Smart Water Security with AI and Blockchain-Enhanced Digital Twins
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Intrusion DetectionSimilar Papers 제목 키워드 기반
FinBTech: Blockchain-Based Video and Voice Authentication System for Enhanced Security in Financial Transactions Utilizing FaceNet512 and Gaussian Mixture Models
In the digital age, it is crucial to make sure that financial transactions are as secure and reliable as possible. This abstract offers a ground-breaking method that combines smart contracts, blockchain technology, FaceN…
Face RecognitionPublic and private blockchain for decentralized digital building twins and building automation system
The communication protocols and data transfer mechanisms employed by IoT devices in smart buildings and corresponding digital twin systems predominantly rely on centralized architectures. Such centralized systems are vul…
Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain
The accelerated expansion of the Internet of Things (IoT) has raised critical challenges associated with privacy, security, and data integrity, specifically in infrastructures such as smart cities or smart manufacturing.…
Decision MakingDeep Reinforcement LearningPrivacy PreservingSmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework
Smart contracts are essential for managing digital assets in blockchain networks, highlighting the need for effective security measures. This paper introduces SmartLLMSentry, a novel framework that leverages large langua…
Vulnerability DetectionBlockchain Signatures to Ensure Information Integrity and Non-Repudiation in the Digital Era: A comprehensive study
Blockchain systems rely on decentralized ledgers and strong security guarantees. A key requirement is non-repudiation, which prevents denial of transaction authorship and supports integrity of recorded data. This work su…