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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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Blog Post number 1
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portfolio
SocialMeta 2023 Best Paper Award
Our paper Learning-based Incentive Mechanism for Task Freshness-aware Vehicular Twin Migration won the best paper award
ECNCT 2023 Best Paper Award
Our paper Coalition Game for Reliable Vehicle Twin Migration in Vehicular Metaverses won the best paper award
publications
Freshness-aware Incentive Mechanism for Mobile AI-Generated Content (AIGC) Networks
Published in ICCC-2023 IEEE/CIC International Conference on Communications in China, 2023
In this paper, we first utilize Age of Information (AoI) as a well-accepted data-freshness metric to quantify data freshness for AIGC fine-tuning. Then, we propose an AoI-based contract theory model to incentivize the contribution of fresh data among UAVs. Moreover, we design the optimal contract that is feasible to maximize the expected utility of the base station that is responsible for dispatching UAVs to collaboratively perform AIGC tasks.
Recommended citation: Jinbo Wen, Jiawen Kang, Minrui Xu, Hongyang Du, Zehui Xiong, Yang Zhang, Dusit Niyato, "Freshness-aware incentive mechanism for mobile AI-Generated Content (AIGC) networks," ICCC-2023 IEEE/CIC International Conference on Communications in China, Sep 2023. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10233667
Blockchain-empowered Federated Learning for Healthcare Metaverses User-centric Incentive Mechanism with Optimal Data Freshness
Published in IEEE Transactions on Cognitive Communications and Networking, 2023
In this paper, we first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. To further improve the privacy protection of healthcare metaverses, a cross-chain empowered FL framework is utilized to enhance sensing data security. This framework utilizes a hierarchical cross-chain architecture with a main chain and multiple subchains to perform decentralized, privacy-preserving, and secure data training in both virtual and physical spaces. Moreover, we utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing in a user-centric manner. This model exploits PT to better capture the subjective utility of the service provider.
Recommended citation: Jiawen Kang, Jinbo Wen, Dongdong Ye, Bingkun Lai, Tianhao Wu, Zehui Xiong, Jiangtian Nie, Dusit Niyato, Yang Zhang, and Shengli Xie, "Blockchain-empowered federated learning for healthcare Metaverses: User-centric incentive mechanism with optimal data freshness," IEEE Transactions on Cognitive Communications and Networking, pp. 348-362, Feb 2024. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10254627
Task Freshness-aware Incentive Mechanism for Vehicle Twin Migration in Vehicular Metaverses
Published in MetaCom-2023 IEEE International Conference on Metaverse Computing, Networking and Applications, 2023
In this paper, we design an efficient incentive mechanism framework for VT migrations. We first propose a novel metric named Age of Migration Task (AoMT) to quantify the task freshness of the VT migration. AoMT measures the time elapsed from the first collected sensing data of the freshest avatar migration task to the last successfully processed data at the next RSU. To incentivize the contribution of bandwidth resources among the next RSUs, we propose an AoMT-based contract model, where the optimal contract is derived to maximize the expected utility of the RSU that provides metaverse services.
Recommended citation: Jinbo Wen, Jiawen Kang, Zehui Xiong, Yang Zhang, Hongyang Du, Yutao Jiao, Dusit Niyato, "Task freshness-aware incentive mechanism for vehicle twin migration in vehicular metaverses," MetaCom-2023 IEEE International Conference on Metaverse Computing, Networking and Applications, Oct 2023. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10271832
Optimizing Information Propagation for Blockchain-empowered Mobile AIGC: A Graph Attention Network Approach
Published in IWCMC 2024-The 20th International Wireless Communications & Mobile Computing Conference, 2024
In this paper, we design a Graph Attention Network (GAT)-based information propagation optimization framework for blockchain-empowered mobile AIGC.
Recommended citation: Jiana Liao, Jinbo Wen, Jiawen Kang, Yang Zhang, Jianbo Du, Qihao Li, Weiting Zhang, Dong Yang, "Optimizing Information Propagation for Blockchain-empowered Mobile AIGC: A Graph Attention Network Approach," IWCMC 2024-The 20th International Wireless Communications & Mobile Computing Conference, arXiv preprint arXiv:2404.04937, 2024. https://arxiv.org/pdf/2404.04937
From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks
Published in IEEE Internet of Things Magazine, 2024
In this article, we present the concept of GIoT and conduct an exploration of its potential prospects.
Recommended citation: Jinbo Wen, Jiangtian Nie, Jiawen Kang, Dusit Niyato, Hongyang Du, Yang Zhang, and Mohsen Guizani, "From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks," IEEE Internet of Things Magazine, pp. 30-37, May 2024. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10517486
submitted
Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0
Published:
Abstract: Web 3.0 is recognized as a pioneering paradigm that empowers users to securely oversee data without reliance on a centralized authority. Blockchains, as a core technology to realize Web 3.0, can facilitate decentralized and transparent data management. Nevertheless, the evolution of blockchain-enabled Web 3.0 is still in its nascent phase, grappling with challenges such as ensuring efficiency and reliability to enhance block propagation performance. In this paper, we design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0. We first innovatively apply a data-freshness metric called age of block to measure block propagation efficiency in public blockchains. To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model, including the local and recommended opinions to calculate the miner reputation value. Moreover, considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory. Numerical results demonstrate that the proposed scheme exhibits the most outstanding block propagation efficiency and reliability compared with traditional routing mechanisms.
Recommended citation: Jiana Liao, Jinbo Wen, Jiawen Kang, Changyan Yi, Yang Zhang, Yutao Jiao, Dusit Niyato, Dong In Kim, and Shengli Xie, "Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0," arXiv preprint arXiv:2403.13237, 2024. https://arxiv.org/pdf/2403.13237
Generative AI for Low-Carbon Artificial Intelligence of Things
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Abstract: By integrating Artificial Intelligence (AI) with the Internet of Things (IoT), Artificial Intelligence of Things (AIoT) has revolutionized many fields. However, AIoT is facing the challenges of energy consumption and carbon emissions due to the continuous advancement of mobile technology. Fortunately, Generative AI (GAI) holds immense potential to reduce carbon emissions of AIoT due to its excellent reasoning and generation capabilities. In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. Specifically, we first study the main impacts that cause carbon emissions in AIoT, and then introduce GAI techniques and their relations to carbon emissions. We then explore the application prospects of GAI in low-carbon AIoT, focusing on how GAI can reduce carbon emissions of network components. Subsequently, we propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules to generate more accurate and reliable optimization problems. Furthermore, we utilize Generative Diffusion Models (GDMs) to identify optimal strategies for carbon emission reduction. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we insightfully provide open research directions for low-carbon AIoT.
Recommended citation: Jinbo Wen, Ruichen Zhang, Dusit Niyato, Jiawen Kang, Hongyang Du, Yang Zhang, Zhu Han, "Generative AI for Low-Carbon Artificial Intelligence of Things," arXiv preprint arXiv:2404.18077, 2024. https://arxiv.org/pdf/2404.18077
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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