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SDN/NFV-Driven 6G and IoT Network Era

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 2408

Special Issue Editors


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Guest Editor
Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: UAV communications; wired and wireless resource allocation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. The Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China
2. The School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Interests: radio resource management in B5G and 6G; massive access techniques; air-and-ground integrated networks; convex optimization and graph theory

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Guest Editor
Computer Science and Engineering, Thapar Institute of Engineering and Technology, Deemed University, Patiala 147004, India
Interests: SDN; cyber physical systems; security; smart cities; deep learning; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

SDN (Software Defined Networking) and NFV (Network Function Virtualization) are key enablers towards network softwarization, which is recognized as one dominant attribute of 6G networks looking forward to the 2030s. Many elements of 6G networks and their specific hardware can be fully softwarized and virtualized. Consequently, various services and novel applications can be developed and deployed. Internet of Things (IoT) is one dominant application branch in 6G-enhanced massive Machine Type of Communication (emMTC) scenarios, and it has emerged as revolutionary for the development of futuristic network services and applications.

Given the strong interest in both industry and academia in the softwarization of 6G network and its IoT application, this Special Issue aims to collate research on SDN/NFV-driven 6G networks and IoT. There are many interesting and topical challenges that need to be addressed by the scientific research community.

The list of potential topics include, but are not limited to:

  • Management of SDIs (Software-Defined Infrastructures) for 6G networks and IoT applications;
  • APIs and management protocols for 6G networks and IoT application;
  • Virtualization of resources, services and functions in SDN and NFV for 6G networks and IoT applications;
  • Management of software-defined datacenters for 6G networks and IoT applications;
  • Efficient management of cloud computing infrastructures for 6G networks and IoT applications;
  • Resource management of SDN- or NFV-based systems for 6G networks and IoT applications;
  • Network slicing for 6G networks and IoT applications;
  • SDN control plane optimizations for 6G networks and IoT applications;
  • Network softwarization for 6G networks and IoT applications;
  • Softwarized edge cloud infrastructures for 6G networks and IoT applications;
  • Network management at the edge for 6G networks and IoT applications;
  • Service Function Chains (SFCs) modeling and representation for 6G networks and IoT applications;
  • Dynamic migration of network functions in SDN- or NFV-based systems for 6G networks and IoT applications;
  • Efficient network and service monitoring of SDN or NFV for 6G networks and IoT applications;
  • Dynamic resource scaling based on user mobility in SDN- and NFV-based systems for 6G networks and IoT applications;
  • QoS/QoE management and control in softwarized networks for 6G networks and IoT applications;
  • Applying AI technologies in softwarized networks for 6G networks and IoT applications.

Dr. Hongtao Cao
Dr. Daosen Zhai
Prof. Dr. Neeraj Kumar
Guest Editors

Manuscript Submission Information

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Keywords

  • SDN
  • NFV
  • 6G networks
  • IoT
  • emMTC

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Published Papers (1 paper)

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Research

18 pages, 4080 KiB  
Article
Securing Dynamic Service Function Chain Orchestration in EC-IoT Using Federated Learning
by Shuyi Wang and Longxiang Yang
Sensors 2022, 22(23), 9041; https://doi.org/10.3390/s22239041 - 22 Nov 2022
Cited by 1 | Viewed by 1496
Abstract
Dynamic service orchestration is becoming more and more necessary as IoT and edge computing technologies continue to advance due to the flexibility and diversity of services. With the surge in the number of edge devices and the increase in data volume of IoT [...] Read more.
Dynamic service orchestration is becoming more and more necessary as IoT and edge computing technologies continue to advance due to the flexibility and diversity of services. With the surge in the number of edge devices and the increase in data volume of IoT scenarios, there are higher requirements for the transmission security of privacy information from each edge device and the processing efficiency of SFC orchestration. This paper proposes a kind of dynamic SFC orchestration security algorithm applicable to EC-IoT scenarios based on the federated learning framework, combined with a block coordinated descent approach and the quadratic penalty algorithm to achieve communication efficiency and data privacy protection. A deep reinforcement learning algorithm is used to simultaneously adapt the SFC orchestration method in order to dynamically observe environmental changes and decrease end-to-end delay. The experimental results show that compared with the existing dynamic SFC orchestration algorithms, the proposed algorithm can achieve better convergence and latency performance under the condition of privacy protection; the overall latency is reduced by about 33%, and the overall convergence speed is improved by about 9%, which not only achieves the security of data privacy protection of edge computing nodes, but also meets the requirements of dynamic SFC orchestration. Full article
(This article belongs to the Special Issue SDN/NFV-Driven 6G and IoT Network Era)
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