Convolutional Auto-encoder-Based Packet-Loss-and-Error-Resilient Data Collection Method for Underwater Sensors

In recent years, the underwater acoustic sensor network (UASN) is emerging as an effective means for marine data collection. However, due to the limited bandwidth of acoustic channel, the limited constrained energy supply of underwater sensors and data redundancy, it is impossible to deliver all the raw data generated by underwater sensors. Hence, to compress time-redundant data at a sensor node is of great significance for data collection from underwater sensors. Moreover, the underwater acoustic transmission link is unreliable, the issue of data collection with certain packet error resilience should be resolved. In this paper, a packet-loss-and-error-resilient data collection method based on convolutional auto-encoder (CAE) is proposed to collect the time-series data from underwater sensors, named as the PLER-CAE data collection method. In the proposed method, the auto-encoder is used to reduce data redundancy of the time-series data. The encoder deployed at the collection end compresses the data for transmission through the underwater channel, and the decoder deployed at the receiving end reconstructs the original data. Moreover, the proposed method complements the retransmission mechanism and the error correction technique in order to enhance data reconstruction quality. Numerical results show that the proposed PLER-CAE data collection method is effective.