Collaborative Intelligent Automation System for Smart Industry

Dear Colleagues,

In an AI-driven IoT system, IoT is used for periodic control, and AI is used for data analysis. This combination makes the system more intelligent over time. An AI-driven IoT system collects huge amounts of data generated by ubiquitous IoT devices and provides automated, inexpensive support services based on complex analysis algorithms. AI can provide an enhanced user interface for real-time data management and pattern finding. AI can further be utilised to provide security in the IoT ecosystem by detecting network intrusion within the system, detecting intermediary security attacks inside the IoT, and conducting web-based security assessments using intelligent learning processes on IoT-enabled devices. These learning processes can be more advanced if we use diverse and big data by merging data from multiple systems. However, it is very tricky to merge these data and find the pattern, especially in a run-time environment. There is now a demand for how we can use real-time data for run-time machine learning in an AI-driven collaborative ecosystem. However, there are several challenges to continuing such a collaborative AI-driven IoT platform, such as security and privacy, synchronisation of ML processes, autonomous agreement among data sources, etc. Scalability and interoperability are two additional difficult tasks that blockchain technology is launching, even though many recent contributions suggest that federated machine learning and blockchain combinations can resolve many difficult issues.

This Special Issue's major goal is to gather scholars together to present their creative research findings relating to the difficulties, uses, architecture development, technologies, and prospects of AI-driven automation systems, while also taking scalability, security, and privacy into consideration.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Intelligent IoT systems framework;
• Privacy-preserving IoT system;
• Privacy-preserving real-time machine learning system;
• Collaborative, secure ML platform;
• Energy-efficient Internet of Medical Things (IoMT) framework for Smart Healthcare;
• Blockchain-controlled intelligent IoT systems;
• Autonomous feature extractions from medical images;
• Deep learning and transfer learning for smart systems;
• Federated-learning-based secure collaborative learning platform.

I look forward to receiving your contributions.

Dr. Sujit Biswas
Guest Editor

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• federated machine learning
• advance artificial intelligence
• blockchain
• Internet of Things