A Smart Portable Dual-Mode IoT Framework for Real-Time Water Quality Intelligence Using Embedded Edge Analytics

Abstract

Safe and clean water remains a major global concern as rising industrial activities, rapid urban growth, and environmental pollution continue to contaminate water resources. These challenges threaten public health, ecosystems, and sustainable development, highlighting the urgent need for effective water quality monitoring, management strategies, and advanced treatment technologies to ensure water security.. This paper presents a smart, portable, and cost-effective Internet of Things (IoT)-based water quality monitoring system for real-time assessment of key parameters such as pH, turbidity, and Total Dissolved Solids (TDS). The proposed system is implemented using an ESP32 microcontroller integrated with multi-parameter sensors and an OLED display for real-time local visualization.

In addition, the system incorporates Wi-Fi-enabled cloud connectivity for remote monitoring, data logging, and temporal trend analysis, enabling ubiquitous access to water quality information. A key contribution of this work is the development of a dual-mode operational framework with embedded edge-level analytics, allowing the system to function independently in local mode while simultaneously supporting cloud-based monitoring. This hybrid architecture reduces dependency on continuous internet connectivity and enhances system robustness in resource-constrained and rural environments.

The proposed system is battery-powered, compact, and optimized for low-cost deployment, making it suitable for scalable environmental monitoring applications. Experimental evaluation demonstrates that the system achieves real-time response with an average latency of less than 2 seconds, along with stable sensor performance across different water samples after calibration. The results validate the effectiveness of the proposed framework for reliable and continuous water quality monitoring.

The system can be further extended to support predictive analytics and large-scale IoT-based smart water management systems, making it a viable solution for sustainable environmental monitoring and public health applications.