IEEE 802.11s无线网状网络中视频监控无人机的路由协议

C. Katila, A. Gianni, C. Buratti, R. Verdone
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引用次数: 20

摘要

在本文中,我们考虑了一个视频监控应用,使用安装在无人机上的摄像机飞过被监控区域并将视频发送到控制中心(CC)。为了确保无人机和CC之间的连接,一些继电器部署在地面上。由此产生的网络由一个静态组件(继电器)和一个移动组件(无人机)组成。假设所有网络设备都具有IEEE 802.11s空中接口。我们工作的目标是设计和验证适合此场景的路由协议。IEEE 802.11s标准提出了混合无线网状路由协议(HWMP),该协议由一种主动的基于树的路由和一种被动的无线电度量自组织按需距离矢量(RM-AODV)方案组成,以支持网状网络。为了满足对可靠连接、更快和资源高效路径发现的需求,我们设想了一种混合优化方案,称为优化混合无线网状协议(O-HWMP),其中RM-AODV和主动基于树的方案同时使用。在O-HWMP中,基于树的路由方案的输出为RM-AODV提供输入,以减少控制数据包的泛滥,并最小化路径发现过程中的延迟。通过NS3-Evalvid模拟,我们证明,与RM-AODV方案相比,我们提出的协议在延迟、数据包成功率、开销成本和接收视频的峰值信噪比指标方面显着提高了网络性能。
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Routing protocols for video surveillance drones in IEEE 802.11s Wireless Mesh Networks
In this paper we consider a video surveillance application, using a camera mounted on a drone flying over the area to be monitored and sending the video to a control center (CC). In order to ensure connectivity between the drone and the CC some relays are deployed on the ground. The resulting network is composed of a static component (relays), and a moving component (the drone). All network devices are assumed to be equipped with IEEE 802.11s air interfaces. The goal of our work is to design and validate a routing protocol appropriate for this scenario. The IEEE 802.11s standard proposes Hybrid Wireless Mesh routing Protocol (HWMP) composed of a proactive tree-based routing and the reactive Radio Metric Ad-hoc On-Demand Distance Vector (RM-AODV) scheme to support mesh networks. To address the need for reliable connectivity, faster and resource-efficient path discovery, we envisage a mixed optimized scheme, called Optimized-Hybrid Wireless Mesh Protocol (O-HWMP), where both, RM-AODV and the proactive tree-based scheme, are used at the same time. In O-HWMP the output of the tree-based routing scheme provides input to the RM-AODV, in order to reduce flooding of control packets, and to minimize delays during path discovery. Through NS3-Evalvid simulations we demonstrate that, compared to RM-AODV scheme, our proposed protocol significantly improves network performance in terms of delays, packet success rate, overhead cost, and peak-signal-to-noise-ratio metric of the received video.
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