Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security

Submission Deadline: 31 July 2025 View: 716 Submit to Special Issue

Guest Editors

Prof. Jong Hyuk (James) Park (Leading Guest Editor), Seoul National University of Science and Technology, South Korea
Prof. Yi Pan, Georgia State University, USA
Prof. Ji Su Park, Jeonju University, South Korea

Summary

Artificial Intelligence of Things (AIoT) is considered a collaborative application of artificial intelligence (AI) and the Internet of Things (IoT). The AIoT system realizes real-time information acquisition through IoT sensors and performs intelligent data analysis tasks anywhere along the terminal-edge-cloud continuum, forming a smart and supportive ecosystem. However, AIoT systems face threats related to IoT data trust, system robustness, security, and privacy, making them susceptible to massive cyberattacks.

AI and blockchain ensure a secure environment for AIoT data communication, computation, and storage to solve trust, alertness, security, and privacy issues in AIoT. AI extends existing blockchain technology to bring a high level of economics, adaptability, and autonomy to blockchain systems. On top of existing blockchain technology, data mining, pattern recognition, machine learning, and deep learning can provide additional capabilities to blockchain systems, providing significant benefits to AIoT systems. Recently, it has been applied to cyber security, smart cities, smart grids, wireless sensor networks, mobile communications, crowdsourcing/crowd sensing, and cyber physical-social systems. However, AIoT's AI and blockchain technology still have several research problems and challenges.

Original papers are requested on topics of interest including, but not limited to:

1. Blockchain Theory and Algorithms for Robustness, Privacy, Trust and Security in AIoT

2. Machine Learning Theory and Algorithm for Robustness, Privacy, Trust and Security in AIoT

3. AI-based data analytics for AIoT

4. Machine/deep learning for AIoT

5. Secure AIoT system design based on ML and blockchain

6. Decentralized and collaborative learning for AIoT

7. Decentralized computing for AIoT(Robustness, Privacy, Trust and Security)

8. Big data analytics based on blockchain in AIoT systems

9. Performance optimization of blockchains in AIoT

Published Papers

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Open Access

ARTICLE

Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder
Saad Alahmari, Abdulwhab Alkharashi
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062549
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >

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Open Access

ARTICLE

LMSA: A Lightweight Multi-Key Secure Aggregation Framework for Privacy-Preserving Healthcare AIoT
Hyunwoo Park, Jaedong Lee
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061178
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract Integrating Artificial Intelligence of Things (AIoT) in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges, including privacy preservation, computational efficiency, and regulatory compliance. Traditional approaches, such as differential privacy, homomorphic encryption, and secure multi-party computation, often fail to balance performance and privacy, rendering them unsuitable for resource-constrained healthcare AIoT environments. This paper introduces LMSA (Lightweight Multi-Key Secure Aggregation), a novel framework designed to address these challenges and enable efficient, secure federated learning across distributed healthcare institutions. LMSA incorporates three key innovations: (1) a lightweight multi-key management system leveraging Diffie-Hellman… More >

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Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security

Guest Editors

Summary

Published Papers

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13

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201

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