Integration of IoT and Edge Computing for Smart Water Resource Management in Urban Ecosystems

Abstract

The Urban water management has become increasingly complex due to rapid urbanization, population growth, and climate-induced variability in water availability. Conventional water management systems, characterized by centralized infrastructure and delayed response mechanisms, are inadequate in addressing real-time challenges such as leakage detection, demand forecasting, and quality monitoring. This study presents an integrated framework combining Internet of Things (IoT) technologies with edge computing to enable intelligent, real-time water resource management in urban ecosystems. The proposed system utilizes distributed sensor networks to collect data on water flow, pressure, and quality parameters, while edge computing nodes process data locally to reduce latency and bandwidth requirements. Machine learning algorithms are deployed at the edge to perform predictive analytics, including demand forecasting and anomaly detection. The results demonstrate significant improvements in system responsiveness, operational efficiency, and resource optimization compared to traditional cloud-centric approaches. The study further explores scalability, security, and interoperability challenges, highlighting the potential of IoT edge integration to transform urban water management into a sustainable, adaptive, and resilient system.