Abstract

The high number of connected nodes in Internet of Vehicles (IoVs) drives to high data exchange between nodes, which increases the network overhead. Moreover, the recurrent change in vehicle mobility in Internet of Vehicles (IoVs) drives to frequent changes in network topology which in turn causes frequent link disconnections. Therefore, the most addressed issues in IoVs are to manage the high quantity of packets sent by the huge number of vehicles connected with IoT devices, to reduce communication delays and guarantee the longest communication stability. Clustering techniques have been utilized to reduce network overhead in IoVs networks. Classical clustering algorithms have been proposed to enhance network performances. However, IoVs environment is characterized by the high dynamicity of nodes, therefore, the optimization methods already proposed cannot perfectly deal with the characteristics of IoVs. Reinforcement learning (RL) is a machine learning algorithm, where the agent learns from its environment and tries to enhance its policies to obtain the best reward. In this paper, we propose to use deep reinforcement learning (DRL) to select the best cluster heads based on node’s degree, node’s buffer size, and signal strength. In the proposed work, the vehicle can perfectly select the cluster heads by choosing the best state-action values taking in consideration the high dynamicity of the network.

中文翻译：

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基于深度强化学习的车联网鲁棒通信

摘要

车联网 (IoV) 中的大量连接节点导致节点之间的数据交换量很大，从而增加了网络开销。此外，车联网中车辆移动性的反复变化导致网络拓扑频繁变化，进而导致链路频繁断开。因此，车联网中最需要解决的问题是管理与物联网设备连接的大量车辆发送的大量数据包，以减少通信延迟并保证最长的通信稳定性。集群技术已被用来减少车联网网络中的网络开销。经典的聚类算法已被提出来增强网络性能。然而，车联网环境的特点是节点的高动态性，因此，现有的优化方法并不能很好地处理车联网的特点。强化学习（RL）是一种机器学习算法，代理从其环境中学习并尝试增强其策略以获得最佳奖励。在本文中，我们建议使用深度强化学习（DRL）根据节点的度数、节点的缓冲区大小和信号强度来选择最佳簇头。在所提出的工作中，考虑到网络的高动态性，车辆可以通过选择最佳状态动作值来完美选择簇头。我们建议使用深度强化学习（DRL）根据节点的度数、节点的缓冲区大小和信号强度来选择最佳簇头。在所提出的工作中，考虑到网络的高动态性，车辆可以通过选择最佳状态动作值来完美选择簇头。我们建议使用深度强化学习（DRL）根据节点的度数、节点的缓冲区大小和信号强度来选择最佳簇头。在所提出的工作中，考虑到网络的高动态性，车辆可以通过选择最佳状态动作值来完美选择簇头。