Smart Water Security with AI and Blockchain-Enhanced Digital Twins
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908342834692096 |
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| author | Homaei, Mohammadhossein Morales, Victor Gonzalez Gutierrez, Oscar Mogollon Gomez, Ruben Molano Caro, Andres |
| author_facet | Homaei, Mohammadhossein Morales, Victor Gonzalez Gutierrez, Oscar Mogollon Gomez, Ruben Molano Caro, Andres |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smart Water Security with AI and Blockchain-Enhanced Digital Twins Homaei, Mohammadhossein Morales, Victor Gonzalez Gutierrez, Oscar Mogollon Gomez, Ruben Molano Caro, Andres Cryptography and Security Artificial Intelligence Machine Learning 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. |
| title | Smart Water Security with AI and Blockchain-Enhanced Digital Twins |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.20275 |