U2 - 10.1007/978-981-95-1353-6_7
DO - 10.1007/978-981-95-1353-6_7
M3 - Contribución a la conferencia
AN - SCOPUS:105028352055
SN - 9789819513529
T3 - Smart Innovation, Systems and Technologies
SP - 81
EP - 91
BT - ICT for Intelligent Systems - Proceedings of ICTIS 2025
A2 - Choudrie, Jyoti
A2 - Tuba, Eva
A2 - Perumal, Thinagaran
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2025
Y2 - 23 May 2025 through 24 May 2025
ER -