A Dynamic Resource Allocation Framework for Synchronizing Metaverse with IoT Service and Data
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Abstract (arXiv)
Spurred by the severe restrictions on mobility due to the COVID-19 pandemic, there is currently intense interest in developing the Metaverse, to offer virtual services/business online. A key enabler of such virtual service is the digital twin, i.e., a digital replication of real-world entities in the Metaverse, e.g., city twin, avatars, etc. The real-world data collected by IoT devices and sensors are key for synchronizing the two worlds. In this paper, we consider the scenario in which a group of IoT devices are employed by the Metaverse platform to collect such data on behalf of virtual service providers (VSPs). Device owners, who are self-interested, dynamically select a VSP to maximize rewards. We adopt hybrid evolutionary dynamics, in which heterogeneous device owner populations can employ different revision protocols to update their strategies. Extensive simulations demonstrate that a hybrid protocol can lead to evolutionary stable states.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Oct 2021.
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Citing papers per year: 2022: 3; 2023: 6; 2024: 1; 2025: 1.
11 papers.
- 2022Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Servicescited by 15
- 2022AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trendscited by 8
- 2022Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approachcited by 6
- 2023Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Frameworkcited by 3
- 2023A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collectioncited by 2
- 2023Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approachcited by 1
- 2023Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learningcited by 0
- 2023Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learningcited by 0
- 2023Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approachcited by 0
- 2024Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectivescited by 0
- 2025Research on environment perception and behavior prediction of intelligent UAV based on semantic communicationcited by 0
- 2025Research on environment perception and behavior prediction of intelligent UAV based on semantic communication8 Jan 2025
- 2024Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectives7 May 2024
- 2023Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach11 Dec 2023
- 2023Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach15 Aug 2023
- 2023A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collection29 May 2023
- 2023Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning18 Mar 2023
- 2023Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning27 Feb 2023
- 2023Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework22 Feb 2023
- 2022Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach27 Sep 2022
- 2022AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends23 Aug 2022
- 2022Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services10 Aug 2022