A Low Complexity Aggregation Method for Underwater On-Pipe Sensor Network
A Low Complexity Aggregation Method for Underwater On-Pipe Sensor Network
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DOI：
10.1145/3491315.3491343
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Chenpei Huang;Chaoxian Qi;A. Song;Gangbing Song;Jiefu Chen;Miao Pan
中科院分区：
文献类型：
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作者：
Chenpei Huang;Chaoxian Qi;A. Song;Gangbing Song;Jiefu Chen;Miao Pan
研究了水下管道辅助应力波通信中的传感器聚合问题。虽然SWC能够支持短距离的管道传感器网络，但由于管道接头处的反射影响，长距离SWC的频谱受到限制。为了解决这个问题，提出了基于正交频分复用（OFDM）的共识数据聚合。此外，可以在多接入信道（MAC）中的“传输期间计算”之后经由时域采样来接收共识数据。仿真结果表明，在5 dB的平均信噪比下，5，10和20个传感器聚合的均方误差（MSE）分别为0.26%，0.03%和0.08%。当增加信噪比（SNR）或传感器数量时，可以观察到消失的均方误差（MSE）。该设计不需要下变频和解调，通过简单的时域采样就可以获得聚合数据。
This paper considers the sensor aggregation for underwater pipe-assisted stress wave communication (SWC). Although the SWC is able to support the on-pipe sensor network in short range, the spectrum of long-range SWC is limited due to the effect of reflections at the pipe joints. To address this issue, the orthogonal frequency-division multiplexing (OFDM)-based consensus data aggregation is proposed. Furthermore, the consensus data can be received via time-domain sampling after ’compute-during-transmit’ in multiple access channels (MAC). The simulation results show 0.26%, 0.03%, and 0.08% mean-square-error (MSE) in 5, 10, and 20 sensor aggregation respectively with 5 dB average SNR. A vanishing mean-square-error (MSE) can be observed when increasing either the signal-to-noise ratio (SNR) or the number of sensors. This design can obtain the aggregated data by a simple time domain sampling with no need of down-conversion and demodulation.