A Five-Layer IoT-Big Data Architecture for Smart Wastewater Monitoring and Treatment: Design, Integration of Machine Learning, and Deployment Feasibility for Vietnam

Abstract

ABSTRACT: Industrial and urban wastewater pollution constitutes a critical environmental challenge in Vietnam, where only approximately 12.5% of municipal wastewater currently undergoes adequate treatment before discharge. This paper proposes the Smart Wastewater Monitoring and Treatment (SWMT) framework, a five-layer IoT–Big Data architecture designed to enable real-time, data-driven management of wastewater quality and treatment processes. The framework integrates: (i) a multi-parameter wireless sensor network (WSN) measuring pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), ammonium (NH₄⁺), temperature, and flow rate; (ii) LoRaWAN and MQTT-based communication protocols; (iii) edge computing for on-site preprocessing and rapid rule-based alerting; (iv) a cloud-hosted Lambda Architecture employing Apache Kafka and Spark Streaming for high-throughput real-time analytics; and (v) machine learning models - Long Short-Term Memory (LSTM) networks, Isolation Forest, and Random Forest - for predictive water quality forecasting, unsupervised anomaly detection, and pollution severity classification. Simulation results demonstrate an anomaly detection latency of 3.2 minutes (versus ~8 hours with conventional manual sampling, representing a 99.3% reduction), a true positive rate of 94.2% (FPR = 2.8%), an LSTM forecast RMSE of 0.12 mg/L for COD, and an estimated 18.5% improvement in treatment efficiency through ML - optimised chemical dosing. The paper further analyses regulatory compliance with QCVN 40:2011/BTNMT and QCVN 14:2008/BTNMT, and presents deployment feasibility assessments for three target contexts: industrial zones (IZs), urban wastewater networks, and rural craft villages. The SWMT framework contributes a comprehensive, scalable, and cost-effective blueprint for intelligent wastewater governance in developing-country contexts. Keywords: Internet of Things (IoT); Big Data; wastewater monitoring; LSTM; anomaly detection; LoRaWAN; smart water management; Vietnam; QCVN; machine learning.