基于卷积自编码器的无线传感器网络容噪数据重构

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY
Trinh Thuc Lai, Tuan Phong Tran, Jaehyuk Cho, Myung-Sig Yoo
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引用次数: 0

摘要

维护无线传感器网络(WSN)系统的数据可靠性具有重要意义。然而,在无人值守和敌对地区部署系统对处理噪声提出了重大挑战。因此,已经进行了几项调查,以解决受噪声影响的数据恢复问题。然而,以往的研究主要集中在系统的内部噪声。在研究中忽略了影响WSN系统的外部因素可能会导致研究结果与现实不符。因此,本研究考虑了内部和外部噪声因素,如雨、雾或雪条件。此外,为了保持时间特征和传感器间的关系,将来自多个传感器节点的数据整合为二维矩阵格式。提出了一种具有数据特征提取能力的堆叠卷积自编码器(SCAE)模型。SCAE的堆叠设计使其能够有效地缓解梯度消失的问题。此外,在两个子网之间使用的权值共享方法也提高了权值初始化过程的效率。利用模拟的WSN系统和真实的传感数据进行了全面的实验。实验结果表明,SCAE在重建噪声数据方面优于现有的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network
Maintaining data dependability within wireless sensor network (WSN) systems has significant importance. Nevertheless, the deployment of systems in unattended and hostile areas poses a major challenge in dealing with noise. Consequently, several investigations have been conducted to address the issue of noise-affected data recovery. Nevertheless, previous research has primarily focused on the internal noise of the system. Neglecting to include external factors that impact the WSN system in the study might lead to findings that are not true to reality. Hence, this research takes into account both internal and external noise factors, such as rain, fog, or snow conditions. Moreover, in order to maintain the temporal characteristics and intersensor relationships, the data from multiple sensor nodes are consolidated into a two-dimensional matrix format. The stacked convolutional autoencoder (SCAE) model is proposed, which has the capability to extract data features. The stack design of the SCAE enables it to effectively mitigate the issue of vanishing gradients. Moreover, the weight sharing approach used between the two subnetworks also enhances the efficiency of the weight initialization procedure. Thorough experiments are conducted using both simulated WSN systems and real-world sensing data. Experimental results demonstrate that the SCAE outperforms existing methods for reconstructing noisy data.
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来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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