基于连通性预测的水下容错网络自适应路由协议

Tiansi Hu, Yunsi Fei
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引用次数: 21

摘要

水下传感器网络(UWSNs)是一种理想的网络技术,可以促进各种水生应用。然而,水下通信的不利特性和水下传感器节点的高成本限制了uwsn的稀疏部署,导致间歇性连接,因此需要延迟/中断容忍网络(DTNs)技术。为了应对中断,必须在路由协议中做出额外的努力,为上层应用程序提供透明和健壮的端到端连接。本文提出了一种新的水下DTNs自适应节能路由协议。通过利用水下节点移动模式与自适应滤波器,传感器节点能够估计未来的接触事件与其他节点除了平均接触概率在一个预测窗口。提出的协议基于分布式机器学习技术Q-learning,旨在选择最有前途的转发器,以最大限度地减少端到端延迟。对所提出的协议进行了大量的仿真,结果表明,与其他现有的DTN路由协议相比,我们的协议产生了更好的网络性能和能源效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adaptive routing protocol based on connectivity prediction for underwater disruption tolerant networks
Underwater Sensor Networks (UWSNs) are a desirable networking technique to facilitate various aquatic applications. However, the adverse characteristics of underwater communications and high cost of underwater sensor nodes limit UWSNs to sparse deployment, resulting in intermittent connectivity and therefore calling for techniques for Delay/Disruption Tolerant Networks (DTNs). To cope with disruptions, extra efforts have to be made in the routing protocol to provide transparent and robust end-to-end connections to upper-layer applications. In this paper, we propose a novel adaptive and energy-efficient routing protocol for underwater DTNs. By exploiting underwater node mobility patterns with adaptive filters, sensor nodes are able to estimate future contact events with other nodes in addition to the average contact probabilities over a prediction window. The proposed protocol is based on a distributed machine learning technique, Q-learning, which aims to select the most promising forwarders so as to minimize the end-to-end delay. Extensive simulations of the proposed protocol are carried out, and the results have shown that our protocol yields significantly better network performances and energy efficiency compared to other existing DTN routing protocols.
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