Smart Water Security with AI and Blockchain-Enhanced Digital Twins

2025-04-28

AI Summary

Rural water supply systems suffer from inadequate real-time monitoring, heightened vulnerability to cyberattacks, and low data credibility. To address these challenges, this paper proposes a blockchain-empowered digital twin (BC-DT) framework for intelligent water security. The framework integrates LoRaWAN-based edge sensing, AI-driven intrusion detection—leveraging an LSTM autoencoder combined with Isolation Forest—and on-chain evidence anchoring via a private Proof-of-Authority (PoA) Ethereum blockchain. It enables dynamic anomaly filtering, tamper-proof on-chain data provenance, and closed-loop physical-system feedback. The system supports leak detection, water consumption forecasting, and predictive maintenance. Experimental evaluation demonstrates throughput exceeding 80 transactions per second (TPS) and end-to-end latency under 2 seconds, with scalability to over one thousand smart meter nodes. Its key innovation lies in the first lightweight BC-DT architecture that simultaneously ensures security, verifiability, deployment affordability, and operational robustness.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• Addressing lack of real-time monitoring in rural water systems
• Preventing cyberattacks and ensuring data reliability in water management
• Enhancing water security with AI and blockchain digital twins

Innovation

• LoRaWAN-based data acquisition for rural water monitoring
• LSTM Autoencoder and Isolation Forest for anomaly detection
• Blockchain-enabled Digital Twin for secure water management