稀疏无线传感器网络中基于多跳自适应扩散的分布式估计

M.Venkatesh Nayak, T. Panigrahi, Renu Sharma
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引用次数: 6

摘要

在无线传感器网络中,通常采用扩散协同算法进行分布参数估计。每个传感器节点都具有局部计算能力,并与预定义的一跳邻居共享信息进行局部扩散。扩散方法的性能取决于传感器节点的程度,对于稀疏的传感器网络,特别是对于位于网络边缘的传感器节点,扩散方法的性能较差。为了提高估计精度和收敛速度,本文提出了多跳扩散合作算法。在这里,一个传感器节点从多跳(使用两跳)传感器节点接收信息,而不像传统的扩散算法中从近邻聚集信息。仿真结果表明,与现有的扩散方法相比,该方法的性能得到了改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed estimation using multi-hop adaptive diffusion in sparse wireless sensor networks
The diffusion cooperative scheme is conventionally used in wireless sensor networks for distributed parameter estimation. Each sensor node has the local computing ability and share information with the predefined one hop neighbors for local diffusion. The performance of the diffusion method depends on the degree of the sensor nodes and which is less for a sparse sensor network, especially for the sensor nodes lying on the edge of the network. Therefore, multi-hop diffusion cooperation is proposed here to improve the estimation accuracy and convergent speed. Here, a sensor node receives information from multi-hop (used two-hop) sensor nodes unlike aggregating from immediate neighbors in the conventional diffusion algorithm. The simulation results show the performance improvement of the proposed scheme over the existing diffusion method.
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