Data prediction model in wireless sensor networks based on bidirectional LSTM

• Published in: EURASIP journal on wireless communications and networking Vol. 2019; no. 1; pp. 1 - 12
• Main Authors: Cheng, Hongju, Xie, Zhe, Wu, Leihuo, Yu, Zhiyong, Li, Ruixing
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 13.08.2019; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms and Architectures for Industrial Wireless Sensor Networks; Communications Engineering; Data prediction; Data transmission; Engineering; Feature extraction; Information Systems Applications (incl.Internet); LSTM; Networks; Neural networks; Nodes; Noise reduction; Redundancy; Remote sensors; Sensors; Signal,Image and Speech Processing; Spatial-temporal correlation; Wavelet analysis; Wireless networks; Wireless sensor networks; Data prediction; Wireless sensor networks; LSTM; Spatial-temporal correlation

The data collected by the wireless sensor nodes often has some spatial or temporal redundancy, and the redundant data impose unnecessary burdens on both the nodes and networks. Data prediction is helpful to improve data quality and reduce the unnecessary data transmission. However, the current data...