AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Zhang, Z.; Wang, J.; Chen, J.; Fu, H.; Tong, Z.; Jiang, C.
Diffusion-Based Reinforcement Learning for Cooperative Offloading and Resource Allocation in Multi-UAV Assisted Edge-Enabled Metaverse Journal Article
In: IEEE Transactions on Vehicular Technology, 2025, ISSN: 00189545 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Content creation, Content services, Contrastive Learning, Decision making, Deep learning, Deep reinforcement learning, Diffusion Model, Global industry, Helicopter services, Markov processes, Metaverse, Metaverses, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Typical application, Unmanned aerial vehicle, Unmanned aerial vehicle (UAV), Unmanned aerial vehicles (UAV)
@article{zhang_diffusion-based_2025,
title = {Diffusion-Based Reinforcement Learning for Cooperative Offloading and Resource Allocation in Multi-UAV Assisted Edge-Enabled Metaverse},
author = {Z. Zhang and J. Wang and J. Chen and H. Fu and Z. Tong and C. Jiang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219108203&doi=10.1109%2fTVT.2025.3544879&partnerID=40&md5=fdbe1554f6cf7d47d4bbbb73b4b0d487},
doi = {10.1109/TVT.2025.3544879},
issn = {00189545 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Vehicular Technology},
abstract = {As one of the typical applications of 6G, the metaverse, with its superior immersion and diversified services, has garnered widespread attention from both the global industry and academia. Simultaneously, the emergence of AI-generated content (AIGC), exemplified by ChatGPT, has revolutionized the mean of content creation in the metaverse. Providing meataverse users with diversified AIGC services anytime and anywhere to meet the demand for immersive and blended virtual-real experiences in the physical world has become a major challenge in the development of the metaverse. Considering the flexibility and mobility of unmanned aerial vehicles (UAVs), we innovatively incorporate multiple UAVs as one of the AIGC service providers and construct a multi-UAV assisted edge-enabled metaverse system in the context of AIGC-as-a-Service (AaaS) scenario. To solve the complex resource management and allocation problem in the aforementioned system, we formulate it as a Markov decision process (MDP) and propose utilizing the generative capabilities of the diffusion model in combination with the robust decision-making abilities of reinforcement learning to tackle these issues. In order to substantiate the efficacy of the proposed diffusion-based reinforcement learning framework, we propose a novel diffusion-based soft actor-critic algorithm for metaverse (Meta-DSAC). Subsequently, a series of experiments are executed and the simulation results empirically validate the proposed algorithm's comparative advantages of the ability to provide stable and substantial long-term rewards, as well as the enhanced capacity to model complex environment. © 2025 IEEE.},
keywords = {Aerial vehicle, Content creation, Content services, Contrastive Learning, Decision making, Deep learning, Deep reinforcement learning, Diffusion Model, Global industry, Helicopter services, Markov processes, Metaverse, Metaverses, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Typical application, Unmanned aerial vehicle, Unmanned aerial vehicle (UAV), Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
As one of the typical applications of 6G, the metaverse, with its superior immersion and diversified services, has garnered widespread attention from both the global industry and academia. Simultaneously, the emergence of AI-generated content (AIGC), exemplified by ChatGPT, has revolutionized the mean of content creation in the metaverse. Providing meataverse users with diversified AIGC services anytime and anywhere to meet the demand for immersive and blended virtual-real experiences in the physical world has become a major challenge in the development of the metaverse. Considering the flexibility and mobility of unmanned aerial vehicles (UAVs), we innovatively incorporate multiple UAVs as one of the AIGC service providers and construct a multi-UAV assisted edge-enabled metaverse system in the context of AIGC-as-a-Service (AaaS) scenario. To solve the complex resource management and allocation problem in the aforementioned system, we formulate it as a Markov decision process (MDP) and propose utilizing the generative capabilities of the diffusion model in combination with the robust decision-making abilities of reinforcement learning to tackle these issues. In order to substantiate the efficacy of the proposed diffusion-based reinforcement learning framework, we propose a novel diffusion-based soft actor-critic algorithm for metaverse (Meta-DSAC). Subsequently, a series of experiments are executed and the simulation results empirically validate the proposed algorithm's comparative advantages of the ability to provide stable and substantial long-term rewards, as well as the enhanced capacity to model complex environment. © 2025 IEEE.
2024
Liew, Z. Q.; Xu, M.; Lim, W. Y. Bryan; Niyato, D.; Kim, D. I.
AI-Generated Bidding for Immersive AIGC Services in Mobile Edge-Empowered Metaverse Proceedings Article
In: Int. Conf. Inf. Networking, pp. 305–309, IEEE Computer Society, 2024, ISBN: 19767684 (ISSN); 979-835033094-6 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence generated bid, Artificial intelligence generated content, Bidding mechanism, Bidding models, Budget constraint, Budget control, Budget-constraint bidding, Constrained optimization, Content services, Immersive, Learning systems, Metaverses, Mobile edge computing, Reinforcement Learning, Semantics, Virtual tour
@inproceedings{liew_ai-generated_2024,
title = {AI-Generated Bidding for Immersive AIGC Services in Mobile Edge-Empowered Metaverse},
author = {Z. Q. Liew and M. Xu and W. Y. Bryan Lim and D. Niyato and D. I. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198324990&doi=10.1109%2fICOIN59985.2024.10572159&partnerID=40&md5=271f5c45e8e95f01b42acaee89599bd5},
doi = {10.1109/ICOIN59985.2024.10572159},
isbn = {19767684 (ISSN); 979-835033094-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Int. Conf. Inf. Networking},
pages = {305–309},
publisher = {IEEE Computer Society},
abstract = {Recent advancements in Artificial Intelligence Generated Content (AIGC) provide personalized and immersive content generation services for applications such as interactive advertisements, virtual tours, and metaverse. With the use of mobile edge computing (MEC), buyers can bid for the AIGC service to enhance their user experience in real-time. However, designing strategies to optimize the quality of the services won can be challenging for budget-constrained buyers. The performance of classical bidding mechanisms is limited by the fixed rules in the strategies. To this end, we propose AI-generated bidding (AIGB) to optimize the bidding strategies for AIGC. AIGB model uses reinforcement learning model to generate bids for the services by learning from the historical data and environment states such as remaining budget, budget consumption rate, and quality of the won services. To obtain quality AIGC service, we propose a semantic aware reward function for the AIGB model. The proposed model is tested with a real-world dataset and experiments show that our model outperforms the classical bidding mechanism in terms of the number of services won and the similarity score. © 2024 IEEE.},
keywords = {Artificial intelligence generated bid, Artificial intelligence generated content, Bidding mechanism, Bidding models, Budget constraint, Budget control, Budget-constraint bidding, Constrained optimization, Content services, Immersive, Learning systems, Metaverses, Mobile edge computing, Reinforcement Learning, Semantics, Virtual tour},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advancements in Artificial Intelligence Generated Content (AIGC) provide personalized and immersive content generation services for applications such as interactive advertisements, virtual tours, and metaverse. With the use of mobile edge computing (MEC), buyers can bid for the AIGC service to enhance their user experience in real-time. However, designing strategies to optimize the quality of the services won can be challenging for budget-constrained buyers. The performance of classical bidding mechanisms is limited by the fixed rules in the strategies. To this end, we propose AI-generated bidding (AIGB) to optimize the bidding strategies for AIGC. AIGB model uses reinforcement learning model to generate bids for the services by learning from the historical data and environment states such as remaining budget, budget consumption rate, and quality of the won services. To obtain quality AIGC service, we propose a semantic aware reward function for the AIGB model. The proposed model is tested with a real-world dataset and experiments show that our model outperforms the classical bidding mechanism in terms of the number of services won and the similarity score. © 2024 IEEE.
You, F.; Du, H.; Kang, J.; Ni, W.; Niyato, D.; Jamalipour, A.
Generative AI-aided Reinforcement Learning for Computation Offloading and Privacy Protection in VR-based Multi-Access Edge Computing Proceedings Article
In: Proc. - IEEE Smart World Congr., SWC - IEEE Ubiquitous Intell. Comput., Auton. Trusted Comput., Digit. Twin, Metaverse, Priv. Comput. Data Secur., Scalable Comput. Commun., pp. 2209–2214, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833152086-1 (ISBN).
Abstract | Links | BibTeX | Tags: Computation offloading, Content services, Differential privacy, Diffusion Model, Edge computing, Generative adversarial networks, Generative diffusion model, generative diffusion models, Inverse problems, Multi-access edge computing, Multiaccess, Policy optimization, Proximal policy optimization, Reinforcement Learning, User privacy, Virtual environments, Virtual Reality
@inproceedings{you_generative_2024,
title = {Generative AI-aided Reinforcement Learning for Computation Offloading and Privacy Protection in VR-based Multi-Access Edge Computing},
author = {F. You and H. Du and J. Kang and W. Ni and D. Niyato and A. Jamalipour},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002235341&doi=10.1109%2fSWC62898.2024.00337&partnerID=40&md5=301c91c0b737c401d54e154e13dc8d47},
doi = {10.1109/SWC62898.2024.00337},
isbn = {979-833152086-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Smart World Congr., SWC - IEEE Ubiquitous Intell. Comput., Auton. Trusted Comput., Digit. Twin, Metaverse, Priv. Comput. Data Secur., Scalable Comput. Commun.},
pages = {2209–2214},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a Data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users' privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users' privacy within the PPO centralized management framework. © 2024 IEEE.},
keywords = {Computation offloading, Content services, Differential privacy, Diffusion Model, Edge computing, Generative adversarial networks, Generative diffusion model, generative diffusion models, Inverse problems, Multi-access edge computing, Multiaccess, Policy optimization, Proximal policy optimization, Reinforcement Learning, User privacy, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a Data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users' privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users' privacy within the PPO centralized management framework. © 2024 IEEE.
Ling, T.; Yanan, L.; Lei, Z.; Shuzhi, J.; Lixin, H.; Xiaoqun, Y.
Modeling the Competitive Content Service Market from the Perspective of Consumer Preferences: A Game Theory Approach Proceedings Article
In: Int. Conf. Comput. Artif. Intell. Technol., CAIT, pp. 326–332, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833153089-1 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence-generated content, consumer preference model, Consumer preference modeling, Content service market, Content services, Duopoly game, High quality, Service industry, Service markets, Service provider, Service Quality, Social welfare
@inproceedings{ling_modeling_2024,
title = {Modeling the Competitive Content Service Market from the Perspective of Consumer Preferences: A Game Theory Approach},
author = {T. Ling and L. Yanan and Z. Lei and J. Shuzhi and H. Lixin and Y. Xiaoqun},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004661601&doi=10.1109%2fCAIT64506.2024.10963074&partnerID=40&md5=005a1cd46af2b7e6613fc521a4ed1c18},
doi = {10.1109/CAIT64506.2024.10963074},
isbn = {979-833153089-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Int. Conf. Comput. Artif. Intell. Technol., CAIT},
pages = {326–332},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Deploying interaction-intensive AI-generated content (AIGC) services on mobile edge networks enables mobile AIGC to deliver personalized, high-quality content efficiently and cost-effectively. These services are designed to automatically generate content based on user inputs or requirements, positioning mobile AIGC as a promising solution for content creation in immersive and dynamic Metaverse environments. However, the current implementation of edge devices as AIGC Service Providers (ASPs) suffers from a lack of incentives, which impedes the sustainable delivery of high-quality edge AIGC services. This paper tries to design an incentive strategy by investigating the competition relationship among AIGC service providers (ASPs) as a Duopoly Game, which mathematically describes the competition among ASPs based on their service quality, cost, and prices. In this content service market, social planners issue policies to improve social welfare while providers maximize their profits according to the consumption preference of users. This consumption preference of consumers is described as consumer preference model to capture consumers' demands for different QoS services when different content service providers serve them simultaneously. The simulation shows that 1) competition among providers will produce more differences on content service quality than social regulation will bring; 2) more social welfare may be engendered by the competition among different ASPs. © 2024 IEEE.},
keywords = {Artificial intelligence-generated content, consumer preference model, Consumer preference modeling, Content service market, Content services, Duopoly game, High quality, Service industry, Service markets, Service provider, Service Quality, Social welfare},
pubstate = {published},
tppubtype = {inproceedings}
}
Deploying interaction-intensive AI-generated content (AIGC) services on mobile edge networks enables mobile AIGC to deliver personalized, high-quality content efficiently and cost-effectively. These services are designed to automatically generate content based on user inputs or requirements, positioning mobile AIGC as a promising solution for content creation in immersive and dynamic Metaverse environments. However, the current implementation of edge devices as AIGC Service Providers (ASPs) suffers from a lack of incentives, which impedes the sustainable delivery of high-quality edge AIGC services. This paper tries to design an incentive strategy by investigating the competition relationship among AIGC service providers (ASPs) as a Duopoly Game, which mathematically describes the competition among ASPs based on their service quality, cost, and prices. In this content service market, social planners issue policies to improve social welfare while providers maximize their profits according to the consumption preference of users. This consumption preference of consumers is described as consumer preference model to capture consumers' demands for different QoS services when different content service providers serve them simultaneously. The simulation shows that 1) competition among providers will produce more differences on content service quality than social regulation will bring; 2) more social welfare may be engendered by the competition among different ASPs. © 2024 IEEE.
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.
Lee, S.; Park, W.; Lee, K.
Building Knowledge Base of 3D Object Assets Using Multimodal LLM AI Model Proceedings Article
In: Int. Conf. ICT Convergence, pp. 416–418, IEEE Computer Society, 2024, ISBN: 21621233 (ISSN); 979-835036463-7 (ISBN).
Abstract | Links | BibTeX | Tags: 3D object, Asset management, Content services, Exponentials, Information Management, Knowledge Base, Language Model, Large language model, LLM, Multi-modal, Multi-Modal AI, Reusability, Visual effects, XR
@inproceedings{lee_building_2024,
title = {Building Knowledge Base of 3D Object Assets Using Multimodal LLM AI Model},
author = {S. Lee and W. Park and K. Lee},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217636269&doi=10.1109%2fICTC62082.2024.10827434&partnerID=40&md5=581ee8ca50eb3dae15dc9675971cf428},
doi = {10.1109/ICTC62082.2024.10827434},
isbn = {21621233 (ISSN); 979-835036463-7 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Int. Conf. ICT Convergence},
pages = {416–418},
publisher = {IEEE Computer Society},
abstract = {The proliferation of various XR (eXtended Reality) services and the increasing incorporation of visual effects into existing content services have led to an exponential rise in the demand for 3D object assets. This paper describes an LLM (Large Language Model)-based multimodal AI model pipeline that can be applied to a generative AI model for creating new 3D objects or restructuring the asset management system to enhance the reusability of existing 3D objects. By leveraging a multimodal AI model, we derived descriptive text for assets such as 3D object, 2D image at a human-perceptible level, rather than mere data, and subsequently used an LLM to generate knowledge triplets for constructing an asset knowledge base. The applicability of this pipeline was verified using actual 3D objects from a content production company. Future work will focus on improving the quality of the generated knowledge triplets themselves by training the multimodal AI model with real-world content usage assets. © 2024 IEEE.},
keywords = {3D object, Asset management, Content services, Exponentials, Information Management, Knowledge Base, Language Model, Large language model, LLM, Multi-modal, Multi-Modal AI, Reusability, Visual effects, XR},
pubstate = {published},
tppubtype = {inproceedings}
}
The proliferation of various XR (eXtended Reality) services and the increasing incorporation of visual effects into existing content services have led to an exponential rise in the demand for 3D object assets. This paper describes an LLM (Large Language Model)-based multimodal AI model pipeline that can be applied to a generative AI model for creating new 3D objects or restructuring the asset management system to enhance the reusability of existing 3D objects. By leveraging a multimodal AI model, we derived descriptive text for assets such as 3D object, 2D image at a human-perceptible level, rather than mere data, and subsequently used an LLM to generate knowledge triplets for constructing an asset knowledge base. The applicability of this pipeline was verified using actual 3D objects from a content production company. Future work will focus on improving the quality of the generated knowledge triplets themselves by training the multimodal AI model with real-world content usage assets. © 2024 IEEE.
2023
Lin, Y.; Gao, Z.; Du, H.; Niyato, D.
Blockchain-Aided AI-Generated Content Services: Stackelberg Game-Based Content Caching Approach Proceedings Article
In: C., Ardagna; B., Benatallah; H., Bian; C.K., Chang; R.N., Chang; J., Fan; G.C., Fox; Z., Jin; X., Liu; H., Ludwig; M., Sheng; J., Yang (Ed.): Proc. - IEEE Int. Conf. Web Serv., ICWS, pp. 186–195, Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835030485-5 (ISBN).
Abstract | Links | BibTeX | Tags: AI-generated content, Block-chain, Blockchain, Caching mechanism, Content caching, Content services, Digital contents, Game-Based, Metaverse, Metaverses, Shared virtual environments, Stackelberg Games, Virtual Reality
@inproceedings{lin_blockchain-aided_2023,
title = {Blockchain-Aided AI-Generated Content Services: Stackelberg Game-Based Content Caching Approach},
author = {Y. Lin and Z. Gao and H. Du and D. Niyato},
editor = {Ardagna C. and Benatallah B. and Bian H. and Chang C.K. and Chang R.N. and Fan J. and Fox G.C. and Jin Z. and Liu X. and Ludwig H. and Sheng M. and Yang J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173901801&doi=10.1109%2fICWS60048.2023.00038&partnerID=40&md5=3a323f44b98bd9e7a86a22523efcd488},
doi = {10.1109/ICWS60048.2023.00038},
isbn = {979-835030485-5 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {Proc. - IEEE Int. Conf. Web Serv., ICWS},
pages = {186–195},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {AI-Generated Content (AIGC) services are essential in developing the Metaverse, providing various digital content to build shared virtual environments. AIGC services can offer personalized content with user assistance, making the Metaverse more human-centric. However, it is difficult for participants to exchange data and construct trust among unknown Metaverse participants. To address the above challenge, we propose an integration of blockchain and AIGC to construct decentralized trust among participants. We design a smart contract-based verification mechanism to prevent random outcomes of AIGC services and guarantee the authenticity of digital contents. Given the quality of digital contents provided by the previous mechanisms, we then utilize them as metrics to establish a Stackelberg game-based content caching mechanism to maximize Metaverse participants' profits. Simulation results show that the proposed caching mechanism can improve utility by 30% compared to other mechanisms. © 2023 IEEE.},
keywords = {AI-generated content, Block-chain, Blockchain, Caching mechanism, Content caching, Content services, Digital contents, Game-Based, Metaverse, Metaverses, Shared virtual environments, Stackelberg Games, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
AI-Generated Content (AIGC) services are essential in developing the Metaverse, providing various digital content to build shared virtual environments. AIGC services can offer personalized content with user assistance, making the Metaverse more human-centric. However, it is difficult for participants to exchange data and construct trust among unknown Metaverse participants. To address the above challenge, we propose an integration of blockchain and AIGC to construct decentralized trust among participants. We design a smart contract-based verification mechanism to prevent random outcomes of AIGC services and guarantee the authenticity of digital contents. Given the quality of digital contents provided by the previous mechanisms, we then utilize them as metrics to establish a Stackelberg game-based content caching mechanism to maximize Metaverse participants' profits. Simulation results show that the proposed caching mechanism can improve utility by 30% compared to other mechanisms. © 2023 IEEE.