Big Data Analytics for the Industrial Internet of Things—2nd Edition

Dear Colleagues,

The fourth industrial revolution (I4.0) promises to bring a significant transformation of industrial manufacturing processes through extensive digitization of factories. I4.0 pushes to evolve the rigid and hierarchical view of manufacturing systems proposed by the ISA95 model towards a more open, flexible, and integrated perspective where factory assets, empowered with communication and computing capabilities, collaborate with one another to increase productivity and enhance business agility. The ecosystem of networked and “smartified” industrial devices, universally referred to as the Industrial Internet of Things (IIOT), is one of the pillars called to sustain the digital transition fostered by I4.0. Like in many other IoT systems, the large use of such devices in industrial manufacturing settings is generating a wealth of raw data; to gain insights from such a large volume of information Big Data, analytics techniques must be employed. Unlike other “data-intensive” domains (such as financial, government, retail, etc.), the industrial one is faced with issues deriving from the need to fulfill requirements such as strong data security, high safety guarantee, and very fast data creation and consumption. Furthermore, it has to cope with complex and distributed computing contexts. On one hand, time-critical control applications consuming data streams in real-time need to run as close to data sources as possible (possibly on the IIoT device itself or on on-premise Edge nodes); on the other hand, higher computing capacity resources, such as those provided in the Cloud, are required to process Big Data at rest, such as, e.g., running analytics for use by the business departments or engineering AI/ML models to be deployed as controllers in machine control on at the shop floor.

This Special Issue aims to collect consolidated research findings and stimulate novel approaches and ideas on how to leverage the Big Data produced by IIoT devices to best support the production process and boost the business of manufacturing companies.

Potential topics include, but are not limited to, the following:

• Big Data gathering and processing in manufacturing environments;
• Distributed data processing in the Cloud continuum;
• Big Data processing for industrial control;
• Big Data processing for production control;
• Hybrid and distributed Digital Twins;
• AI/ML models in the control loop;
• Distributed ML for IIoT;
• Big Data for model-driven industrial control systems;
• Analytics for Big Data at rest;
• Resource virtualization to support data management;
• Industrial Big Data confidentiality, tampering and leakage;
• Security for IIoT devices;
• Interoperability and compliance with IIoT reference architectures;
• Scalability and robustness of data-intensive control loops.

Prof. Dr. Paolo Bellavista
Dr. Giuseppe Di Modica
Dr. Fernando Cucchietti
Guest Editors

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• industrial internet of things (IIoT)
• big data processing
• edge cloud computing
• cloud continuum
• digital twins
• distributed machine learning for IIoT
• big data processing for control loops
• performance isolation over virtualized resources

• Big Data Analytics for the Industrial Internet of Things in Future Internet (6 articles)