A Novel Proactive Cache Decision Algorithm Based on Prior Knowledge and Aerial Cloud Assistance in Internet of Vehicles

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-25 DOI:10.1109/TNSE.2024.3433544

Geng Chen;Jingli Sun;Yuxiang Zhou;Qingtian Zeng;Fei Shen

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引用次数: 0

Abstract

In recent years, mobile data has grown explosively due to the rapid development of Internet of Vehicles (IoV). However, resources of IoV are limited, in order to alleviate the problem of resource shortage, it is necessary to combine the resource rich aerial cloud and the ground edge nodes. In order to improve efficiency of proactive cache, we propose a proactive cache decision algorithm based on prior knowledge and aerial cloud assistance. Firstly, we divide requests into two types: content download requests and task calculation requests. Then the dynamic request graph based on relationship between users and requests is constructed, temporal graph network and long short term memory are used to predict prior information and caching benefit function is proposed based on popularity and supplemented by prior information to indicate cache location of request content. Finally, the problem of maximizing cache benefit is proposed and the theoretical solution is obtained using Lagrange multiplier method as well as simulation solution is obtained based on Deep Deterministic Policy Gradient. The simulation results demonstrate that the proposed caching scheme can greatly improve caching efficiency, reduce latency and energy consumption.Compared to D3QN, Dueling DQN, and Double DQN, system revenue of proposed algorithm has increased by 66.65%, 177.71% and 36.08%.

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车联网中基于先验知识和空中云辅助的新型主动缓存决策算法

近年来，随着车联网（IoV）的快速发展，移动数据呈爆炸式增长。然而，车联网的资源是有限的，为了缓解资源短缺的问题，有必要将资源丰富的空中云和地面边缘节点结合起来。为了提高主动缓存的效率，我们提出了一种基于先验知识和空中云辅助的主动缓存决策算法。首先，我们将请求分为两类：内容下载请求和任务计算请求。然后，根据用户和请求之间的关系构建动态请求图，利用时序图网络和长短期记忆预测先验信息，并提出基于流行度的缓存收益函数，辅以先验信息指示请求内容的缓存位置。最后，提出了缓存效益最大化问题，并利用拉格朗日乘数法获得了理论解，同时基于深度确定性策略梯度法获得了仿真解。仿真结果表明，所提出的缓存方案可以大大提高缓存效率，降低延迟和能耗。与 D3QN、Dueling DQN 和 Double DQN 相比，所提出算法的系统收益分别提高了 66.65%、177.71% 和 36.08%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.