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

  • Special Issue
  • - Volume 2021
  • - Article ID 5595260
  • - Research Article

Intelligent Forwarding Strategy for Congestion Control Using Q-Learning and LSTM in Named Data Networking

Sanguk Ryu | Inwhee Joe | WonTae Kim
  • Special Issue
  • - Volume 2021
  • - Article ID 5596291
  • - Research Article

Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking

Wenwen Pan | Jianzhi Wang | Jingsheng Ji
  • Special Issue
  • - Volume 2021
  • - Article ID 5545621
  • - Research Article

Education Data-Driven Online Course Optimization Mechanism for College Student

Ziqiao Wang | Ningning Yu
  • Special Issue
  • - Volume 2020
  • - Article ID 6675140
  • - Research Article

Pattern Recognition and Neural Network-Driven Roller Track Analysis via 5G Network

Yuliang Guo
  • Special Issue
  • - Volume 2020
  • - Article ID 6698448
  • - Research Article

In-Network Caching and Edge Computing-Based Live Broadcasting Optimization for Football Competitions

Zhigang Li
  • Special Issue
  • - Volume 2020
  • - Article ID 8825643
  • - Research Article

DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic

Hongyi Li | Xinrui Che
  • Special Issue
  • - Volume 2020
  • - Article ID 8868225
  • - Research Article

Performance Optimization Mechanism of Adolescent Physical Training Based on Reinforcement Learning and Markov Model

Mingze Wei | Lei Yuan
  • Special Issue
  • - Volume 2020
  • - Article ID 8826088
  • - Research Article

Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM

Daohua Pan | Hongwei Liu | ... | Zhan Zhang
Mobile Information Systems
 Journal metrics
Acceptance rate37%
Submission to final decision102 days
Acceptance to publication42 days
CiteScore4.100
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Impact Factor1.802
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