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

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 8252

Special Issue Editors

Dr. Zhihao Liu

E-Mail Website
Guest Editor
Department of Production Engineering, The Royal Institute of Technology (KTH), 114 28 Stockholm, Sweden
Interests: Industry 5.0; digital twin and metaverse; embodied AI; human–robot collaboration; robot learning; reinforcement learning; neural information processing; human-compatible AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Franco Davoli

E-Mail Website
Guest Editor
1. Department of Electrical, Electronic, and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16126 Genova, Italy
2. CNIT National Laboratory of Smart and Secure Networks (S2N), 16126 Genova, Italy
Interests: dynamic resource allocation in multiservice networks and in future internet; mobile wireless and satellite networks; multimedia communications and services; flexible; programmable; energy-efficient networking
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Borsatti

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy
Interests: NFV; SDN; intent-based networking; MEC; 5G network slicing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Industrial Internet of Things (IIoT) has emerged as a transformative force revolutionizing industries across the globe. It encompasses the integration of sensor technologies, machine learning, big data analytics, and connectivity to revolutionize industrial processes and enhance productivity. This transformation has not just enhanced industrial processes and productivity but is also paving the way for innovative business models. As the IIoT continues to evolve rapidly, it is crucial to delve into its latest trends, explore state-of-the-art technologies, and understand the emerging challenges and future possibilities.

In this Special Issue, we welcome submissions focusing on the dynamic landscape of the Industrial Internet of Things (IIoT) and its evolving trends and technologies. We encourage researchers, academicians, and industry practitioners to contribute their significant insights and research findings to this Special Issue, fostering the dissemination of knowledge and advancements in the realm of the Industrial Internet of Things (IIoT).

This Special Issue seeks to explore various facets of IIoT, including, but not limited to, the following:

  • IIoT applications in manufacturing, healthcare, energy, transportation, agriculture, and other industries;
  • Edge computing and IIoT;
  • Security and privacy challenges in IIoT;
  • AI and machine learning in IIoT systems;
  • Big data analytics for IIoT;
  • Connectivity protocols and standards for IIoT: URLLC, TSN, OPC UA, etc.;
  • Sensor technologies and their role in IIoT;
  • Industry 4.0 and the convergence of IIoT with other technologies;
  • Case studies and real-world implementations of IIoT solutions.

Dr. Zhihao Liu
Prof. Dr. Franco Davoli
Dr. Davide Borsatti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • industrial Internet of Things (IIoT)
  • edge computing
  • cybersecurity in IIoT
  • Artificial Intelligence (AI) in IIoT applications
  • Industry 5.0 with IIoT
  • big data analytics for IIoT
  • sensor networks for IIoT
  • IIoT-driven smart manufacturing
  • IIoT standards and protocols
  • wireless communication in industrial environments

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Related Special Issue

Published Papers (6 papers)

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Editorial

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3 pages, 127 KB  
Editorial
Industrial Internet of Things (IIoT): Trends and Technologies—2nd Edition
by Zhihao Liu, Franco Davoli and Davide Borsatti
Future Internet 2026, 18(4), 185; https://doi.org/10.3390/fi18040185 - 1 Apr 2026
Viewed by 433
Abstract
The Industrial Internet of Things (IIoT) continues to evolve as a key enabler of digital transformation across modern industries [...] Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies—2nd Edition)

Research

Jump to: Editorial

26 pages, 1629 KB  
Article
Performance Evaluation of MongoDB and RavenDB in IIoT-Inspired Data-Intensive Mobile and Web Applications
by Mădălina Ciumac, Cornelia Aurora Győrödi, Robert Ștefan Győrödi and Felicia Mirabela Costea
Future Internet 2026, 18(1), 57; https://doi.org/10.3390/fi18010057 - 20 Jan 2026
Cited by 1 | Viewed by 1267
Abstract
The exponential growth of data generated by modern digital applications, including systems inspired by Industrial Internet of Things (IIoT) requirements, has accelerated the adoption of NoSQL databases due to their scalability, flexibility, and performance advantages over traditional relational systems. Among document-oriented solutions, MongoDB [...] Read more.
The exponential growth of data generated by modern digital applications, including systems inspired by Industrial Internet of Things (IIoT) requirements, has accelerated the adoption of NoSQL databases due to their scalability, flexibility, and performance advantages over traditional relational systems. Among document-oriented solutions, MongoDB and RavenDB stand out due to their architectural features and their ability to manage dynamic, large-scale datasets. This paper presents a comparative analysis of MongoDB and RavenDB, focusing on the performance of fundamental CRUD (Create, Read, Update, Delete) operations. To ensure a controlled performance evaluation, a mobile and web application for managing product orders was implemented as a case study inspired by IIoT data characteristics, such as high data volume and frequent transactional operations, with experiments conducted on datasets ranging from 1000 to 1,000,000 records. Beyond the core CRUD evaluation, the study also investigates advanced operational scenarios, including joint processing strategies (lookup versus document inclusion), bulk data ingestion techniques, aggregation performance, and full-text search capabilities. These complementary tests provide deeper insight into the systems’ architectural strengths and their behavior under more complex and data-intensive workloads. The experimental results highlight MongoDB’s consistent performance advantage in terms of response time, particularly with large data volumes, while RavenDB demonstrates competitive behavior and offers additional benefits such as built-in ACID compliance, automatic indexing, and optimized mechanisms for relational retrieval and bulk ingestion. The analysis does not propose a new benchmarking methodology but provides practical insights for selecting an appropriate document-oriented database for data intensive mobile and web application contexts, including IIoT-inspired data characteristics, based on a controlled single-node experimental setting, while acknowledging the limitations of a single-host experimental environment. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies—2nd Edition)
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22 pages, 1021 KB  
Article
A Multiclass Machine Learning Framework for Detecting Routing Attacks in RPL-Based IoT Networks Using a Novel Simulation-Driven Dataset
by Niharika Panda and Supriya Muthuraman
Future Internet 2026, 18(1), 35; https://doi.org/10.3390/fi18010035 - 7 Jan 2026
Cited by 4 | Viewed by 1029
Abstract
The use of resource-constrained Low-Power and Lossy Networks (LLNs), where the IPv6 Routing Protocol for LLNs (RPL) is the de facto routing standard, has increased due to the Internet of Things’ (IoT) explosive growth. Because of the dynamic nature of IoT deployments and [...] Read more.
The use of resource-constrained Low-Power and Lossy Networks (LLNs), where the IPv6 Routing Protocol for LLNs (RPL) is the de facto routing standard, has increased due to the Internet of Things’ (IoT) explosive growth. Because of the dynamic nature of IoT deployments and the lack of in-protocol security, RPL is still quite susceptible to routing-layer attacks like Blackhole, Lowered Rank, version number manipulation, and Flooding despite its lightweight architecture. Lightweight, data-driven intrusion detection methods are necessary since traditional cryptographic countermeasures are frequently unfeasible for LLNs. However, the lack of RPL-specific control-plane semantics in current cybersecurity datasets restricts the use of machine learning (ML) for practical anomaly identification. In order to close this gap, this work models both static and mobile networks under benign and adversarial settings by creating a novel, large-scale multiclass RPL attack dataset using Contiki-NG’s Cooja simulator. To record detailed packet-level and control-plane activity including DODAG Information Object (DIO), DODAG Information Solicitation (DIS), and Destination Advertisement Object (DAO) message statistics along with forwarding and dropping patterns and objective-function fluctuations, a protocol-aware feature extraction pipeline is developed. This dataset is used to evaluate fifteen classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), AdaBoost (AB), and XGBoost (XGB) and several ensemble strategies like soft/hard voting, stacking, and bagging, as part of a comprehensive ML-based detection system. Numerous tests show that ensemble approaches offer better generalization and prediction performance. With overfitting gaps less than 0.006 and low cross-validation variance, the Soft Voting Classifier obtains the greatest accuracy of 99.47%, closely followed by XGBoost with 99.45% and Random Forest with 99.44%. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies—2nd Edition)
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16 pages, 1100 KB  
Article
Federated Learning-Based Intrusion Detection in Industrial IoT Networks
by George Dominic Pecherle, Robert Ștefan Győrödi and Cornelia Aurora Győrödi
Future Internet 2026, 18(1), 2; https://doi.org/10.3390/fi18010002 - 19 Dec 2025
Cited by 6 | Viewed by 1893
Abstract
Federated learning (FL) is a promising privacy-preserving paradigm for machine learning in distributed environments. Although FL reduces communication overhead, it does not itself provide low-latency guarantees. In IIoT environments, real-time responsiveness is primarily enabled by edge computing and local inference, while FL contributes [...] Read more.
Federated learning (FL) is a promising privacy-preserving paradigm for machine learning in distributed environments. Although FL reduces communication overhead, it does not itself provide low-latency guarantees. In IIoT environments, real-time responsiveness is primarily enabled by edge computing and local inference, while FL contributes indirectly by minimizing the need to transmit raw data across the network. This paper explores the use of FL for intrusion detection in IIoT networks and compares its performance with traditional centralized machine learning approaches. A simulated IIoT environment was developed in which each node locally trains a model on synthetic normal and attack traffic data, sharing only model parameters with a central server. The Flower framework was employed to coordinate training and model aggregation across multiple clients without exposing raw data. Experimental results show that FL achieves detection accuracy comparable to centralized models while significantly reducing privacy risks and network transmission overhead. These results demonstrate the feasibility of FL as a secure and scalable solution for IIoT intrusion detection. Future work will validate the approach on real-world datasets and heterogeneous edge devices to further assess its robustness and effectiveness. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies—2nd Edition)
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17 pages, 3261 KB  
Article
Scalable Generation of Synthetic IoT Network Datasets: A Case Study with Cooja
by Hrant Khachatrian, Aram Dovlatyan, Greta Grigoryan and Theofanis P. Raptis
Future Internet 2025, 17(11), 518; https://doi.org/10.3390/fi17110518 - 13 Nov 2025
Cited by 2 | Viewed by 1025
Abstract
Predicting the behavior of Internet of Things (IoT) networks under irregular topologies and heterogeneous battery conditions remains a significant challenge. Simulation tools can capture these effects but can require high manual effort and computational capacity, motivating the use of machine learning surrogates. This [...] Read more.
Predicting the behavior of Internet of Things (IoT) networks under irregular topologies and heterogeneous battery conditions remains a significant challenge. Simulation tools can capture these effects but can require high manual effort and computational capacity, motivating the use of machine learning surrogates. This work introduces an automated pipeline for generating large-scale IoT network datasets by bringing together the Contiki-NG firmware, parameterized topology generation, and Slurm-based orchestration of Cooja simulations. The system supports a variety of network structures, scalable node counts, randomized battery allocations, and routing protocols to reproduce diverse failure modes. As a case study, we conduct over 10,000 Cooja simulations with 15–75 battery-powered motes arranged in sparse grid topologies and operating the RPL routing protocol, consuming 1300 CPU-hours in total. The simulations capture realistic failure modes, including unjoined nodes despite physical connectivity and cascading disconnects caused by battery depletion. The resulting graph-structured datasets are used for two prediction tasks: (1) estimating the last successful message delivery time for each node and (2) predicting network-wide spatial coverage. Graph neural network models trained on these datasets outperform baseline regression models and topology-aware heuristics while evaluating substantially faster than full simulations. The proposed framework provides a reproducible foundation for data-driven analysis of energy-limited IoT networks. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies—2nd Edition)
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21 pages, 787 KB  
Article
Rethinking Modbus-UDP for Real-Time IIoT Systems
by Ivan Cibrario Bertolotti
Future Internet 2025, 17(8), 356; https://doi.org/10.3390/fi17080356 - 5 Aug 2025
Cited by 2 | Viewed by 1694
Abstract
The original Modbus specification for RS-485 and RS-232 buses supported broadcast transmission. As the protocol evolved into Modbus-TCP, to use the TCP transport, this useful feature was lost, likely due to the point-to-point nature of TCP connections. Later proposals did not restore the [...] Read more.
The original Modbus specification for RS-485 and RS-232 buses supported broadcast transmission. As the protocol evolved into Modbus-TCP, to use the TCP transport, this useful feature was lost, likely due to the point-to-point nature of TCP connections. Later proposals did not restore the broadcast transmission capability, although they used UDP as transport and UDP, by itself, would have supported it. Moreover, they did not address the inherent lack of reliable delivery of UDP, leaving datagram loss detection and recovery to the application layer. This paper describes a novel redesign of Modbus-UDP that addresses the aforementioned shortcomings. It achieves a mean round-trip time of only 38% with respect to Modbus-TCP and seamlessly supports a previously published protocol based on Modbus broadcast. In addition, the built-in retransmission of Modbus-UDP reacts more efficiently than the equivalent Modbus-TCP mechanism, exhibiting 50% of its round-trip standard deviation when subject to a 1% two-way IP datagram loss probability. Combined with the lower overhead of UDP versus TCP, this makes the redesigned Modbus-UDP protocol better suited for a variety of Industrial Internet of Things systems with limited computing and communication resources. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies—2nd Edition)
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