Mobile Information Systems

AI and Edge Computing-Driven Technologies for Knowledge Defined Networking


Publishing date
01 Sep 2021
Status
Closed
Submission deadline
30 Apr 2021

Lead Editor

1Tsinghua University, Beijing, China

2Southern University of Science and Technology, Shenzhen, China

3Northeastern University, Shenyang, China

4University of Hertfordshire, Hatfield, UK

This issue is now closed for submissions.
More articles will be published in the near future.

AI and Edge Computing-Driven Technologies for Knowledge Defined Networking

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are the two main driving forces transforming network architecture from rigid and ossified to flexible and programmable. With the now-widespread use of Artificial Intelligence (AI) and edge computing, the new concept of Knowledge Defined Networking (KDN) has emerged, which has the potential to address new challenges in the current programmable networks by providing mobile edge computing and edge caching capabilities together with AI to the proximity of end users. In AI and edge computing integrated networks, edge resources are managed by AI systems to offer powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with heterogeneous resources and a massive number of devices, while meeting the ultra-low latency and ultra-high reliability requirements of novel applications, e.g. self-driving cars, remote operation, intelligent transport systems, Industry 4.0, smart energy, e-health, and AR/VR services. By integrating AI functions and edge computing technologies into KDN, the network system becomes evolvable by forming a closed network loop that consists of data collection, learning, deciding, and forwarding, which will finally have full insight into the operating environment and can adapt resource allocation or orchestration in a dynamic manner.

Despite the benefits introduced by AI and edge computing-driven KDN, many challenges are still faced in this new paradigm. For example, the integrated architectures and frameworks are not clearly identified. In addition, the related concepts and protocols are not well defined. To sum up, AI and edge computing-driven KDN is still in its infancy, and only limited research efforts have been made to apply AI and edge computing in KDN.

The aim of this Special Issue is to promote integration among the technologies of AI and edge computing to speed up the development of KDN on the basis of SDN and NFV. The Special Issue will also present and highlight the advances and latest implementations and applications in the field of KDN such that the theoretical and practical frontiers can be moved forward for a deeper understanding from both the academic and industrial viewpoints. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

  • The architectures of AI, edge computing, and some new emerging techniques
  • New concepts, theories, and protocols the AI and edge computing enabled KDN
  • AI and edge computing enabled KDN in AR/VR, smart cities, IoT, and satellite communications
  • Edge computing for distributed AI-based applications
  • AI for task scheduling or/and resource allocation in edge computing
  • Network traffic prediction and control in AI-based KDN
  • AI-based data analysis in KDN
  • Edge computing-based smart resources management in KDN
  • Energy-efficient knowledge management in KDN
  • Advanced techniques or networking paradigms for KDN
  • Security and privacy in KDN

Articles

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Research on Influence of Attribute Frame Effect on Loan Decision of Undergraduate and Risk Assessment Model of Undergraduate Loan Behavior

Jinsong Luan
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  • - Volume 2021
  • - Article ID 4485589
  • - Research Article

Artificial Intelligence and Neural Network-Based Shooting Accuracy Prediction Analysis in Basketball

Hongfei Li | Maolin Zhang
  • Special Issue
  • - Volume 2021
  • - Article ID 9145952
  • - Research Article

Analysis of Physical Expansion Training Based on Edge Computing and Artificial Intelligence

Zhongle Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 3534577
  • - Research Article

Sports Dance Movement Assessment Method Using Augment Reality and Mobile Edge Computing

Fang Xu | Wentao Chu
  • Special Issue
  • - Volume 2021
  • - Article ID 9687950
  • - Research Article

Positioning of Apple’s Growth Cycle Based on Pattern Recognition

Wenfeng Li | Yulin Yuan | ... | Jiaxin Zheng
  • Special Issue
  • - Volume 2021
  • - Article ID 9985251
  • - Research Article

Efficient English Translation Method and Analysis Based on the Hybrid Neural Network

Chuncheng Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 9956482
  • - Research Article

Artificial Intelligence-Based Joint Movement Estimation Method for Football Players in Sports Training

Bin Zhang | Ming Lyu | ... | Yang Wu
  • Special Issue
  • - Volume 2021
  • - Article ID 9981767
  • - Research Article

Data Collection and Analysis of Track and Field Athletes’ Behavior Based on Edge Computing and Reinforcement Learning

Di Han
  • Special Issue
  • - Volume 2021
  • - Article ID 9977736
  • - Research Article

Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities

Ni Hong | Xuefeng Wang | Zhonghua Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 9958703
  • - Research Article

High-Performance Server-Based Live Streaming Transmission Optimization for Sports Events in Smart Cities

Si Chen | Gang Zhao
Mobile Information Systems
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Acceptance rate37%
Submission to final decision102 days
Acceptance to publication42 days
CiteScore4.100
Journal Citation Indicator0.370
Impact Factor1.802
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