Multivariate Data Prediction in a Wireless Sensor Network based on Sequence to Sequence Models

被引：4
|
作者：
Morales, Carlos R. [1 ]
de Sousa, Fernando R. [1 ]
Brusamarello, Valner [2 ]
Fernandes, Nestor C. [3 ]
机构：
[1] Univ Fed Santa Catarina, Dept Elect & Elect Engn, Florianopolis, SC, Brazil
[2] Univ Fed Rio Grande do Sul, Dept Elect Engn, Porto Alegre, RS, Brazil
[3] Traceback Technol, Florianopolis, SC, Brazil
来源：
2021 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC 2021) | 2021年
关键词：
Wireless Sensor Networks; Data prediction; Seq2Seq; Neural Networks;
D O I：
10.1109/I2MTC50364.2021.9459957
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Energy consumption is a major issue in Wireless Sensor Networks (WSNs). Data prediction methods can improve the lifetime of the network and reduce congestion by reducing the amount of unnecessary data transmission. This paper proposes the use of a Sequence to Sequence (Seq2Seq) model with an attention mechanism for the case of multivariate time series (MTS) prediction in a WSN. First, a basic Seq2Seq model is introduced, and then the Attention mechanism. The model is compared with others using different error metrics in prediction. Finally, a measure of the reduction in data transmission is given, considering different error thresholds. Results show that the model can save a considerable amount of data in transmission and still maintain a good representation of the measured data.
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收藏
页数：5
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