An RNN-Based Delay-Guaranteed Monitoring Framework in Underwater Wireless Sensor Networks
| Autor: | Hengshan Yue, Xingwang Wang, Xiaohui Wei, Shang Gao, Yuanyuan Liu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Underwater wireless sensor networks Computer science Network packet Retransmission Real-time computing General Engineering 020206 networking & telecommunications 02 engineering and technology real-time monitoring Missing data RNN missing values Recurrent neural network 020204 information systems 0202 electrical engineering electronic engineering information engineering General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering Underwater lcsh:TK1-9971 Wireless sensor network |
| Zdroj: | IEEE Access, Vol 7, Pp 25959-25971 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2899916 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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