Wireless Communications and Mobile Computing

Efficient Resource Management, Organization, and Load Balancing Of 6G-Enabled Next Generation Internet of Things

Publishing date
01 Aug 2021
Submission deadline
26 Mar 2021

Lead Editor

1National University of Computer & Emerging Sciences, Islamabad, Pakistan

2Brandon University, Brandon, Canada

3Western Norway University of Applied Sciences, Bergen, Norway

Efficient Resource Management, Organization, and Load Balancing Of 6G-Enabled Next Generation Internet of Things

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This Issue is now open for submissions.

Papers are published upon acceptance, regardless of the Special Issue publication date.

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Next generation Internet of Things (NGIoT) is a communication paradigm that aims to connect billions of devices with each other using the Internet, which will be accompanied by enabling technologies like massive multiple-input multiple-output, coordinated multipoint processing, centralized radio access network, software-defined networking, network function virtualization, cognitive radios, and Artificial Intelligence (AI).

In the coming decade, NGIoT will have a strong influence on the business and technological landscapes. The NGIoT will generate a massive volume of traffic from heterogenous networks. There is a need for automated and adaptive techniques that can process big data and deal with low-latency applications such as Internet of Health Things (IoHT), smart grids, intelligent transportation systems, and smart cities. With the advent of 6G, NGIoT will continue to generate traffic with stringent Quality of Service (QoS) requirements. NGIoT applications are involved in self-organizing and adaptive capabilities taking advantage of recent developments in AI techniques such as deep reinforcement learning and federated learning techniques.

The objective of this Special Issue is to explore the area of NGIoT for efficient resource management, organization, and load balancing of 6G-enabled networks. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

  • Resource management in 6G-enabled NGIoT using software defined networking and network function virtualization
  • Load balancing in 6G-enabled NGIoT
  • Federated learning architectures for 6G-enabled NGIoT
  • Federated learning theories and algorithms
  • Deep reinforcement learning for 6G-enabeled NGIoT networks
  • Novel architectures for 6G-enabled NGIoT
  • Traffic engineering and traffic optimization techniques for 6G-enabled NGIoT
  • Machine learning techniques for data analytics
Wireless Communications and Mobile Computing
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