{"id":85,"date":"2025-08-25T16:16:33","date_gmt":"2025-08-25T08:16:33","guid":{"rendered":"https:\/\/www.lu-bjut.net\/?page_id=85"},"modified":"2025-09-09T21:58:59","modified_gmt":"2025-09-09T13:58:59","slug":"85-2","status":"publish","type":"page","link":"https:\/\/www.lu-bjut.net\/?page_id=85","title":{"rendered":"Multi"},"content":{"rendered":"\n<h2 class=\"wp-block-heading alignwide has-x-large-font-size\"><strong>Multi-Timescale Hierarchical Prefetching for Online Caching in Vehicular Edge Networks<\/strong><\/h2>\n\n\n\n<h2 class=\"wp-block-heading alignwide has-medium-font-size\"><strong>Authors:<\/strong>&nbsp;Shuaibing Lu, Bojin Xiang, Jie Wu, Philipp Andelfinger, Wentong Cai<\/h2>\n\n\n\n<h2 class=\"wp-block-heading alignwide has-medium-font-size\"><strong>Conference:<\/strong>&nbsp;ICCCN 2025 \u2013 International Conference on Computer Communications and Networks<\/h2>\n\n\n\n<h2 class=\"wp-block-heading alignwide has-medium-font-size\"><strong>Publication Date:<\/strong>&nbsp;August 4, 2025<\/h2>\n\n\n\n<h2 class=\"wp-block-heading alignwide has-large-font-size\"><strong>Abstract<\/strong><\/h2>\n\n\n\n<div class=\"wp-block-columns alignwide is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<p class=\"has-text-align-left has-medium-font-size\">Content delivery in vehicular edge networks faces critical challenges due to dynamic user mobility, unpredictable content request patterns, and limited storage at edge nodes. To tackle these problems, we propose a distributed online framework that jointly performs proactive caching at roadside units (RSUs) and hierarchical prefetching from the cloud to macro base stations (MBSs), enabling real-time adaptation to spatiotemporal variations in content demand across different time scales. Our goal is to minimize content transmission latency while satisfying system-wide resource and cost constraints. The proposed Vehicular-based Online Proactive caching and Prefetching (VOPP), integrates trajectory-based user mobility prediction with future content demand estimation to guide online distributed caching. At the RSU level, we formulate a distributed online convex optimization model with fine-grained gradient updates and inter-agent coordination based on real-time mobility patterns. At the MBS level, we construct a multi-step predicted content set using user mobility and request forecasts, and define a value density metric that combines popularity and delay reduction. On both levels, additional subsequent refinement steps ensure high-quality caching decisions. Extensive simulations based on a real-world GPS dataset of 10,357 taxi trajectories in Beijing demonstrate that VOPP significantly reduces transmission delay and achieves robust performance across diverse mobility patterns and user densities, outperforming baseline methods.<\/p>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading alignwide has-large-font-size\"><strong>Download<\/strong><\/h2>\n\n\n\n<h2 class=\"wp-block-heading alignwide has-medium-font-size\">You can download the full paper here:&nbsp;<a href=\"https:\/\/www.lu-bjut.net\/wp-content\/uploads\/2025\/08\/content_prefetch.pdf\" download=\"\">content_prefetch.pdf<\/a><\/h2>\n","protected":false},"excerpt":{"rendered":"<p>Multi-Timescale Hierarchical Prefetching for Online Cac [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-no-title","meta":{"footnotes":""},"class_list":["post-85","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.7 - 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