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AI-IoT-Blockchain Integrated Computational Intelligence for Solving Complex Engineering Problems

Submission Deadline: 30 June 2023 (closed)

Guest Editors

Prof. Jiachen Yang, Tianjin University, China
Prof. Houbing Song, Embry-Riddle Aeronautical University, USA
Prof. Qinggang Meng, Loughborough University, UK


Benefiting from the development of Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain, precision manufacturing and intelligent engineering applications have brought substantial changes to human life. In the past decades, many AI-driven computational mathematics modeling methods have been applied to complex engineering problems. These methods can better understand the complex engineering characteristics, generate hypotheses and predictions, and guide engineers to carry out successful applications. The IoT gathers and transmits ubiquitous real-time data from wireless sensor networks, monitoring various statuses in complex industrial and engineering applications. However, using IoT and open wireless communication technologies exposes the systems to a wide range of cyber threats. Blockchain is a shared, immutable ledger for recording transactions, tracking assets, and building trust while also providing security, ease of use, transparency, and trust among the participants. Consequently, the convergence of AI, IoT, and Blockchain is of great significance for solving complex engineering problems.

This special issue seeks original work focused on efficient algorithms, data analysis, blockchain technology, hardware deployment, and applications towards the AI-IoT-Blockchain integrated computational intelligence for solving complex engineering problems. The original research and review articles are both welcomed.


• Multi-source IoT-based data fusion
• AI-driven computational modeling
• Digital twin systems
• Intelligent decision systems
• Cloud computing and edge computing
• Blockchain applications

Published Papers

  • Open Access


    PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

    Yiqun Zhu, Shuxian Sun, Chunyu Liu, Xinyi Tian, Jingyi He, Shuai Xiao
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3285-3297, 2023, DOI:10.32604/cmes.2023.026691
    (This article belongs to the Special Issue: AI-IoT-Blockchain Integrated Computational Intelligence for Solving Complex Engineering Problems)
    Abstract With the rapid development of artificial intelligence and computer technology, grid corporations have also begun to move towards comprehensive intelligence and informatization. However, data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data. The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’ needs and their habits, providing better services for users. Nevertheless, users’ electricity consumption data is sensitive and private. In order to achieve highly efficient analysis of massive private electricity consumption data without direct access, a blockchain-based federated learning method is proposed… More >

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