WITHDRAWN: Auction, Learning and Bargaining based Control Scheme for Edge assisted Metaverse System

Abstract

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 socialize 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, which adopts three control approaches. First, McAfee double auction is applied to handle the collected data trading between service providers and IoT devices. Second, Q-learning algorithm is adopted to decide the power level of each IoT device. Third, the status quo proportional bargaining solution is used to provide a proper resource allocation problem for the devices’ wireless communications. Based on the reciprocal combination of auction, learning and bargaining algorithms, we explore the sequential interaction of system agents, and 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.