Introduction

The rapid advancement of Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technologies is revolutionizing monitoring across sectors, including manufacturing, infrastructure, and environmental sciences. With the rise of Industry 4.0, real-time, data-driven smart monitoring solutions are increasingly critical for enhancing safety, optimizing resources, and enabling intelligent decision-making, especially in essential infrastructure like smart transportation, healthcare systems, and manufacturing facilities. These innovations are vital in addressing pressing global challenges such as climate change and environmental sustainability. Advanced AI methods— including Swarm Intelligence, NeuroEvolutionary Algorithms, and the integration of AI with IoT (AIoT)— are facilitating the development of adaptive models capable of handling complex monitoring and decision-making with high accuracy and reliability.

 

Special Issue Aim and Scope

This special issue invites research on smart monitoring and decision-making systems that integrate advanced AI algorithms with real-time IoT data to address practical challenges across a wide range of applications—from in-situ manufacturing processes to large-scale environmental systems. We invite contributions that explore innovative solutions leveraging AI, IoT, and Digital Twin technologies to enhance system resilience, efficiency, and sustainability. Featured topics include, but are not limited to:

·         Model Development and Optimization: Research on Swarm Intelligence, NeuroEvolutionary Algorithms, and other techniques to improve monitoring accuracy and efficiency.

·         Digital Twin Technology: Studies on the applications of Digital Twins in real-time monitoring, fault detection, and enhancing system resilience.

·         AIoT for Real-Time Monitoring: Approaches that integrate AI and IoT (AIoT) for continuous data collection, analysis, and responsive monitoring in various environments.

·         Industry 4.0 Applications: AI-driven monitoring solutions implemented in factories, production lines, and critical infrastructure to improve operational efficiency and reliability.

This special issue offers a platform for interdisciplinary research that integrates AI, IoT, Digital Twin, and optimization methodologies to advance the field of smart monitoring and decision-making.

 

Suggested Themes:

We welcome submissions on a variety of themes, including:

1.       Smart Manufacturing and Quality Control: Exploration of AI-driven quality inspection systems, implementation of Digital Twins in manufacturing environments, and AIoT-based process monitoring to enhance production efficiency and product quality.

2.       Environmental Monitoring and Climate Change: Development of real-time monitoring systems for air and water quality, predictive analytics for climate modeling, and the application of AI in environmental data analysis to support sustainability efforts.

3.       Healthcare Monitoring and Patient Safety: Innovative applications of AI and IoT in healthcare settings for patient monitoring, predictive maintenance of medical equipment, and enhancing healthcare service delivery through Digital Twin technologies.

4.       Infrastructure Monitoring and Public Safety: Innovative approaches to predictive maintenance and real-time health assessment of critical infrastructure using Digital Twin technology, aiming to improve public safety and infrastructure resilience.

5.       Swarm Intelligence & NeuroEvolutionary Algorithms: Advanced optimization techniques for monitoring models, focusing on adaptive AI systems capable of operating effectively in dynamic and complex environments.

6.       IoT and AIoT in Industry 4.0: Integration of AIoT solutions for real-time data processing, enhancing operational efficiency, and enabling intelligent decision-making in industrial applications.

Cross-Disciplinary Innovations: Novel applications of AIoT technologies in sectors such as healthcare, logistics, and energy, addressing global challenges like pollution reduction, resource conservation, and sustainable development.

About ten papers

Artificial Intelligence of Things (AIoT); Digital Twins; Swarm Intelligence; Smart Monitoring; Predictive Maintenance; Smart Manufacturing; Quality Control; AI-driven Quality Inspection; Process Optimization; Industrial Automation; Industry 4.0; Environmental Monitoring; Climate Change; Air Quality Monitoring; Water Quality Monitoring; Machine Learning Algorithms; Big Data Analytics; Sustainability; Climate Resilience; Healthcare Monitoring; Patient Safety; Medical IoT; Wearable Sensors; Telemedicine; Patient Monitoring Systems; Personalized Medicine; Infrastructure Monitoring; Public Safety; Structural Health Assessments; Smart Grids; Adaptive Systems; Dynamic Environments; IoT; Edge Computing; Cyber-Physical Systems; Real-time Data Analysis; Cross-Disciplinary Innovations; Logistics Optimization; Energy Efficiency; Smart Agriculture; Resource Conservation; Pollution Reduction; Sustainable Development; Infrastructure Health Assessment; System Resilience; Quality Inspection; Environmental Data Analysis; Anomaly Detection; Fault Diagnosis; Resilience Engineering; Sensor Networks; Intelligent Decision-Making; Autonomous Systems.