Higher Reconstruction Performance with Dual Deep Neural Networks for Wireless Sensor Networks Compressed Temporal Signals

In many applications using wireless sensor networks, the reliability of monitored data is crucial to analyze situations and take decisions. Compressed sensing methods are effective to ensure durability of a wireless sensor network installation and to overcome communication failures by drastically lowering the amount of data to transmit. The method proposed in this paper aims to perform an abstraction of the signals sparsity model required by compressed sensing in order to use a deep learning-based approach. This method is designed to be effective for all kinds of transient or temporally dynamic signals sufficiently regular to be predictable, that is to say signals that can be implicitly modelled by a sparse representation. Through the collaboration of two recurrent neural networks and signal processing algorithms, our temporal method outperforms, in terms of accuracy, state-of-art methods, including distributed compressed sensing, time series imputation and forecasting deep learning methods. Thus, high compression rates up to 95% can be reached while leading to an error of less than 0.5°C in reconstructing temperature signals.