AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
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You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Zhang, Z.; Wang, J.; Chen, J.; Fang, Z.; Jiang, C.; Han, Z.
A Priority-Aware AI-Generated Content Resource Allocation Method for Multi-UAV Aided Metaverse Proceedings Article
In: IEEE Wireless Commun. Networking Conf. WCNC, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 15253511 (ISSN); 979-835036836-9 (ISBN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, AI-generated content, AI-generated content (AIGC), Allocation methods, Content-resources, Diffusion Model, Drones, Metaverse, Metaverses, Priority-aware, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Target drones, Unmanned aerial vehicle, Unmanned aerial vehicle (UAV)
@inproceedings{zhang_priority-aware_2025,
title = {A Priority-Aware AI-Generated Content Resource Allocation Method for Multi-UAV Aided Metaverse},
author = {Z. Zhang and J. Wang and J. Chen and Z. Fang and C. Jiang and Z. Han},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006408540&doi=10.1109%2fWCNC61545.2025.10978443&partnerID=40&md5=69937c6fa9be1a038b28e7884dfe586b},
doi = {10.1109/WCNC61545.2025.10978443},
isbn = {15253511 (ISSN); 979-835036836-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IEEE Wireless Commun. Networking Conf. WCNC},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {With the advancement of large model technologies, AI -generated content is gradually emerging as a mainstream method for content creation. The metaverse, as a key application scenario for the next-generation communication technologies, heavily depends on advanced content generation technologies. Nevertheless, the diverse types of metaverse applications and their stringent real-time requirements constrain the full potential of AIGC technologies within this environment. In order to tackle with this problem, we construct a priority-aware multi-UAV aided metaverse system and formulate it as a Markov decision process (MDP). We propose a diffusion-based reinforcement learning algorithm to solve the resource allocation problem and demonstrate its superiority through enough comparison and ablation experiments. © 2025 IEEE.},
keywords = {Aerial vehicle, AI-generated content, AI-generated content (AIGC), Allocation methods, Content-resources, Diffusion Model, Drones, Metaverse, Metaverses, Priority-aware, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Target drones, Unmanned aerial vehicle, Unmanned aerial vehicle (UAV)},
pubstate = {published},
tppubtype = {inproceedings}
}
With the advancement of large model technologies, AI -generated content is gradually emerging as a mainstream method for content creation. The metaverse, as a key application scenario for the next-generation communication technologies, heavily depends on advanced content generation technologies. Nevertheless, the diverse types of metaverse applications and their stringent real-time requirements constrain the full potential of AIGC technologies within this environment. In order to tackle with this problem, we construct a priority-aware multi-UAV aided metaverse system and formulate it as a Markov decision process (MDP). We propose a diffusion-based reinforcement learning algorithm to solve the resource allocation problem and demonstrate its superiority through enough comparison and ablation experiments. © 2025 IEEE.
2024
Truong, V. T.; Le, H. D.; Le, L. B.
Trust-Free Blockchain Framework for AI-Generated Content Trading and Management in Metaverse Journal Article
In: IEEE Access, vol. 12, pp. 41815–41828, 2024, ISSN: 21693536 (ISSN).
Abstract | Links | BibTeX | Tags: AI-generated content, AI-generated content (AIGC), Artificial intelligence, Asset management, Assets management, Block-chain, Blockchain, Commerce, Content distribution networks, Cyber-attacks, Decentralised, Decentralized application, Digital asset management, Digital system, Generative AI, Metaverse, Metaverses, Plagiarism, Security, Trustless service, Virtual Reality
@article{truong_trust-free_2024,
title = {Trust-Free Blockchain Framework for AI-Generated Content Trading and Management in Metaverse},
author = {V. T. Truong and H. D. Le and L. B. Le},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188472793&doi=10.1109%2fACCESS.2024.3376509&partnerID=40&md5=301939c1faef0c5a7b56d9feadce27ee},
doi = {10.1109/ACCESS.2024.3376509},
issn = {21693536 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {41815–41828},
abstract = {The rapid development of the metaverse and generative Artificial Intelligence (GAI) has led to the emergence of AI-Generated Content (AIGC). Unlike real-world products, AIGCs are represented as digital files, thus vulnerable to plagiarism and leakage on the Internet. In addition, the trading of AIGCs in the virtual world is prone to various trust issues between the involved participants. For example, some customers may try to avoid the payment after receiving the desired AIGC products, or the content sellers refuse to grant the products after obtaining the license fee. Existing digital asset management (DAM) systems often rely on a trusted third-party authority to mitigate these issues. However, this might lead to centralization problems such as the single-point-of-failure (SPoF) when the third parties are under attacks or being malicious. In this paper, we propose MetaTrade, a blockchain-empowered DAM framework that is designed to tackle these urgent trust issues, offering secured AIGC trading and management in the trustless metaverse environment. MetaTrade eliminates the role of the trusted third party, without requiring trust assumptions among participants. Numerical results show that MetaTrade offers higher performance and lower trading cost compared to existing platforms, while security analysis reveals that the framework is resilient against plagiarism, SPoF, and trust-related attacks. To showcase the feasibility of the design, a decentralized application (DApp) has been built on top of MetaTrade as a marketplace for metaverse AIGCs. © 2013 IEEE.},
keywords = {AI-generated content, AI-generated content (AIGC), Artificial intelligence, Asset management, Assets management, Block-chain, Blockchain, Commerce, Content distribution networks, Cyber-attacks, Decentralised, Decentralized application, Digital asset management, Digital system, Generative AI, Metaverse, Metaverses, Plagiarism, Security, Trustless service, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The rapid development of the metaverse and generative Artificial Intelligence (GAI) has led to the emergence of AI-Generated Content (AIGC). Unlike real-world products, AIGCs are represented as digital files, thus vulnerable to plagiarism and leakage on the Internet. In addition, the trading of AIGCs in the virtual world is prone to various trust issues between the involved participants. For example, some customers may try to avoid the payment after receiving the desired AIGC products, or the content sellers refuse to grant the products after obtaining the license fee. Existing digital asset management (DAM) systems often rely on a trusted third-party authority to mitigate these issues. However, this might lead to centralization problems such as the single-point-of-failure (SPoF) when the third parties are under attacks or being malicious. In this paper, we propose MetaTrade, a blockchain-empowered DAM framework that is designed to tackle these urgent trust issues, offering secured AIGC trading and management in the trustless metaverse environment. MetaTrade eliminates the role of the trusted third party, without requiring trust assumptions among participants. Numerical results show that MetaTrade offers higher performance and lower trading cost compared to existing platforms, while security analysis reveals that the framework is resilient against plagiarism, SPoF, and trust-related attacks. To showcase the feasibility of the design, a decentralized application (DApp) has been built on top of MetaTrade as a marketplace for metaverse AIGCs. © 2013 IEEE.