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User Connection and Resource Allocation Optimization in Blockchain Empowered Metaverse over 6G Wireless Communications

Published 8 Mar 2024 in cs.ET, cs.NI, and eess.SP | (2403.05116v2)

Abstract: The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters, and server computing frequency division. In the resource allocation phase, we optimize communication-computation resource distributions, including bandwidth, transmit power, and computing frequency. We introduce the trust-cost ratio (TCR), a pivotal measure combining trust scores from users' resources and server history with delay and energy costs. This balance ensures sustained user engagement and trust. The DASHF algorithm, central to our approach, encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a novel fractional programming technique from a recent IEEE JSAC paper [2]. The most challenging part of DASHF is to rewrite an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via carefully designed transformations, in order to be solved by SDR and the Hungarian algorithm. Extensive simulations validate the DASHF algorithm's efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.

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References (33)
  1. L. Qian and J. Zhao, “User association and resource allocation in large language model based mobile edge computing system over wireless communications,” accepted by IEEE Vehicular Technology Conference (VTC), 2024. [Online]. Available: https://arxiv.org/pdf/2310.17872.pdf
  2. J. Zhao, L. Qian, and W. Yu, “Human-centric resource allocation in the Metaverse over wireless communications,” IEEE Journal on Selected Areas in Communications (JSAC), vol. 42, no. 3, pp. 514–537, 2024.
  3. C. Wang, C. Yu, and Y. Li, “Toward understanding attention economy in Metaverse: A case study of NFT value,” IEEE Transactions on Computational Social Systems, 2022.
  4. W. Y. B. Lim, Z. Xiong, D. Niyato, X. Cao, C. Miao, S. Sun, and Q. Yang, “Realizing the Metaverse with edge intelligence: A match made in heaven,” IEEE Wireless Communications, 2022.
  5. M. Xu, W. C. Ng, W. Y. B. Lim, J. Kang, Z. Xiong, D. Niyato, Q. Yang, X. S. Shen, and C. Miao, “A full dive into realizing the edge-enabled metaverse: Visions, enabling technologies, and challenges,” IEEE Communications Surveys & Tutorials, 2022.
  6. M. Xu, D. Niyato, B. Wright, H. Zhang, J. Kang, Z. Xiong, S. Mao, and Z. Han, “Epvisa: Efficient auction design for real-time physical-virtual synchronization in the human-centric metaverse,” IEEE Journal on Selected Areas in Communications, 2023.
  7. D. Wu, Z. Yang, P. Zhang, R. Wang, B. Yang, and X. Ma, “Virtual-reality inter-promotion technology for Metaverse: A survey,” IEEE Internet of Things Journal, 2023.
  8. Y. Wang, Z. Su, N. Zhang, R. Xing, D. Liu, T. H. Luan, and X. Shen, “A survey on Metaverse: Fundamentals, security, and privacy,” IEEE Communications Surveys & Tutorials, 2022.
  9. K. Christodoulou, L. Katelaris, M. Themistocleous, P. Christodoulou, and E. Iosif, “NFTs and the Metaverse revolution: Research perspectives and open challenges,” Blockchains and the Token Economy: Theory and Practice, pp. 139–178, 2022.
  10. D. Chalmers, C. Fisch, R. Matthews, W. Quinn, and J. Recker, “Beyond the bubble: Will NFTs and digital proof of ownership empower creative industry entrepreneurs?” Journal of Business Venturing Insights, vol. 17, p. e00309, 2022.
  11. R. Cheng, N. Wu, S. Chen, and B. Han, “Will Metaverse be NextG Internet? Vision, hype, and reality,” IEEE Network, vol. 36, no. 5, pp. 197–204, 2022.
  12. S. Aggarwal, R. Chaudhary, G. S. Aujla, N. Kumar, K.-K. R. Choo, and A. Y. Zomaya, “Blockchain for smart communities: Applications, challenges and opportunities,” Journal of Network and Computer Applications, vol. 144, pp. 13–48, 2019.
  13. Y. Lu, “The blockchain: State-of-the-art and research challenges,” Journal of Industrial Information Integration, vol. 15, pp. 80–90, 2019.
  14. Q. Yang, Y. Zhao, H. Huang, Z. Xiong, J. Kang, and Z. Zheng, “Fusing blockchain and AI with Metaverse: A survey,” IEEE Open Journal of the Computer Society, vol. 3, pp. 122–136, 2022.
  15. Y. Huang, Y. J. Li, and Z. Cai, “Security and privacy in Metaverse: A comprehensive survey,” Big Data Mining and Analytics, vol. 6, no. 2, pp. 234–247, 2023.
  16. R. Di Pietro and S. Cresci, “Metaverse: Security and privacy issues,” in IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications.   IEEE, 2021, pp. 281–288.
  17. G. Kang, J. Koo, and Y.-G. Kim, “Security and privacy requirements for the Metaverse: A Metaverse applications perspective,” IEEE Communications Magazine, 2023.
  18. S. B. Far and A. I. Rad, “Applying digital twins in Metaverse: User interface, security and privacy challenges,” Journal of Metaverse, vol. 2, no. 1, pp. 8–15, 2022.
  19. J. Feng, F. R. Yu, Q. Pei, J. Du, and L. Zhu, “Joint optimization of radio and computational resources allocation in blockchain-enabled mobile edge computing systems,” IEEE Transactions on Wireless Communications, vol. 19, no. 6, pp. 4321–4334, 2020.
  20. Y. Dai, D. Xu, S. Maharjan, and Y. Zhang, “Joint computation offloading and user association in multi-task mobile edge computing,” IEEE Transactions on Vehicular Technology, vol. 67, no. 12, pp. 12 313–12 325, 2018.
  21. F. Guo, F. R. Yu, H. Zhang, H. Ji, M. Liu, and V. C. Leung, “Adaptive resource allocation in future wireless networks with blockchain and mobile edge computing,” IEEE Transactions on Wireless Communications, vol. 19, no. 3, pp. 1689–1703, 2019.
  22. W. Sun, J. Liu, Y. Yue, and P. Wang, “Joint resource allocation and incentive design for blockchain-based mobile edge computing,” IEEE Transactions on Wireless Communications, vol. 19, no. 9, pp. 6050–6064, 2020.
  23. H. Xu, W. Huang, Y. Zhou, D. Yang, M. Li, and Z. Han, “Edge computing resource allocation for unmanned aerial vehicle assisted mobile network with blockchain applications,” IEEE Transactions on Wireless Communications, vol. 20, no. 5, pp. 3107–3121, 2021.
  24. X. Jiang, F. R. Yu, T. Song, and V. C. Leung, “Intelligent resource allocation for video analytics in blockchain-enabled Internet of autonomous vehicles with edge computing,” IEEE Internet of Things Journal, vol. 9, no. 16, pp. 14 260–14 272, 2020.
  25. Z. Tu, H. Zhou, K. Li, H. Song, and Y. Yang, “A blockchain-based trust and reputation model with dynamic evaluation mechanism for IoT,” Computer Networks, vol. 218, p. 109404, 2022.
  26. Y. Liu, C. Zhang, Y. Yan, X. Zhou, Z. Tian, and J. Zhang, “A semi-centralized trust management model based on blockchain for data exchange in IoT system,” IEEE Transactions on Services Computing, vol. 16, no. 2, pp. 858–871, 2022.
  27. J. Xi, G. Xu, S. Zou, Y. Lu, G. Li, J. Xu, and R. Wang, “A blockchain dynamic sharding scheme based on hidden Markov model in collaborative IoT,” IEEE Internet of Things Journal, 2023.
  28. N. T. Hoa, B. D. Son, N. C. Luong, and D. Niyato, “Dynamic offloading for edge computing-assisted Metaverse systems,” IEEE Communications Letters, 2023.
  29. “Monkey Kingdom.” [Online]. Available: https://monkeykingdom.io/
  30. Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, “Energy efficient federated learning over wireless communication networks,” IEEE Transactions on Wireless Communications, vol. 20, no. 3, pp. 1935–1949, 2020.
  31. M. S. Al-Rakhami and M. Al-Mashari, “A blockchain-based trust model for the Internet of Things supply chain management,” Sensors, vol. 21, no. 5, p. 1759, 2021.
  32. D. Yang, G. Xue, X. Fang, and J. Tang, “Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones,” IEEE/ACM Transactions on Networking, vol. 24, no. 3, pp. 1732–1744, 2015.
  33. W. Dinkelbach, “On nonlinear fractional programming,” Management Science, vol. 13, no. 7, pp. 492–498, 1967.
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