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An AAE-based Proactive Content Chunked Caching Scheme with Mobility Prediction in VANETs

Published: 27 July 2023 Publication History

Abstract

In Vehicular Ad hoc Networks(VANETs), edge caching can meet the increasing needs of users for vehicular applications and reduce the network burden. However, the existing studies rarely consider the differences in popularity caused by different regions and the high mobility of vehicles. To improve the efficiency of edge caching and reduce the content retrieval cost, an AAE-based proactive content chunked caching scheme(APCCMP) with mobility prediction is proposed. Firstly, according to the local historical request matrix and user preference matrix, Adversarial Autoencoder(AAE) is used to predict local content popularity. Then, considering the moving trajectory, RSUs predict the transition probability of vehicles on the Markov model. Finally, according to the prediction results and vehicles’ speeds, the content chunks are cached on multiple RSUs. The experimental results show that the proposed scheme is significantly superior to other baseline schemes.

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      cover image ACM Other conferences
      CNIOT '23: Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things
      May 2023
      1025 pages
      ISBN:9798400700705
      DOI:10.1145/3603781
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 27 July 2023

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      Author Tags

      1. VANETs
      2. chunked caching
      3. mobility prediction
      4. popularity prediction

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