The field of Industrial Internet of Things (IIoT) has seen significant advancements in recent years, revolutionizing sectors such as manufacturing, transportation, oil & gas, and logistics. IIoT is a cornerstone of the Industry 4.0 vision, promising enhanced operational efficiency and smart communication between devices. However, the development of IIoT applications is fraught with challenges, primarily due to the limited computational, memory, and energy resources of IoT devices. These devices generate vast amounts of data at the network edge, making cloud-based processing impractical due to bandwidth limitations, latency issues, and security concerns. Edge computing has emerged as a viable solution, enabling data processing closer to the source. Despite its potential, edge computing in IIoT faces hurdles such as diverse sensor types, large-scale deployments, and resource constraints. Machine learning, with its proven success in fields like robotics and natural language processing, offers promising solutions for these challenges, particularly in resource management and intelligent data processing.
This Research Topic aims to explore the integration of machine learning with edge computing to address resource management challenges in IIoT. The primary objectives include investigating how machine learning can optimize resource allocation at various network layers, enhance data processing efficiency at edge nodes, and enable predictive maintenance. Specific questions to be answered include: How can machine learning improve MAC and network layer resource management in IIoT? What are the best practices for implementing cross-layer resource management using machine learning? How can edge computing be optimized through machine learning to reduce latency and enhance security?
To gather further insights in the realm of machine learning for resource management in IIoT, we welcome articles addressing, but not limited to, the following themes:
- MAC layer resource management for IoT applications
- Network layer resource management for IoT applications
- Cross-layer resource management for IoT applications
- Intelligent data processing for edge nodes in IoT
- Automation and optimization in edge-computing based IoT
- Big data processing and modeling of IoT
- Management and storage of data for IIoT applications
- Energy efficiency in IIoT network operation
- Security, privacy, and trust management in IIoT
- Applications of machine learning and edge computing in IIoT scenarios
The field of Industrial Internet of Things (IIoT) has seen significant advancements in recent years, revolutionizing sectors such as manufacturing, transportation, oil & gas, and logistics. IIoT is a cornerstone of the Industry 4.0 vision, promising enhanced operational efficiency and smart communication between devices. However, the development of IIoT applications is fraught with challenges, primarily due to the limited computational, memory, and energy resources of IoT devices. These devices generate vast amounts of data at the network edge, making cloud-based processing impractical due to bandwidth limitations, latency issues, and security concerns. Edge computing has emerged as a viable solution, enabling data processing closer to the source. Despite its potential, edge computing in IIoT faces hurdles such as diverse sensor types, large-scale deployments, and resource constraints. Machine learning, with its proven success in fields like robotics and natural language processing, offers promising solutions for these challenges, particularly in resource management and intelligent data processing.
This Research Topic aims to explore the integration of machine learning with edge computing to address resource management challenges in IIoT. The primary objectives include investigating how machine learning can optimize resource allocation at various network layers, enhance data processing efficiency at edge nodes, and enable predictive maintenance. Specific questions to be answered include: How can machine learning improve MAC and network layer resource management in IIoT? What are the best practices for implementing cross-layer resource management using machine learning? How can edge computing be optimized through machine learning to reduce latency and enhance security?
To gather further insights in the realm of machine learning for resource management in IIoT, we welcome articles addressing, but not limited to, the following themes:
- MAC layer resource management for IoT applications
- Network layer resource management for IoT applications
- Cross-layer resource management for IoT applications
- Intelligent data processing for edge nodes in IoT
- Automation and optimization in edge-computing based IoT
- Big data processing and modeling of IoT
- Management and storage of data for IIoT applications
- Energy efficiency in IIoT network operation
- Security, privacy, and trust management in IIoT
- Applications of machine learning and edge computing in IIoT scenarios