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Digital Twin Networks, Network Virtualization and Applications for Next-Generation Sensor Systems

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 725

Special Issue Editors


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Guest Editor
Department of Network Engineering, Polytechnic University of Catalonia (UPC), Jordi Girona 1-3, E-08034 Barcelona, Spain
Interests: network virtualization; network softwarization; digital twin networks; B5G/6G technology; IoT
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Electronics and Telecommunications Engineering Department, Universidad de Antioquia, Antioquia, Medellin 050010, Colombia
Interests: network virtualization; digital twin networks; energy efficiency and network flows; IoT

Special Issue Information

Dear Colleagues,

The convergence of Digital Twin Networks with 6G technology and advanced Sensor Networks represents a promising breakthrough capable of transforming how we connect, communicate, and utilize networks. By integrating terahertz communications, AI-native network orchestration, and ultra-massive MIMO, this synergy establishes the foundation for a new generation of intelligent, ultra-responsive applications and services, surpassing the limits of existing solutions and enabling unprecedented levels of automation, connectivity, and real-time data exchange. Network virtualization serves as a key enabler for the seamless deployment and scalability of Digital Twin Networks for a new generation of resilient, adaptive, and context-aware applications and services.

This Special Issue aims to highlight original research and review articles in this field. Potential topics in Digital Twin Networks for Sensor Networks include, but are not limited to, the following:

  • Architectural foundations;
  • Modelling;
  • Data acquisition and synchronization;
  • Network communications and protocols;
  • Energy-efficient protocols for large-scale sensor-digital twin network connectivity;
  • Data management;
  • AI processing;
  • Edge devices;
  • Security;
  • Resilience and fault-tolerant strategies;
  • Application domains.

Dr. Xavier Hesselbach
Dr. Juan Felipe Botero
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

  • digital twin networks
  • virtualization
  • AI
  • massive IoT
  • 6G
  • edge computing
  • terahertz communication
  • ultra-low latency

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Published Papers (1 paper)

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Research

20 pages, 5039 KB  
Article
RL-PMO: A Reinforcement Learning-Based Optimization Algorithm for Parallel SFC Migration
by Hefei Hu, Zining Liu and Fan Wu
Sensors 2026, 26(1), 242; https://doi.org/10.3390/s26010242 - 30 Dec 2025
Viewed by 414
Abstract
In edge networks, hardware failures and resource pressure may disrupt Service Function Chains (SFCs) deployed on the failed node, making it necessary to efficiently migrate multiple Virtual Network Functions (VNFs) under limited resources. To address these challenges, this paper proposes an offline reinforcement [...] Read more.
In edge networks, hardware failures and resource pressure may disrupt Service Function Chains (SFCs) deployed on the failed node, making it necessary to efficiently migrate multiple Virtual Network Functions (VNFs) under limited resources. To address these challenges, this paper proposes an offline reinforcement learning-based parallel migration optimization algorithm (RL-PMO) to enable parallel migration of multiple VNFs. The method follows a two-stage framework: in the first stage, improved heuristic algorithms are used to generate high-quality migration trajectories and construct a multi-scenario dataset; in the second stage, the Decision Mamba model is employed to train the policy network. With its selective modeling capability for structured sequences, Decision Mamba can capture the dependencies between VNFs and underlying resources. Combined with a twin-critic architecture and CQL regularization, the model effectively mitigates distribution shift and Q-value overestimation. The simulation results show that RL-PMO maintains approximately a 95% migration success rate across different load conditions and improves performance by about 13% under low and medium loads and up to 17% under high loads compared with typical offline RL algorithms such as IQL. Overall, RL-PMO provides an efficient, reliable, and resource-aware solution for SFC migration in node failure scenarios. Full article
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