飞行自组织网络视频流的多源马尔可夫模型

M. A. Akkad, A. Abilov, M. A. Lamri
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引用次数: 0

摘要

在多无人机网络中,视频流有助于执行许多任务,如监视、检查和地图生成。在这种网络中,由于节点的高移动性和拥塞,经常发生丢包。因此,需要一个模型来调查和分析端到端延迟、丢包概率和所需路径数之间的权衡。本文对飞行自组织网络(fanet)中的多源视频直播进行了分析,提出了一种网络队列模型。然后,对路径质量进行估计,进行实验,并根据实验结果得出结论。该网络采用M/M/1马尔可夫模型进行建模,以量化系统的性能。该模型将好状态和坏状态之间的转换序列作为丢包过程来考虑。在测量包接收比PRR后,利用好坏平稳概率来区分好路径和弱路径。这种多源方法提高了网络性能,并允许系统不受最弱源节点质量的限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Source Markovian Model for Video Streaming in Flying Ad hoc Networks
In multi-drone networks, video streaming helps in performing many tasks such as surveillance, inspection, and map generation. In such networks, packet loss frequently occurs due to the high mobility of the nodes and to congestion. Therefore, a model is required in order to investigate and analyze the trade-off between the end-to-end delay, packet dropping probability, and the required number of paths. In this paper, multi-source live-video streaming in Flying Ad Hoc Networks FANETs is analyzed, and a network queue model is suggested. Then, the quality of the paths is estimated, experiments are conducted, and conclusions based on the results are obtained. The network is modelled with an M/M/1 Markovian model to quantify the system’s performance. Transition sequence between good and bad states is taken into account by the proposed model as a packet loss process. The good and bad stationary probability is used, after measuring the Packet Reception Ratio PRR, in order to discriminate the good paths from the weak ones. This multi-source approach increases the network goodput and allows the system to be unlimited by the weakest source node quality.
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