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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: 31 December 2025 | Viewed by 828

Special Issue Editors


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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

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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

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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

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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 (1 paper)

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Research

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
Viewed by 531
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
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