Prefetching Method for Low-Latency Web AR in the WMN Edge Server
Source abstract
Abstract
Recently, low-latency services for large-capacity data have been studied given the development of edge servers and wireless mesh networks. The 3D data provided for augmented reality (AR) services have a larger capacity than general 2D data. In the conventional WebAR method, a variety of data such as HTML, JavaScript, and service data are downloaded when they are first connected. The method employed to fetch all AR data when the client connects for the first time causes initial latency. In this study, we proposed a prefetching method for low-latency AR services. Markov model-based prediction via the partial matching (PPM) algorithm was applied for the proposed method. Prefetched AR data were predicted during AR services. An experiment was conducted at the Nowon Career Center for Youth and Future in Seoul, Republic of Korea from 1 June 2022 to 31 August 2022, and a total of 350 access data points were collected over three months; the prefetching method reduced the average total latency of the client by 81.5% compared to the conventional method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: IoT and Edge/Fog Computing · Augmented Reality Applications · Age of Information Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Smart System, Kwangwoon University, Seoul 01897, Republic of Korea
Department of Plasma Bio Display, Kwangwoon University, Seoul 01897, Republic of Korea
Department of Ingenium College Liberal Arts, Kwangwoon University, Seoul 01897, Republic of Korea
Graduate School of Smart Convergence, Kwangwoon University, Seoul 01897, Republic of Korea
Funding
National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT · Ministry of Culture, Sports and Tourism and Korea Creative Content Agency
References (41)
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