计算机科学
激励
边缘计算
收入
云计算
投标
双重拍卖
分布式计算
共同价值拍卖
微观经济学
操作系统
经济
会计
作者
Nguyen Cong Luong,Thien Huynh‐The,Van‐Dinh Nguyen,Derrick Wing Kwan Ng,Symeon Chatzinotas,Dusit Niyato,Quoc‐Viet Pham
出处
期刊:IEEE Network
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-1
标识
DOI:10.1109/mnet.2023.3334285
摘要
Edge computing as a disruptive solution addresses the emerging requirement of low latency data processing in Metaverse. However, edge computing resources are provided by computing service providers (ECPs) and thus it is essential to design appealing incentive mechanisms for the provision of limited resources. Meanwhile, deep learning (DL)-based auction has recently been proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality (IR) and incentive compatibility (IC). Therefore, in this work, we introduce the design of the DL-based auction for the computing resource allocation for the edge computing-assisted Metaverse. Furthermore, a semantic communication (SemCom) technique is exploited that helps to reduce the offloading data and offloading cost for the VSPs. Particularly, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present edge computing, incentive mechanisms, and SemCom. Third, we present the design of the DL-based auction for edge resource allocation for the edge computing-assisted Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue of the ECP and that VSPs pay a lower offloading cost with SemCom.
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