Abstract
In recent years, the use of wireless sensor networks has become increasingly widespread. Because of the instability of wireless networks, packet loss occasionally occurs. To reduce the impact of packet loss on data integrity, we take advantage of the deep neural network's excellent ability to understand natural data and propose a data repair method based on a deep convolutional neural network with an encoder-decoder architecture. Compared with common interpolation algorithms and compressed sensing algorithms, this method obtains better repair results, is suitable for a wider range of applications, and does not need prior knowledge. This method adopts measures such as preparing training set data as well as the design and optimization of loss functions to achieve faster convergence speed, higher repair accuracy, and better stability. To fairly compare the repair performance of different signals, the mean squared error, relative peak-to-peak average error, and relative peak-to-peak max error are adopted to quantitatively evaluate the repair results of different signals. Comparative experiments prove that this method has better data recovery performance than traditional interpolation and compressed sensing algorithms.
Highlights
Wireless sensor network technology has been widely used in fields such as the military, national security, environmental science, traffic management, disaster prediction, medical and health, manufacturing, and urban information construction
When using a deep convolutional neural network to denoise noisy images, the best filtering results are obtained after a certain number of training iterations, and as this number increases, noise gradually appears on the output image [16]
The results obtained in continuous packet loss mode show the proposed data repair method based on DCNNWEDA and the CS algorithm can reconstruct the detailed characteristics of the signal well
Summary
Wireless sensor network technology has been widely used in fields such as the military, national security, environmental science, traffic management, disaster prediction, medical and health, manufacturing, and urban information construction. Y. Qie et al.: Data Repair Without Prior Knowledge Using Deep Convolutional Neural Networks deep learning in recent years, many algorithms based on these approaches have achieved good results in the field of image reconstruction and denoising [9]–[15]. Qie et al.: Data Repair Without Prior Knowledge Using Deep Convolutional Neural Networks deep learning in recent years, many algorithms based on these approaches have achieved good results in the field of image reconstruction and denoising [9]–[15] These deep learning algorithms often need to learn prior knowledge about the signals through a large amount of data, which increases the cost of algorithm. Compared with the interpolation algorithm and CS algorithm, the method proposed in this paper has a very good repair results for both high and low-frequency signals.
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