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Applied Sciences
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IoT, Artificial Intelligence and Metaverse: Applications and Challenges
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IoT, Artificial Intelligence and Metaverse: Applications and Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 4920

Special Issue Editors

Prof. Dr. Zhangbing Zhou

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences (Beijing), Beijing, China
Interests: computer networks; mobile networks; network routing; Internet-of-Things; service computing; fog computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Patrick Hung

E-Mail Website
Guest Editor
Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
Interests: social robots; human–robot interactions; smart toy; robotic computing; services computing; security and privacy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiao Xue

E-Mail Website
Guest Editor
College of Intelligence and Computing, Tianjin University, Tianjin, China
Interests: service ecosystem; crowd intelligence
Dr. Yuzhu Wang

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences, Beijing 100083, China
Interests: high performance computing; parallel computing; GPU; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, researchers throughout academia and industry have been advancing the theory, operation, and applications of the Internet of Things (IoT), artificial intelligence (AI) and Metaverse. IoT plays an important role in ubiquitous smart objects interacting and exchanging data to improve people’s quality of life in various areas, such as transportation, manufacturing industry, health care industry, etc. AI based on deep learning and big data has been widely used in natural language processing, image recognition, etc. Metaverse, an evolving paradigm of the next-generation Internet, seamlessly integrates the real world with the virtual world by using virtual reality and augmented reality. Innovative research on how IoT and AI can make a valuable contribution to the Metaverse is needed.

This Special Issue aims to encourage researchers to study advanced methods, key technologies and vital applications of IoT, AI and Metaverse; we especially encourage research fusing IoT and AI with Metaverse, such as IoT and AI applications in the virtual world.

Topics of interest include, but are not limited to, the following:

  • Crowd intelligence and service ecosystems;
  • Service intelligence framework in the cyber-physical-social Metaverse;
  • Virtual–reality interaction for Metaverse service intelligence;
  • Deep/federated-learning-based Metaverse service intelligence;
  • Cloud/edge computation for IoT networks;
  • Intelligent system modeling and optimization;
  • Intelligent IoT pattern recognition;
  • Applications and use cases.

Prof. Dr. Zhangbing Zhou
Prof. Dr. Patrick Hung
Prof. Dr. Xiao Xue
Dr. Yuzhu Wang
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 2092 KiB  
Article
A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs
by Ruilin Lai, Gansen Zhao, Yale He and Zhihao Hou
Appl. Sci. 2023, 13(15), 8726; https://doi.org/10.3390/app13158726 - 28 Jul 2023
Cited by 2 | Viewed by 1902
Abstract
Blockchain establishes security and trust in mobile ad hoc networks (MANETs). Due to the decentralized and opportunistic communication characteristics of MANETs, hashgraph consensus is more applicable to the MANET-based blockchain. Sharding scales the consensus further through disjoint nodes in multiple shards simultaneously updating [...] Read more.
Blockchain establishes security and trust in mobile ad hoc networks (MANETs). Due to the decentralized and opportunistic communication characteristics of MANETs, hashgraph consensus is more applicable to the MANET-based blockchain. Sharding scales the consensus further through disjoint nodes in multiple shards simultaneously updating ledgers. However, the dynamic addition and deletion of nodes in a shard pose challenges regarding robustness and efficiency. Particularly, the shard is vulnerable to Sybil attacks and targeted attacks, and dishonest gossip reduces the efficiency of hashgraph consensus. Therefore, we proposed a behavior-based sharding hashgraph scheme. First, dishonest behaviors of nodes are recorded in a decentralized blacklist. Gossip information is sent to a reliable neighbor, and gossip information from another reliable neighbor is received. Second, a tree-assisted inter-sharding consensus is proposed to prevent Sybil attacks. The combination of shard recovery and reconfiguration based on node state is devised to prevent targeted attacks. Finally, we conducted the performance evaluation including security analysis and experimental evaluation to reveal the security and efficiency of the proposed scheme. Full article
(This article belongs to the Special Issue IoT, Artificial Intelligence and Metaverse: Applications and Challenges)
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20 pages, 732 KiB  
Article
Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
by Yang Bai, Xiaocui Li, Xinfan Wu and Zhangbing Zhou
Appl. Sci. 2023, 13(3), 2010; https://doi.org/10.3390/app13032010 - 3 Feb 2023
Cited by 2 | Viewed by 2402
Abstract
With the booming proliferation of user requests in the Internet of Things (IoT) network, Edge Computing (EC) is emerging as a promising paradigm for the provision of flexible and reliable services. Considering the resource constraints of IoT devices, for some delay-aware user requests, [...] Read more.
With the booming proliferation of user requests in the Internet of Things (IoT) network, Edge Computing (EC) is emerging as a promising paradigm for the provision of flexible and reliable services. Considering the resource constraints of IoT devices, for some delay-aware user requests, a heavy-workload IoT device may not respond on time. EC has sparked a popular wave of offloading user requests to edge servers at the edge of the network. The orchestration of user-requested offloading schemes creates a remarkable challenge regarding the delay in user requests and the energy consumption of IoT devices in edge networks. To solve this challenge, we propose a dynamic computation offloading strategy consisting of the following: (i) we propose the concept of intermediate nodes, which can minimize the delay in user requests and the energy consumption of the current tasks handled by IoT devices by dynamically combining task-offloading and service migration strategies; (ii) based on the workload of the current network, the intermediate node selection problem is modeled as a multi-dimensional Markov Decision Process (MDP) space, and a deep reinforcement learning algorithm is implemented to reduce the large MDP space and make a fast decision. Experimental results show that this strategy is superior to the existing baseline methods to reduce delays in user requests and the energy consumption of IoT devices. Full article
(This article belongs to the Special Issue IoT, Artificial Intelligence and Metaverse: Applications and Challenges)
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