AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
How to
Here you can find the complete list of our publications.
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.
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.
2024
Lin, Y.; Gao, Z.; Du, H.; Niyato, D.; Kang, J.; Xiong, Z.; Zheng, Z.
Blockchain-Based Efficient and Trustworthy AIGC Services in Metaverse Journal Article
In: IEEE Transactions on Services Computing, vol. 17, no. 5, pp. 2067–2079, 2024, ISSN: 19391374 (ISSN).
Abstract | Links | BibTeX | Tags: AI-generated content, Block-chain, Blockchain, Computational modelling, Content services, Data Mining, Digital contents, Information Management, Metaverse, Metaverses, Resource management, Semantic communication, Semantics, Virtual Reality
@article{lin_blockchain-based_2024,
title = {Blockchain-Based Efficient and Trustworthy AIGC Services in Metaverse},
author = {Y. Lin and Z. Gao and H. Du and D. Niyato and J. Kang and Z. Xiong and Z. Zheng},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189177655&doi=10.1109%2fTSC.2024.3382958&partnerID=40&md5=5e3e80fbc88a49293b892acd762af3e9},
doi = {10.1109/TSC.2024.3382958},
issn = {19391374 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on Services Computing},
volume = {17},
number = {5},
pages = {2067–2079},
abstract = {AI-Generated Content (AIGC) services are essential in developing the Metaverse, providing various digital content to build shared virtual environments. The services can also offer personalized content with user assistance, making the Metaverse more human-centric. However, user-assisted content creation requires significant communication resources to exchange data and construct trust among unknown Metaverse participants, which challenges the traditional centralized communication paradigm. To address the above challenge, we integrate blockchain with semantic communication to establish decentralized trust among participants, reducing communication overhead and improving trustworthiness for AIGC services in Metaverse. To solve the out-of-distribution issue in data provided by users, we utilize the invariant risk minimization method to extract invariant semantic information across multiple virtual environments. To guarantee trustworthiness of digital contents, we also design a smart contract-based verification mechanism to prevent random outcomes of AIGC services. We utilize semantic information and quality of digital contents provided by the above mechanisms as metrics to develop a Stackelberg game-based content caching mechanism, which can maximize the profits of Metaverse participants. Simulation results show that the proposed semantic extraction and caching mechanism can improve accuracy by almost 15% and utility by 30% compared to other mechanisms. © 2008-2012 IEEE.},
keywords = {AI-generated content, Block-chain, Blockchain, Computational modelling, Content services, Data Mining, Digital contents, Information Management, Metaverse, Metaverses, Resource management, Semantic communication, Semantics, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
AI-Generated Content (AIGC) services are essential in developing the Metaverse, providing various digital content to build shared virtual environments. The services can also offer personalized content with user assistance, making the Metaverse more human-centric. However, user-assisted content creation requires significant communication resources to exchange data and construct trust among unknown Metaverse participants, which challenges the traditional centralized communication paradigm. To address the above challenge, we integrate blockchain with semantic communication to establish decentralized trust among participants, reducing communication overhead and improving trustworthiness for AIGC services in Metaverse. To solve the out-of-distribution issue in data provided by users, we utilize the invariant risk minimization method to extract invariant semantic information across multiple virtual environments. To guarantee trustworthiness of digital contents, we also design a smart contract-based verification mechanism to prevent random outcomes of AIGC services. We utilize semantic information and quality of digital contents provided by the above mechanisms as metrics to develop a Stackelberg game-based content caching mechanism, which can maximize the profits of Metaverse participants. Simulation results show that the proposed semantic extraction and caching mechanism can improve accuracy by almost 15% and utility by 30% compared to other mechanisms. © 2008-2012 IEEE.
2023
Xu, M.; Niyato, D.; Chen, J.; Zhang, H.; Kang, J.; Xiong, Z.; Mao, S.; Han, Z.
Generative AI-Empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses Journal Article
In: IEEE Journal on Selected Topics in Signal Processing, vol. 17, no. 5, pp. 1064–1079, 2023, ISSN: 19324553 (ISSN).
Abstract | Links | BibTeX | Tags: Auction theory, Autonomous driving, Autonomous Vehicles, Computation theory, Computational modelling, generative artificial intelligence, Job analysis, Metaverse, Metaverses, Mixed reality, Online systems, Roadside units, Task analysis
@article{xu_generative_2023,
title = {Generative AI-Empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses},
author = {M. Xu and D. Niyato and J. Chen and H. Zhang and J. Kang and Z. Xiong and S. Mao and Z. Han},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164670036&doi=10.1109%2fJSTSP.2023.3293650&partnerID=40&md5=f28390de62f0f44c38a902e6c32dcd16},
doi = {10.1109/JSTSP.2023.3293650},
issn = {19324553 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {IEEE Journal on Selected Topics in Signal Processing},
volume = {17},
number = {5},
pages = {1064–1079},
abstract = {In the vehicular mixed reality (MR) Metaverse, the discrepancy between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSUs), and virtual simulators can maintain the vehicular MR Metaverse via simulations for sharing data and making driving decisions collaboratively. However, it is challenging and costly to enable large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data via simulations for improving driving safety and traffic control efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and real-world data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets for improved robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs on providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective. © 2007-2012 IEEE.},
keywords = {Auction theory, Autonomous driving, Autonomous Vehicles, Computation theory, Computational modelling, generative artificial intelligence, Job analysis, Metaverse, Metaverses, Mixed reality, Online systems, Roadside units, Task analysis},
pubstate = {published},
tppubtype = {article}
}
In the vehicular mixed reality (MR) Metaverse, the discrepancy between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSUs), and virtual simulators can maintain the vehicular MR Metaverse via simulations for sharing data and making driving decisions collaboratively. However, it is challenging and costly to enable large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data via simulations for improving driving safety and traffic control efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and real-world data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets for improved robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs on providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective. © 2007-2012 IEEE.