Smart IoT Network for Real-Time Urban Water Quality Monitoring

Thesis Abstract

Urban water utilities face increasing pressures from rapid urbanization, climate variability, and aging infrastructure, which compromise water quality monitoring and timely public health protection. This study addresses the gap in real-time, scalable monitoring by developing and evaluating a Smart Internet of Things (IoT) network that continuously measures physico-chemical and microbial water quality indicators in urban distribution systems to enable proactive management and rapid incident response. The aim is to design, deploy, and validate an integrated IoT-based sensing platform that provides real-time data on key quality parameters, supports predictive analytics for contamination events, and informs operational decisions for water utilities. Specific objectives are (i) to design an energy-efficient sensor node architecture capable of multi-parameter measurement (pH, turbidity, residual chlorine, dissolved oxygen, temperature, conductance) and microbial indicators (coliforms via rapid surrogate assays); (ii) to implement secure wireless communication and edge processing for data fusion and anomaly detection; (iii) to develop a cloud-based data analytics framework incorporating time-series forecasting (ARIMA and Prophet) and anomaly detection (Isolation Forest) to identify deviations from baseline water quality; (iv) to validate the system through a 12-month field deployment across 10 strategically selected network nodes within a metropolitan district, incorporating both high- and low-risk zones; (v) to evaluate system performance against traditional grab-sampling and laboratory testing in terms of accuracy, responsiveness, operational cost, and decision-support effectiveness. The methodology adopts a mixed-methods approach anchored in a pragmatic research design. The population comprises urban water distribution segments, utility operators, and residents, with a purposive sample of 10 deployment sites and 30 utility staff participants for qualitative insights. Data collection combines (a) quantitative measurements from calibrated IoT nodes and monthly laboratory analyses (n = 360 water samples) for validation, (b) operational logs, maintenance records, and incident reports, and (c) semi-structured interviews with utility engineers and managers to elucidate adoption barriers and decision-making dynamics. Instrument validity and reliability are established through calibration protocols, cross-validation with ISO 19458 water quality standards, and test–retest reliability checks (Cronbach’s alpha for qualitative instruments and technical accuracy assessments for sensors). Data analysis employs time-series analysis (ARIMA and Exponential Smoothing) for forecasting, machine learning-based anomaly detection (Isolation Forest and One-Class SVM), regression analysis to correlate sensor readings with laboratory results, and cost-benefit analysis to assess economic viability. A conceptual model is developed to illustrate the interplay between sensor fidelity, network reliability, data analytics, and decision-support outcomes, drawing on the Technological-Organizational-Environmental (TOE) framework and Diffusion of Innovations theory to interpret adoption dynamics. Expected findings indicate that the IoT network achieves a mean operational accuracy of 92–95% for key indicators relative to lab measurements, reduces response time to 15–20 minutes for detected anomalies, and lowers per-sample monitoring costs by 40% over the 12-month period. The research anticipates that edge analytics will mitigate latency and bandwidth demands, while cloud analytics will enhance early warning capabilities for contamination events, enabling targeted flushing and hydraulics adjustments. The study contributes to knowledge by integrating multi-parameter in-situ sensing with robust edge-to-cloud analytics in municipal water systems, providing a replicable deployment blueprint and an evidence-based evaluation framework for real-time water quality management. Conclusions emphasize the feasibility of scalable urban IoT monitoring, improved resilience of water supply, and actionable insights for policy and practice. Recommendations include standardizing data-sharing protocols across utilities, exploring renewable-powered sensor nodes for remote communities, expanding microbial proxy assays with rapid qPCR validation, and continuing longitudinal assessments to monitor long-term performance and health risk outcomes.