An RNN-Based Delay-Guaranteed Monitoring Framework in Underwater Wireless Sensor Networks

Abstract

Real-time underwater monitoring has been widely applied in many applications of underwater wireless sensor networks (UWSNs). Due to the long acoustic communication delays, the real-time data collection in UWSNs is challenging. Moreover, the underwater acoustic transmission faces the problem of high data loss rate, which causes a longer delay time due to the need for packet retransmissions. To address these problems, we propose a recurrent neural network (RNN)-based underwater monitoring framework with the consideration of delay, energy, and data quality. We drop the automatic retransmission mechanism applied in the MAC protocols to reduce the long end-to-end delay and energy cost. Facing high data loss, we propose an efficient RNN learning model, LSTM-Decay, to analyze the raw data with the time-related decay weights features and predict the missing values. The experiments with the real-world underwater sensing datasets show that our learning model can achieve an accurate estimation with different degrees of missing rates and can provide better performance compared with the non-RNN and RNN baselines.