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End-to-end congestion control in wireless mesh networks using a neural network

机译:使用神经网络的无线网状网络中的端到端拥塞控制

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Maintaining the performance of reliable transport protocols, such as TCP, over wireless mesh networks is a challenging problem due to the unique characteristics of wireless mesh networks such as the lossy nature of the communication medium, absence of a base station, similarity in traffic pattern experienced by neighboring mesh nodes, etc. One of the reasons for the poor performance of conventional TCP variants over wireless mesh networks is that the congestion control mechanisms in conventional TCP variants do not explicitly account for these unique characteristics. To address this problem, this paper proposes a novel neural network based congestion control technique for reliable data transfer over wireless mesh networks. We analyze the proposed congestion control technique in detail and incorporate it into TCP to create a variant that we name intelligent TCP or iTCP. We evaluate the performance of iTCP using ns-2 simulations. Our results demonstrate that our proposed congestion control technique exhibits a significant improvement in total network throughput and average energy consumption per bit compared to congestion control techniques used in other variants of TCP.
机译:由于无线网状网络的独特特性,例如通信介质的有损特性,缺少基站,经​​历的流量模式相似,在无线网状网络上保持可靠的传输协议(如TCP)的性能是一个具有挑战性的问题。无线网状网络上常规TCP变体性能较差的原因之一是,常规TCP变体中的拥塞控制机制并未明确考虑这些独特特性。为了解决这个问题,本文提出了一种新的基于神经网络的拥塞控制技术,用于无线网状网络上可靠的数据传输。我们将详细分析所建议的拥塞控制技术,并将其合并到TCP中,以创建一个名为智能TCP或iTCP的变体。我们使用ns-2仿真评估iTCP的性能。我们的结果表明,与其他TCP变体中使用的拥塞控制技术相比,我们提出的拥塞控制技术在总网络吞吐量和每比特平均能耗方面具有显着改善。

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