Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaWAN-based data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-Term Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and scalable for up to 1,000 smart meters. This work demonstrates a practical and secure architecture for decentralized water infrastructure in under-connected rural environments.

本研究解决了农村地区水分配系统在实时监测、网络攻击脆弱性和数据处理不可靠等方面的挑战。通过结合LoRaWAN数据采集、机器学习的入侵检测系统以及区块链驱动的数字双胞胎平台，研究提出了一种安全透明的水管理框架。实验结果表明，该系统在保证2秒内延迟的同时，能够实现超过80次交易每秒，为改善偏远地区的水基础设施提供了实用和安全的架构。

智能水安全：人工智能与区块链增强的数字双胞胎