arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2108.05057cs.NI
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基于最近邻回归的水下传感器网络路由协议

A Channel-Aware Routing Protocol With Nearest Neighbor Regression For Underwater Sensor Networks

Boyu Diao, Chao Li, Qi Wang, Zhulin An, Yongjun Xu

摘要:

水声信道是最具挑战性的通信信道之一。由于潮汐和气候的周期性变化,水下噪声具有周期性波动,导致声信道质量长期周期性变化。此外,时变信道质量导致路由失败。近年来,人们提出了基于声信道估计的路由协议,即水下信道感知路由协议。然而,这些路由协议的信道估计算法大多是线性的,很少考虑声学信道的周期性。介绍了一种基于最近邻回归的水声信道估计方法。我们扩展了最近邻回归SNR(信噪比)时间序列预测,提供了一个出色的预测精度复杂的周期性和波动的接收SNR时间序列。提出了一种快速搜索算法,并利用统计存储压缩来优化算法的时间复杂度和空间复杂度。与线性方法相比,该算法在仿真和海试数据集上的信道预测精度都有显著提高(最高可达3倍以上)。基于这种信道估计方法,我们提出了一种基于深度的路由协议(DBCAR)。通过大量的仿真实验,验证了DBCAR在数据包投递率、平均能耗和平均传输时延等方面具有优异的网络性能。

英文摘要:

The underwater acoustic channel is one of the most challenging communication channels. Due to periodical tidal and daily climatic variation, underwater noise is periodically fluctuating, which result in the periodical changing of acoustic channel quality in long-term. Also, time-variant channel quality leads to routing failure. Routing protocols with acoustic channel estimation, namely underwater channel-aware routing protocols are recently proposed to maintain the routing performance. However, channel estimation algorithms for these routing protocols are mostly linear and rarely consider periodicity of acoustic channels. In this paper, we introduce acoustic channel estimation based on nearest neighbor regression for underwater acoustic networks. We extend nearest neighbor regression for SNR (Signal-to-Noise Ratio) time series prediction, providing an outstanding prediction accuracy for intricately periodical and fluctuating received SNR time series. Moreover, we propose a quick search algorithm and use statistical storage compression to optimize the time and space complexity of the algorithm. In contrast with linear methods, this algorithm significantly improves channel prediction accuracy (over three times at most) on both simulation and sea trial data sets. With this channel estimation method, we then propose a Depth-Based Channel-Aware Routing protocol (DBCAR). Taking advantage of depth-greedy forwarding and channel-aware reliable communication, DBCAR has an outstanding network performance on packet delivery ratio, average energy consumption and average transmission delay which is validated through extensive simulations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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