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Advanced Industrial Internet of Things (IIoT): Network Architectures and Distributed Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 4471

Special Issue Editors

Dr. Francesco Chiti

E-Mail Website
Guest Editor
Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Firenze, Italy
Interests: Internet of Things and software-defined networking/network function virtualization paradigms with application to 6G systems; 5G vehicular networks; Industry 4.0; smart cities
Special Issues, Collections and Topics in MDPI journals
Dr. Giampaolo Cuozzo

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering, Wi-Lab, CNIT, University of Bologna, 40136 Bologna, Italy
Interests: multiple-access schemes; radio resource management; scheduling; multi-hop protocols; reinforcement learning; deep learning; internet of things; THz networks
Special Issues, Collections and Topics in MDPI journals
Dr. Deepti Gupta

E-Mail Website
Guest Editor
Subhani Department of Computer Information Systems, Texas A&M University-Central Texas, Killeen, TX 76549, USA
Interests: IoT security; machine learning; cloud computing; access control; anomaly detection

Special Issue Information

Dear Colleagues,

The unprecedented advancements in hardware technologies and the momentum of the Fourth Industrial Revolution (Industry 4.0) have driven the evolution of the Internet of Things (IoT) paradigm toward more sophisticated and complex systems, commonly referred to as IoT 2.0.

Industrial Internet of Things (IIoT) devices are increasingly mobile and geographically distributed across wide operational areas, making the design of effective internetworking strategies a critical challenge. In this context, the integration of 5G, hybrid 5G/6G, or fully 6G infrastructures and technologies offers transformative potential. These advancements promise unprecedented flexibility and scalability, facilitating the creation of an extensive, intelligent, and interconnected industrial ecosystem. Furthermore, IIoT systems merge pervasive remote process control with machine interconnectedness, underscoring the importance of distributed architectures.

This Special Issue seeks to explore challenges and innovations tied to this emerging paradigm, with a focus on network architecture and industrial service frameworks. Given the large-scale complexity of IIoT networks, there is a pressing need for adaptable network architectures and protocol designs that are capable of unifying diverse domains and dynamically managing escalating complexity. One promising approach to address this lies in software-defined networking (SDN), an emerging paradigm that enables on-demand network (re)design by decoupling the control plane from the data-forwarding plane. SDN has also been proposed to overcome the resource-constrained nature of IoT devices, ensuring adequate quality of service (QoS) for data flows while advancing threat detection and mitigation strategies.

At its core, this Special Issue emphasizes network architecture and communication protocol design in smart industrial environments, particularly those supporting distributed and cooperative sensing/processing services aligned with the IoT-as-a-Service model. Central to this evolution is the strategic integration of explainable artificial intelligence (AI) and machine learning (ML) into network frameworks. These technologies herald a future of transparent, trustworthy, and self-sufficient networks capable of autonomous adaptation and optimization.

We cordially invite you to contribute original research addressing these themes. While the topics above highlight key areas of interest, submissions on related challenges and innovations are also welcome. We look forward to receiving your valuable contributions.

This Special Issue is partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRPP) of Next Generation EU (NGEU), partnership on "Telecommunications of the Future" (PE00000001—program "RESTART").

Dr. Francesco Chiti
Dr. Giampaolo Cuozzo
Dr. Deepti Gupta
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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
  • industrial web of things
  • 6G
  • cellular IoT architectures and protocols
  • distributed machine learning
  • security and privacy

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Published Papers (4 papers)

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Research

17 pages, 408 KB  
Article
A Low-Code Containerized Edge Architecture for IIoT Telemetry Orchestration: Mitigating Cloud API Rate Limits Through Dual-Path Routing
by Jesús Rosa-Bilbao
Sensors 2026, 26(10), 3082; https://doi.org/10.3390/s26103082 - 13 May 2026
Viewed by 255
Abstract
This paper investigates whether a low-code workflow engine can operate as practical Industrial Internet of Things (IIoT) middleware at the edge when cloud application programming interface (API) rate limits make direct telemetry upload unsustainable. The main contribution is a dual-path architecture in which [...] Read more.
This paper investigates whether a low-code workflow engine can operate as practical Industrial Internet of Things (IIoT) middleware at the edge when cloud application programming interface (API) rate limits make direct telemetry upload unsustainable. The main contribution is a dual-path architecture in which a Hot Path persists all telemetry locally, while a Cold Path selectively forwards only anomalous or summary events to cloud services. The architecture is implemented as a lightweight containerized stack based on n8n, Eclipse Mosquitto, InfluxDB, and Grafana, and evaluated on a Raspberry Pi 4 under baseline, cloud-only saturation, and edge-filtered stress scenarios. Under the cloud-only condition, the external endpoint is throttled to approximately 60 requests/min, yielding a rejection rate of 98.0% (95% Wilson confidence interval: 97.43–98.44%). Under the dual-path condition, the same inbound load is fully retained locally while outbound cloud traffic is reduced by 98.0%, thereby avoiding throttling without sacrificing edge-side data fidelity. The measured Hot Path processing latency remains around 5 ms on average, with observed peaks below 10 ms, which is compatible with soft real-time monitoring workloads. Compared with more established low-code tools such as Node-RED, the novelty of the study is not the existence of visual orchestration itself, but the combination of containerized deployment, explicit hot/cold decoupling, and an empirical rate-limit mitigation analysis focused on low-cost edge hardware. Full article
(This article belongs to the Special Issue Advanced Industrial Internet of Things (IIoT): Network Architectures and Distributed Applications)
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20 pages, 1368 KB  
Article
Comparison of Input-Data Matrix Representations Used for Continual Learning with Orthogonal Weight Modification on Edge Devices
by Ronald Mendez, Andreas Maier and Johannes Emmert
Sensors 2026, 26(2), 425; https://doi.org/10.3390/s26020425 - 9 Jan 2026
Viewed by 524
Abstract
The number of industrial processes in which smart devices have been employed rises every day; these devices can be found performing tasks related to the automation, digitization, or optimization of the process. Generally, for these tasks, the devices need to communicate with each [...] Read more.
The number of industrial processes in which smart devices have been employed rises every day; these devices can be found performing tasks related to the automation, digitization, or optimization of the process. Generally, for these tasks, the devices need to communicate with each other and with a central unit monitored by humans, which is where Industrial Internet of Things (IIoT) comes into play, allowing a network to be built between the devices. Communication might be enough for monitoring purposes, but the optimization and automation of the process are yet to be addressed. In this study, we use an object detection sensor as an initial test subject to explore the Artificial Neural Twin (ANT) as a distributed-process optimization tool in combination with Orthogonal Weight Modification (OWM), a continual learning (CL) method used to augment self-operating devices (i.e., microcontrollers used for machine-vision sensors) with the capacity to learn new tasks autonomously. Some of these devices lack the hardware capacity to run a CL algorithm, which also motivated the comparison of the Fisher matrix, NEig-OWM, and LoRA as matrix approximations to reduce the complexity of the operations between them. Among the compared matrices, we found the Fisher matrix to be the least expensive solution with a negligible reduction in the model’s performance after CL, which makes it a viable solution for large AI models, while NEig-OWM is better suited for smaller models that require fewer hardware resources but more control over the CL algorithm. Full article
(This article belongs to the Special Issue Advanced Industrial Internet of Things (IIoT): Network Architectures and Distributed Applications)
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23 pages, 1172 KB  
Article
SDN-Oriented 6G Industrial IoT Architecture Design and Application to Optimal RIS Placement and Selection
by Francesco Chiti, Matteo Lotti, Sara Picchioni and Laura Pierucci
Sensors 2026, 26(2), 411; https://doi.org/10.3390/s26020411 - 8 Jan 2026
Viewed by 809
Abstract
This paper presents a high-level system architecture that integrates the Software Defined Networking (SDN) paradigm in 5G/6G networks with the aim of supporting the requirements expected for Industrial Internet of Things (IIoT) devices and services. To this purpose, we include multiple Reconfigurable Intelligent [...] Read more.
This paper presents a high-level system architecture that integrates the Software Defined Networking (SDN) paradigm in 5G/6G networks with the aim of supporting the requirements expected for Industrial Internet of Things (IIoT) devices and services. To this purpose, we include multiple Reconfigurable Intelligent Surfaces (RISs) systems and provide for them an abstract representation consistent with the OpenFlow interface and messaging framework. The main contribution of this is firstly focused on designing a comprehensive framework that specifies the modules, components, interfaces, protocols, and message exchanges across the typical three layers SDN architecture. In addition, we characterize the Network Discovery (ND) and Host Discovery (HD) protocols that enable the SDN Controller to achieve a global and updated view of the network. Then, the RIS Placement and Selection Problem (RPSP) is formulated by using two graph-theory approaches, i.e., Set Covering (SC) and Minimum Spanning Tree (MST). Finally, we conduct an extensive simulation campaign that evaluates the performance of the discovery phases and the RIS placement/selection algorithms in realistic industrial environments. The results highlight the advantages achieved in terms of coverage and complexity. Full article
(This article belongs to the Special Issue Advanced Industrial Internet of Things (IIoT): Network Architectures and Distributed Applications)
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17 pages, 940 KB  
Article
ON-NSW: Accelerating High-Dimensional Vector Search on Edge Devices with GPU-Optimized NSW
by Taeyoon Park, Haena Lee, Yedam Na and Wook-Hee Kim
Sensors 2025, 25(20), 6461; https://doi.org/10.3390/s25206461 - 19 Oct 2025
Cited by 1 | Viewed by 2294
Abstract
The Industrial Internet of Things (IIoT) increasingly relies on vector embeddings for analytics and AI-driven applications such as anomaly detection, predictive maintenance, and sensor fusion. Efficient approximate nearest neighbor search (ANNS) is essential for these workloads. Graph-based methods are among the most representative [...] Read more.
The Industrial Internet of Things (IIoT) increasingly relies on vector embeddings for analytics and AI-driven applications such as anomaly detection, predictive maintenance, and sensor fusion. Efficient approximate nearest neighbor search (ANNS) is essential for these workloads. Graph-based methods are among the most representative methods for ANNS. However, most existing graph-based methods, such as Hierarchical Navigable Small World (HNSW), are designed for CPU execution on high-end servers and give little consideration to the unique characteristics of edge devices. In this work, we present ON-NSW, a GPU-optimized design of HNSW optimized for edge devices. ON-NSW employs a flat graph structure derived from HNSW to fully exploit GPU parallelism. In addition, it carefully places HNSW components in the unified memory architecture of NVIDIA Jetson Orin Nano. Also, ON-NSW introduces warp-level parallel neighbor exploration and lightweight synchronization to reduce search latency. Our experimental results on real-world high-dimensional datasets show that ON-NSW achieves up to 1.44× higher throughput than the original HNSW on the NVIDIA Jetson device while maintaining comparable recall. These results demonstrate that ON-NSW provides an effective design for enabling efficient and high-throughput vector search on embedded edge platforms. Full article
(This article belongs to the Special Issue Advanced Industrial Internet of Things (IIoT): Network Architectures and Distributed Applications)
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