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UAVs-assisted Data Collection in Vehicular Network

机译:无人机辅助数据收集在车辆网络中

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摘要

In vehicular network, nodes generate and transmit timely measured data by embedded sensors. Some data needs to be gathered by remote devices. The limited communication range of the vehicular wireless system requires to proceed through multi-hop data routing to collect periodic fresh data. The hop-by-hop based data journey and the dynamic topology increase the overall packet delivery delay, as well as the packet loss ratio. Unmanned Aerial Vehicles (UAVs) could be used in relaying vehicular data, particularly for sparse networks. Once they join the network, they are considered as normal network nodes however with some specific link characteristics. In this paper, a link-aware data collection approach is investigated. More precisely, an optimization problem maximizing a weighted multi-objective utility including the wireless link data rate, the wireless link stability, and the data progress towards the destination is formulated and solved using a Distributed Minimum Spanning Forest (DMSF) approach. The outcomes of the proposed approach and the impact of the UAVs assistance are evaluated. The present approach outperforms the other algorithms and the usage UAVs enhances the DMSF-based solution especially for low-density network.
机译:在车辆网络中,节点通过嵌入式传感器生成和传输及时测量数据。需要由远程设备收集一些数据。车辆无线系统的有限通信范围需要通过多跳数据路由来收集周期性的新鲜数据。基于跳跃的数据旅程和动态拓扑增加了整体分组输送延迟,以及丢包比。无人驾驶飞行器(无人机)可用于中继车辆数据,特别是对于稀疏网络。一旦它们加入网络,它们被视为正常的网络节点,但具有一些特定的链路特性。在本文中,研究了链接感知数据收集方法。更确切地说,使用分布式最小跨越森林(DMSF)方法,制定并解决了包括无线链路数据速率,无线链路稳定性和朝向目的地的数据进度的加权多目标实用程序的优化问题。评估所提出的方法的结果和无人机援助的影响。目前的方法优于其他算法,并且使用无人机可以增强基于DMSF的解决方案,特别是对于低密度网络。

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