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 2836

Special Issue Editors

School of Information Engineering, China University of Geosciences, Beijing 100083, China
Interests: IoT; services computing; artificial intelligence
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
College of Intelligence and Computing, Tianjin University, Tianjin, China
Interests: service ecosystem; crowd intelligence
School of Information Engineering, China University of Geosciences, Beijing 100083, China
Interests: high-performance computing; parallel computing; artificial intelligence; distributed machine learning
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

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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
Viewed by 900
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
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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 - 03 Feb 2023
Cited by 1 | Viewed by 1530
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
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