.png){kind=icon}
返回
INTELLIGENT TASK OFFLOADING IN VEHICULAR EDGE COMPUTING NETWORKS
.png){kind=icon}
PRE
AI
DOI：10.1109/MWC.001.1900489.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, traditional transportation systems have been gradually evolving to ITS, inspired by both artificial intelligence and wireless communications technologies. The vehicles get smarter and connected, and a variety of intelligent applications have emerged. Meanwhile, the shortage of vehicles' computing capacity makes it insufficient to support a growing number of applications due to their compute-intensive nature. This contradiction restricts the development of ICVs and ITS. Under this background, vehicular edge computing networks (VECNs), which integrate MEC and vehicular networks, have been proposed as a promising network paradigm. By deploying MEC servers at the edge of the network, ICVs' computational burden can be greatly eased via MEC offloading. However, existing task offloading schemes had insufficient consideration of fast-moving ICVs and frequent handover with the rapid changes in communications, computing resources, and so on. Toward this end, we design an intelligent task offloading scheme based on deep Q learning, to cope with such a rapidly changing scene, where software-defined network is introduced to achieve information collection and centralized management of the ICVs and the network. Extensive numerical results and analysis demonstrate that our scheme not only has good adaptability, but also can achieve high performance compared to traditional offloading schemes.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.5
论文数:
2.7K
被引数:
1.3W
机构
引用论文
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks车载边缘计算和网络中的联合负载均衡和卸载
PREAI
分享收藏Datanet: Deep learning Based Encrypted Network Traffic Classification in SDN Home GatewayDatanet: 基于深度学习的SDN家庭网关加密网络流量分类.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容