A WSN-based prediction model of microclimate in a greenhouse using an extreme learning approach

Liu, Qi; Jin, Dandan; Shen, Jian; Fu, Zhangjie; Linge, Nigel

doi:10.1109/icact.2015.7224772

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…W c and U c are the weights of the alternative new state, and * is the Hadamard product. Equations (9) and (10) are the output gate functions. Firstly, the sigmoid layer is used to determine the state of the cells to be output, then the updated cell state is processed by the tanh layer.…”

Section: Methodsmentioning

confidence: 99%

“…Then, they used the Nonparametric Belief Propagation technique for prediction of the areas that would be visited and those that would not in the future. Liu et al [9] proposed a microclimate data prediction model based on the extreme learning machine. The model is oriented to improve the prediction speed while ensuring accurate prediction.…”

Section: Related Workmentioning

confidence: 99%

“…Using data prediction methods to reduce unnecessary data transmission is an effective way to improve the quality of data collection and increase the network lifetime. The current methods usually use the periodicity and redundancy to predict the specific sensory data based on historical data, which often results in low prediction stability and biased predictions [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data prediction model in wireless sensor networks based on bidirectional LSTM

Cheng

Xie

et al. 2019

J Wireless Com Network

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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 prediction methods of wireless sensor networks seldom consider how to utilize the spatial-temporal correlation among the sensory data. This paper has proposed a new data prediction method multi-node multi-feature (MNMF) based on bidirectional long short-term memory (LSTM) network. Firstly, the data quality is improved by quartile method and wavelet threshold denoising. Then, the bidirectional LSTM network is used to extract and learn the abstract features of sensory data. Finally, the abstract features are used in the data prediction by adopting the merge layer of the neural network. The experimental results show that the proposed MNMF model has better performance compared with the other methods in many evaluation indicators.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%