Industrial Internet of Things (IIoT): Trends and Technologies—2nd Edition

The Industrial Internet of Things (IIoT) continues to evolve as a key enabler of digital transformation across modern industries. Building upon the progress highlighted in the first edition of this Special Issue [1], recent developments in artificial intelligence, edge and cloud computing, advanced sensing technologies, and secure communication infrastructures are enabling increasingly intelligent and interconnected industrial environments. These technologies are reshaping how industrial data are collected, processed, and utilized, supporting more efficient, autonomous, and resilient industrial operations. As IIoT systems become more pervasive, they are playing an increasingly important role in enabling smart manufacturing, predictive maintenance, industrial automation, and data-driven decision making.

Despite these advances, the rapid growth of IIoT deployments also introduces significant technical challenges. Industrial environments typically involve heterogeneous devices, resource-constrained edge nodes, large-scale distributed infrastructures, and strict requirements for reliability, security, and real-time performance. Ensuring secure and efficient communication across diverse devices remains a critical challenge, particularly in networks characterized by limited bandwidth and constrained computing resources. In addition, the increasing scale of data generated by IIoT systems raises challenges related to scalable data processing, distributed analytics, and reliable data management. Additionally, cybersecurity remains a major concern, as industrial systems become increasingly connected and exposed to potential cyber threats. Addressing these challenges requires interdisciplinary approaches that integrate networking technologies, machine learning, distributed systems, and data management frameworks.

Within this context, the second edition of this Special Issue aims to present recent advances in research and technological innovations that contribute to the development of intelligent, secure, and scalable IIoT systems. The Special Issue focuses on emerging technologies supporting industrial digitalization, including AI-driven analytics, secure network architectures, distributed computing frameworks, and scalable data management solutions. After a rigorous peer-review process, five papers were accepted and published in this Special Issue, covering a diverse range of IIoT-related topics.

The aforementioned contributions highlight recent advances in several key areas of IIoT research, including industrial communication protocols, simulation-driven data analysis, machine learning-based cybersecurity solutions, and scalable data management systems. These works collectively demonstrate how emerging technologies can support the development of more intelligent, secure, and efficient industrial IoT infrastructures.

In the first article, Cibrario Bertolotti [2] proposes a redesigned Modbus-UDP communication protocol aimed at improving real-time performance and reliability in IIoT communication systems. The proposed protocol reintroduces broadcast communication capabilities and incorporates an automatic datagram retransmission mechanism, addressing limitations of existing Modbus-TCP implementations. Experimental results show that the redesigned protocol achieves significantly lower round-trip latency and improved robustness in scenarios with network packet loss, making it suitable for industrial systems with constrained communication resources.

The second paper by Khachatrian et al. [3] presents an automated framework for generating large-scale IoT network datasets using the Contiki-NG operating system and the Cooja network simulator. The proposed pipeline integrates parameterized topology generation with distributed simulation orchestration, enabling large-scale simulation campaigns that capture complex network behaviors such as node failures and energy constraints. The generated datasets are subsequently used to train graph neural network models capable of predicting network connectivity and sensing coverage, significantly reducing the computational cost compared to full-scale simulations.

As for the third paper, the authors Pecherle et al. [4] investigate the use of federated learning for intrusion detection in Industrial IoT networks. The proposed framework enables decentralized model training across multiple IIoT devices, allowing collaborative learning without sharing raw data. Experimental results demonstrate that the federated learning approach achieves comparable detection accuracy to centralized machine learning models while reducing communication overhead and preserving data privacy, highlighting its potential for secure and scalable industrial deployments.

Meanwhile, in their paper, Panda and Muthuraman [5] propose a machine-learning-based framework for detecting routing attacks in RPL-based IoT networks. A novel dataset is generated through extensive simulations using the Cooja platform, capturing both benign network behavior and multiple routing-layer attacks. Using this dataset, the authors evaluate a comprehensive set of machine learning classifiers and ensemble models, demonstrating that ensemble learning approaches can achieve high detection accuracy for identifying malicious routing behaviors in low-power and lossy networks.

Finally, Ciumac et al. [6] present a comparative performance evaluation of MongoDB and RavenDB for data-intensive applications inspired by IIoT scenarios. Through experiments on CRUD (Create, Read, Update, and Delete) operations and datasets of varying sizes, the study analyzes performance differences between the two document-oriented databases in terms of response time, scalability, and consistency mechanisms. The results provide practical insights for selecting appropriate NoSQL data management solutions in applications characterized by large data volumes and frequent transactions.

Looking ahead, several important research directions remain open for the IIoT community. Future research is expected to further explore the integration of artificial intelligence with edge computing to enable real-time industrial analytics, the development of more robust and adaptive cybersecurity mechanisms for distributed industrial networks, and the design of scalable architectures capable of handling the massive volumes of data generated by industrial systems. In addition, improving interoperability across heterogeneous industrial platforms and developing standardized frameworks for trustworthy data sharing will be essential for realizing the full potential of IIoT in future industrial ecosystems.

The Guest Editors would like to thank all the authors for their invaluable contributions, and the reviewers for their constructive comments which helped improve the quality of the published papers.