Intelligent UAV-aided controller placement scheme for software-defined vehicular networks

Na Lin, Qi Zhao, Liang Zhao
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Abstract

Recently, researchers have used long short-term memory (LSTM) networks and the bi-directional long short-term memory (Bi-LSTM) networks to process sequence data sets such as vehicle positions in software-defined vehicular networks (SDVN). In this paper, we present a three-component intelligent UAV-aided controller placement scheme (CPP) for SDVN. First, we use Bi-LSTM to model the real-time position of vehicles (traffic flow). Second, we implement a dynamic scheme to place controllers and UAVs (DCUPE) in the network based on the predicted flow. Third, in order to collect real-time traffic information and manage the network, we compute trajectories for the UAVs from real-time Bi-LSTM predictions of vehicle positions and an adaptive artificial bee colony algorithm for the traveling salesman problem (IDABC-TSP). We evaluate our proposed design as a function of energy cost, communication delay, and packet delivery ratio. Our experimental results show the effectiveness of our scheme on real geographical topologies.
软件定义车辆网络智能无人机辅助控制器布局方案
近年来,研究人员利用长短期记忆(LSTM)网络和双向长短期记忆(Bi-LSTM)网络处理软件定义车辆网络(SDVN)中车辆位置等序列数据集。本文提出了一种用于SDVN的三组件智能无人机辅助控制器放置方案(CPP)。首先,我们使用Bi-LSTM建模车辆(交通流)的实时位置。其次,我们实现了一种基于预测流的动态布局方案,将控制器和无人机(DCUPE)放在网络中。第三,为了收集实时交通信息和管理网络,我们利用实时Bi-LSTM预测车辆位置和自适应人工蜂群算法计算旅行推销员问题(IDABC-TSP)的无人机轨迹。我们以能量成本、通讯延迟和封包传送率的函数来评估我们所提出的设计。实验结果表明了该方法在实际地理拓扑上的有效性。
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