Feature-Level Data Aggregation for Underwater Acoustic Networks Based on Distributed Function Approximation and Cross-Layer Design

Zhe Jiang, Bingbing Zheng, Weijie Ning, Xiaohong Shen

科研成果: 期刊稿件文章同行评审

摘要

In underwater acoustic sensor networks (UASNs), feature-level data of targets from each sensor often need to be collected for further processing. Given that feature-level data of each sensor come from the same target, spatial correlation among these data is natural. Surprisingly, there has been a very limited exploration into leveraging this spatial correlation for UASN data aggregation. In this article, we have addressed feature-level data aggregation for UASNs by exploring and leveraging the spatial correlation of data from different sensors. We have proposed a model where various sensor data can be effectively represented through spatial functions. We have also proposed a feature-level data aggregation algorithm based on distributed function approximation, and thus, only a small number of sensors need to transmit their data. We also utilize a cross-layer design combined with the carrier sense multiple access (CSMA) protocol to further accelerate the data aggregation. We have provided simulation results to demonstrate the performance of the proposed algorithm.

源语言英语
页(从-至)28164-28177
页数14
期刊IEEE Sensors Journal
24
17
DOI
出版状态已出版 - 2024

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