Data trading, power control and resource allocation algorithms for metaverse platform

초록

Edge computing (EC) has emerged as a cost-effective platform to enhance the computing capability of hardware-constrained IoT devices. Recently, EC-assisted Metaverse system is regarded as the next-generation internet paradigm that allows humans to play, work, and socialise in an alternative virtual world. With the help of ubiquitous wireless connections and powerful EC technologies, the Metaverse system effectively manages the interactions among system agents. In this study, we present a new intelligent Metaverse control scheme based on the reciprocal combination of auction, learning and bargaining methods. Specifically, McAfee double auction is applied to handle the collected data trading between service providers and IoT devices. In addition, learning algorithm and bargaining solution are used to provide a proper resource allocation problem for the devices' wireless communications. To explore the sequential interaction of system agents, we jointly design an integrated control scheme to strike an appropriate Metaverse performance balance. According to the synergy effect, our hybrid protocol is a novel method in the EC-assisted Metaverse infrastructure. Finally, extensive simulations demonstrate that our approach can lead to achieve a mutually desirable solution with a good balance between efficiency and fairness comparing with the currently published Metaverse system control schemes.

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