面向元宇宙移动增强现实应用的异质内容主动缓存与个性化交付机制
Proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the Metaverse
- 通信学报 2024年45卷第10期 页码：55-68
- 作者机构：
北京邮电大学网络与交换技术全国重点实验室，北京 100876
- 作者简介：
[ "徐思雅（1988- ），女，北京人，博士，北京邮电大学副教授、博士生导师，主要研究方向为信息通信网络管理、SDN/NFV、移动边缘计算、人工智能等。" ]
[ "付琦梦（2000- ），女，河南许昌人，北京邮电大学硕士生，主要研究方向为移动边缘计算和人工智能。" ]
[ "郭少勇（1985- ），男，河北邢台人，博士，北京邮电大学教授、博士生导师，主要研究方向为区块链、物联网等。" ]
- 基金信息：国家自然科学基金资助项目(62201074);北京邮电大学基本科研业务费基金资助项目(2023ZCTH11)
DOI：10.11959/j.issn.1000-436x.2024187
中图分类号： TP393收稿：2024-08-02，
修回：2024-10-11，
纸质出版：2024-10-25
- 稿件说明：
移动端阅览
徐思雅,付琦梦,郭少勇.面向元宇宙移动增强现实应用的异质内容主动缓存与个性化交付机制[J].通信学报,2024,45(10):55-68.
XU Siya,FU Qimeng,GUO Shaoyong.Proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the Metaverse[J].Journal on Communications,2024,45(10):55-68.
徐思雅,付琦梦,郭少勇.面向元宇宙移动增强现实应用的异质内容主动缓存与个性化交付机制[J].通信学报,2024,45(10):55-68. DOI： 10.11959/j.issn.1000-436x.2024187.
XU Siya,FU Qimeng,GUO Shaoyong.Proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the Metaverse[J].Journal on Communications,2024,45(10):55-68. DOI： 10.11959/j.issn.1000-436x.2024187.
摘要
针对移动增强现实（MAR）应用异质数据内容传输和响应时延的问题，提出了面向元宇宙移动增强现实应用的异质内容主动缓存与个性化交付机制，首先综合考虑了用户特征和边缘节点服务能力，提出了用户行为和资源感知的边缘协作服务域构建方法；进而，基于协作服务域设计基于存储空间划分和用户偏好预测的异质内容预缓存机制；特别地，针对前景内容引入了个性化推荐机制，并设计了前景内容的差异化策略。仿真结果表明，所提机制在缓存命中率、前景内容和异质内容的平均响应时延方面均优于NCPCR策略、CCS-AGP策略和AIEC-RSC策略。
Abstract
Addressing the issues of heterogeneous data content transmission and response latency in mobile augmented reality (MAR) applications
a proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the metaverse was proposed. Firstly
considering user characteristics and edge node service capabilities
a user behavior and resource aware edge collaboration service domain (ECSD) construction method was proposed. Then
based on the ECSD
heterogeneous content pre-caching mechanisms based on storage space division and user preference prediction were designed. Specially
by introducing the personalized recommendation mechanism of foreground content
a differentiated delivery strategy of foreground content was designed. Simulation results show that the proposed mechanism outperforms NCPCR
CCS-AGP
and AIEC-RSC strategy in terms of cache hit rate
average response latency for foreground content and heterogeneous content.
关键词
Keywords
references
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