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Improving broadcast efficiency of irresponsible forwarding with random linear coding at source

机译:在源头使用随机线性编码提高不负责任转发的广播效率

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Broadcasting is the most prevalent method for disseminating information in vehicular networks. At high vehicle densities, the so-called broadcast storm problem degrades the efficiency of broadcasting. A so-called Irresponsible Forwarding (IF) scheme has recently been proposed in the literature that can effectively combat the broadcast storm problem. For messages consisting of multiple packets, the coupon collector problem also degrades the broadcast efficiency at all vehicle densities. In this paper, we extend the basic IF scheme to multi-packet messages, which we call the max-min IF, and combine it with Random Linear Coding (RLC) of packets at the source to solve the coupon collector problem and improve the broadcast efficiency of IF. Through discrete event simulations, with a widely accepted realistic vehicular mobility model based on cellular automata, we demonstrate that our IF+RLC scheme can significantly improve the reachability in sparsely connected vehicular networks at low vehicle densities as well as reduce the mean delay under high probability of collisions at high vehicle densities.
机译:广播是在车辆网络中传播信息的最普遍的方法。在高车辆密度下,所谓的广播风暴问题降低了广播效率。最近在文献中提出了一种所谓的不负责任转发(IF)方案,该方案可以有效地解决广播风暴问题。对于包含多个数据包的消息,优惠券收集器问题还会降低所有车辆密度下的广播效率。在本文中,我们将基本的IF方案扩展到多包消息,我们将其称为max-min IF,并将其与源处的数据包的随机线性编码(RLC)结合起来,以解决优惠券收集器问题并改善广播中频的效率。通过离散事件模拟,并使用广泛认可的基于细胞自动机的现实车辆机动性模型,我们证明了我们的IF + RLC方案可以显着提高稀疏连接的车辆网络在低车密度下的可达性,并在高概率下减少平均时延高车辆密度时发生碰撞的情况。

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