Industrial IoT and Computing Based on Mathematical Methods
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 25
Special Issue Editors
Interests: optimization and evolutionary algorithms; data analytics; early warning systems; IoT and wireless sensor network
Special Issue Information
Dear Colleagues,
The rapid proliferation of Industrial Internet of Things (IIoT) technologies is revolutionizing manufacturing, supply chain management, and industrial automation. This transformation generates unprecedented volumes of heterogeneous data that require sophisticated mathematical frameworks for effective analysis, optimization, and decision-making. This Special Issue of Mathematics invites cutting-edge research that explores the intersection of mathematical methods and industrial IoT applications.
We welcome contributions addressing mathematical foundations for IoT data analytics, including but not limited to machine learning algorithms for sensor networks, optimization methods for resource allocation, graph theory applications in network topology, statistical models for predictive maintenance, signal processing techniques for real-time monitoring, and mathematical frameworks for edge computing architectures.
We particularly encourage submissions that demonstrate practical implementations, provide theoretical insights into computational complexity, or propose novel mathematical approaches to address challenges in industrial IoT environments. This Special Issue aims to bridge the gap between theoretical mathematics and practical industrial computing solutions, fostering innovation in the era of Industry 4.0.
Dr. Paskorn Champrasert
Prof. Dr. Chonho Lee
Guest Editors
Manuscript Submission Information
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Keywords
- industrial internet of things (IIoT)
- mathematical optimization
- machine learning and deep learning algorithms
- edge computing
- predictive analytics
- network graph theory signal processing
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