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- W4386447118 abstract "Real-time content delivery is crucial for augmented and virtual reality applications in AR/VR-enabled autonomous vehicles (AVs), but data retrieval is challenging due to network congestion, node mobility, and intermittent connectivity. In this paper, we propose a hierarchical search framework for real-time data retrieval in vehicular networks. Our proposed system architecture includes a cloud server that quickly forwards and searches queries from indexed edges when the local edge search fails. To retrieve the most relevant data with minimal delay, we develop a search utility model that measures both the expected response time and the freshness of cached data. Based on this model, we formulate an edge-assisted real-time search (EARS) problem to maximize the overall search utility. We further streamline our approach by utilizing an ant colony optimization algorithm called ant colony optimization for search utility (ACOSU). Our extensive simulations demonstrate the effectiveness of our approach, which significantly enhances the freshness (latency) by 26.15%-44.44%, the hit ratio by 19.48%-33.89%, and reduces excessive network overheads." @default.
- W4386447118 created "2023-09-06" @default.
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- W4386447118 date "2023-08-10" @default.
- W4386447118 modified "2023-09-27" @default.
- W4386447118 title "Real-Time Search-Driven Content Delivery in Vehicular Networks for AR/VR-Enabled Autonomous Vehicles" @default.
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- W4386447118 doi "https://doi.org/10.1109/iccc57788.2023.10233627" @default.
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