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Sensor and IoT Technologies for Next-Generation/6G Communication Systems

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

Deadline for manuscript submissions: 30 October 2026 | Viewed by 1103

Special Issue Editors

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: 5G/6G network management and optimization; intelligent network control; green communication; smart grid communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To achieve the ambitious goals of Next-Generation/6G communication systems, significant advancements must be made in sensor and IoT technologies. These goals include ultra-reliability, low latency, and massive connectivity, and will directly enable intelligent network management, optimization, and control. Key research directions in this field include leveraging sensor and IoT technologies to enhance energy efficiency (green communication) and support the diverse range of critical 6G applications. This Special Issue seeks contributions on novel sensors, IoT architectures, and intelligent approaches to managing, controlling, and optimizing 6G environments.

The Guest Editors welcome contributions on topics including, but not limited to, the following:

  • Energy-Efficient Sensor Networks for Sustainable 6G Communications;
  • AI-Driven IoT Architectures for Intelligent 6G Network Management;
  • Massive IoT Connectivity in 6G Networks;
  • Vehicular IoT and Cooperative Sensing for Autonomous Systems in 6G;
  • The fusion of Multi-Modal Sensor Data for Intelligent Decision-Making in 6G;
  • Reconfigurable Intelligent Surfaces (RIS) Assisted by IoT Sensors in 6G;
  • Joint Resource Allocation and Sensing Strategies for Green 6G Communications;
  • Blockchain-Enabled Trust Architectures for Sensor IoT Systems in 6G;
  • Generative AI for Real-Time Network Optimization in 6G IoT Systems;
  • Multi-Modal Generative AI Models for Cross-Domain Sensor Fusion in 6G IoT Networks;
  • Sensor-Enabled UAV Swarm Communication and Coordination in 6G Networks;
  • IoT-Driven Low-Altitude Infrastructure for Smart City and Low-Altitude Economy Applications.

Dr. Peng Yu
Dr. Lisu Yu
Guest Editors

Manuscript Submission Information

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Keywords

  • 6G communication systems
  • energy-efficient sensor networks
  • intelligent IoT architectures
  • generative artificial intelligence
  • low-altitude economy and infrastructure
  • autonomous and vehicular IoT systems
  • intelligent sensing

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Published Papers (1 paper)

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Research

23 pages, 3958 KB  
Article
Performance of the Novel Reactive Access-Barring Scheme for NB-IoT Systems Based on the Machine Learning Inference
by Anastasia Daraseliya, Eduard Sopin, Julia Kolcheva, Vyacheslav Begishev and Konstantin Samouylov
Sensors 2026, 26(2), 636; https://doi.org/10.3390/s26020636 - 17 Jan 2026
Viewed by 587
Abstract
Modern 5G+grade low power wide area network (LPWAN) technologies such as Narrowband Internet-of-Things (NB-IoT) operate utilizing a multi-channel slotted ALOHA algorithm at the random access phase. As a result, the random access phase in such systems is characterized by relatively low throughput and [...] Read more.
Modern 5G+grade low power wide area network (LPWAN) technologies such as Narrowband Internet-of-Things (NB-IoT) operate utilizing a multi-channel slotted ALOHA algorithm at the random access phase. As a result, the random access phase in such systems is characterized by relatively low throughput and is highly sensitive to traffic fluctuations that could lead the system outside of its stable operational regime. Although theoretical results specifying the optimal transmission probability that maximizes the successful preamble transmission probability are well known, the lack of knowledge about the current offered traffic load at the BS makes the problem of maintaining the optimal throughput a challenging task. In this paper, we propose and analyze a new reactive access-barring scheme for NB+IoT systems based on machine learning (ML) techniques. Specifically, we first demonstrate that knowing the number of user equipments (UE) experiencing a collision at the BS is sufficient to make conclusions about the current offered traffic load. Then, we show that through utilizing ML-based techniques, one can safely differentiate between events in the Physical Random Access Channel (PRACH) at the base station (BS) side based on only the signal-to-noise ratio (SNR). Finally, we mathematically characterize the delay experienced under the proposed reactive access-barring technique. In our numerical results, we show that by utilizing modern neural network approaches, such as the XGBoost classifier, one can precisely differentiate between events on the PRACH channel with accuracy reaching 0.98 and then associate it with the number of user equipment (UE) competing at the random access phase. Our simulation results show that the proposed approach can keep the successful preamble transmission probability constant at approximately 0.3 in overloaded conditions, when for conventional NB-IoT access, this value is less than 0.05. The proposed scheme achieves near-optimal throughput in multi-channel ALOHA by employing dynamic traffic awareness to adjust the non-unit transmission probability. This proactive congestion control ensures a controlled and bounded delay, preventing latency from exceeding the system’s maximum load capacity. Full article
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