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Future Internet
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Edge AI: Applications of Edge Computing and Artificial Intelligence in...
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Edge AI: Applications of Edge Computing and Artificial Intelligence in IoT

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 3930

Special Issue Editor

Prof. Dr. Vijayakumar Varadarajan

E-Mail Website
Guest Editor
School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Interests: detection system; network security; intrusion detection system; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 5.0 is a promising future that will integrate humans, the Internet of Things (IIoT), Big Data, Edge Computing, and Artificial Intelligence to obtain a higher level of efficiency and productivity. Edge computing improves the performance of the network and reduces latency at the same time. The integration of Artificial Intelligence (AI) enhances the decision-making capability of the system and increases user experience. The integration of AI in edge computing for IoT also possesses some challenges like higher resource requirements for machine learning, vulnerable security features, energy consumption, data storage, lack of security standards, etc. This Special Issue aims to provide a platform where researchers contribute their findings regarding the applications of Edge AI and IoT and provide solutions for different problems. The collected research papers will act as a learning resource for the stakeholders who are working in the area and can find a solution to problems, they may face in industry 5.0.

Potential topics include, but are not limited to:

  • Artificial Intelligence algorithms for edge computing
  • Applications of edge AI in IoT
  • Hardware architecture for edge AI and IoT
  • Lightweight machine learning algorithms for edge AI
  • Federated learning for edge AI
  • Applications of TinyML in edge AI and IoT
  • Offloading strategy in edge AI
  • Deep Learning for edge AI and IoT
  • Big Data processing for edge AI and IoT
  • AI-based mobile edge computing for IoT
  • Challenges, opportunities, and case studies in edge AI for IoT
  • Security and privacy in edge AI for IoT

Prof. Dr. Vijayakumar Varadarajan
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Future Internet is an international peer-reviewed open access monthly 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 1600 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.

Published Papers (2 papers)

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Research

16 pages, 3048 KiB  
Article
A Carrying Method for 5G Network Slicing in Smart Grid Communication Services Based on Neural Network
by Yang Hu, Liangliang Gong, Xinyang Li, Hui Li, Ruoxin Zhang and Rentao Gu
Future Internet 2023, 15(7), 247; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15070247 - 20 Jul 2023
Cited by 3 | Viewed by 1039
Abstract
When applying 5G network slicing technology, the operator’s network resources in the form of mutually isolated logical network slices provide specific service requirements and quality of service guarantees for smart grid communication services. In the face of the new situation of 5G, which [...] Read more.
When applying 5G network slicing technology, the operator’s network resources in the form of mutually isolated logical network slices provide specific service requirements and quality of service guarantees for smart grid communication services. In the face of the new situation of 5G, which comprises the surge in demand for smart grid communication services and service types, as well as the digital and intelligent development of communication networks, it is even more important to provide a self-intelligent resource allocation and carrying method when slicing resources are allocated. To this end, a carrying method based on a neural network is proposed. The objective is to establish a hierarchical scheduling system for smart grid communication services at the power smart gate-way at the edge, where intelligent classification matching of smart grid communication services to (i) adapt to the characteristics of 5G network slicing and (ii) dynamic prediction of traffic in the slicing network are both realized. This hierarchical scheduling system extracts the data features of the services and encodes the data through a one-dimensional Convolutional Neural Network (1D CNN) in order to achieve intelligent classification and matching of smart grid communication services. This system also combines with Bidirectional Long Short-Term Memory Neural Network (BILSTM) in order to achieve a dynamic prediction of time-series based traffic in the slicing network. The simulation results validate the feasibility of a service classification model based on a 1D CNN and a traffic prediction model based on BILSTM for smart grid communication services. Full article
(This article belongs to the Special Issue Edge AI: Applications of Edge Computing and Artificial Intelligence in IoT)
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19 pages, 3398 KiB  
Article
Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
by Zhiqiang Dai, Gaochao Xu, Ziqi Liu, Jiaqi Ge and Wei Wang
Future Internet 2022, 14(8), 226; https://0-doi-org.brum.beds.ac.uk/10.3390/fi14080226 - 26 Jul 2022
Cited by 3 | Viewed by 1500
Abstract
Unmanned aerial vehicles (UAVs) have the characteristics of portability, safety, and strong adaptability. In the case of a maritime disaster, they can be used for personnel search and rescue, real-time monitoring, and disaster assessment. However, the power, computing power, and other resources of [...] Read more.
Unmanned aerial vehicles (UAVs) have the characteristics of portability, safety, and strong adaptability. In the case of a maritime disaster, they can be used for personnel search and rescue, real-time monitoring, and disaster assessment. However, the power, computing power, and other resources of UAVs are often limited. Therefore, this paper combines a UAV and mobile edge computing (MEC), and designs a deep reinforcement learning-based online task offloading (DOTO) algorithm. The algorithm can obtain an online offloading strategy that maximizes the residual energy of the UAV by jointly optimizing the UAV’s time and communication resources. The DOTO algorithm adopts time division multiple access (TDMA) to offload and schedule the UAV computing task, integrates wireless power transfer (WPT) to supply power to the UAV, calculates the residual energy corresponding to the offloading action through the convex optimization method, and uses an adaptive K method to reduce the computational complexity of the algorithm. The simulation results show that the DOTO algorithm proposed in this paper for the energy-saving goal of maximizing the residual energy of UAVs in MEC can provide the UAV with an online task offloading strategy that is superior to other traditional benchmark schemes. In particular, when an individual UAV exits the system due to insufficient power or failure, or a new UAV is connected to the system, it can perform timely and automatic adjustment without manual participation, and has good stability and adaptability. Full article
(This article belongs to the Special Issue Edge AI: Applications of Edge Computing and Artificial Intelligence in IoT)
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