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Convergence of Blockchain and AIoT: Secure and Intelligent Systems

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Special Issue Information

Dear Colleagues,

This Special Issue explores the powerful synergy between blockchain and artificial intelligence of things (AIoT). It investigates how these technologies can be combined to create the next generation of intelligent systems that are not only smart but also secure, transparent, and trustworthy. We welcome innovative research on blockchain-secured federated AI models for edge AIoT, privacy-preserving analytics, and real-time applications in smart infrastructure, healthcare, and Industry 5.0, advancing the Computers journal's emphasis on next-generation computing paradigms.

Suggested Topics:

We invite original research and review articles on topics including, but not limited to, the following:

Architectures and Models: Decentralized AI, federated learning with blockchain, and AI-driven smart contracts;
Security and Privacy: AI for blockchain security (anomaly detection, fraud analysis) and blockchain for securing AI (data integrity, model provenance);
Data and Trust: Blockchain-based trustworthy data markets for AI training and AI analytics for on-chain data;
Applications: Secure intelligent supply chains, autonomous DeFi systems, AI-audited smart contracts, and privacy-preserving healthcare data management;
Edge AIoT: Edge AIoT architectures using smart contracts for anomaly detection and predictive maintenance;
AIoT Tokenization: Tokenization of AIoT sensor data streams for decentralized marketplaces and provenance tracking.

Dr. Prince Waqas Khan
Guest Editor

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Keywords

blockchain
artificial intelligence (AI)
decentralized AI
artificial intelligence of things (AIoT)
smart contracts
decentralized systems
autonomous agents
security
data privacy
transparency
internet of things (IoT)
supply chain
healthcare
decentralized finance (DeFi)
predictive analytics
blockchain-AIoT convergence

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Published Papers (1 paper)

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Research

41 pages, 1898 KB

Open AccessArticle

Securing Cross-Chain Multisignature Execution Through Deterministic Enforcement and Explainable Anomaly Awareness

by Usman Mohyud din Chaudhary, Humaira Arshad, Muhammad Ismail Mohmand, Erum Ashraf and Waheed Ali H. M. Ghanem

Computers 2026, 15(8), 536; https://doi.org/10.3390/computers15080536 - 18 Aug 2026

Viewed by 442

Abstract

Cross-chain bridges represent one of the most damaging attack surfaces in decentralized finance, with major exploits (e.g., Ronin, Wormhole, Nomad, Multichain) arising not from broken signature schemes but from failures in proof verification, replay protection, and signer-set management, gaps that conventional threshold-signature multisignature wallets do not address. This study presents an incident-aware multisignature architecture combining three on-chain predicates—block-height freshness windows, epoch-bound signer sets, and Merkle inclusion-proof verification—with a non-authoritative off-chain LightGBM classifier that generates SHAP-attributed risk explanations to support governance actions such as pausing, vetoing, or rotating signers, without directly blocking or approving execution. The framework was evaluated on a simulated benchmark of 78,600 Ethereum testnet transactions containing six injected anomaly classes (gas spikes, nonce jitter, malformed call data, stale intents, proof-delivery delays, and epoch-rotation replays). The LightGBM advisor achieved ROC-AUC 0.92 (95% CI [0.906, 0.926]) and F1 0.73 ([0.712, 0.749]), outperforming five baselines—logistic regression, Random Forest, XGBoost, isolation forest, and a rule-based detector—with the highest F1 (0.731) and PR-AUC (0.799), while the rule-based detector, which by construction covers only the anomaly classes addressed by the deterministic predicates, attained F1 0.282. Differences were statistically significant except for the LightGBM–XGBoost PR-AUC comparison. The deterministic layer itself is verified through 28 property-level contract tests covering all seven modeled attack objectives, with measured per-function gas costs (execute_Intent: 118,756 gas, of which 28,432 gas is Merkle-proof verification). Within this controlled setting, the results indicate that a machine learning advisor can extend anomaly-prioritization coverage beyond the scope of the deterministic predicates while leaving execution control fully deterministic. This work is presented as a controlled proof of concept: the reported metrics quantify recovery of scripted injection patterns, and validation against real-world exploit traces remains future work. Full article

(This article belongs to the Special Issue Convergence of Blockchain and AIoT: Secure and Intelligent Systems)

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