Data recovery in wireless sensor networks based on attribute correlation and extremely randomized trees


In wireless sensor networks, collected data usually have a certain degree of loss and are unable to meet actual application needs due to node failures or energy limitation, etc. The current data recovery methods in wireless sensor networks focus on the usage of spatial–temporal correlation between perceptual data but seldom exploit the correlation between different attributes. This paper proposes a data recovery algorithm based on the Attribute Correlation and Extremely randomized Trees (ACET). Firstly, the Spearman’s correlation coefficient is adopted to construct the correlation model between different attributes. In case that a given attribute is lost, the correlation model is used to select other attributes that have a strong correlation with this attribute, and then take advantage of them to train the extremely randomized trees. Finally, the lost data can be recovered by the trained model. Experimental results show that the correlation between attributes can improve the effectiveness of data recovery compared with other methods.

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This work is supported by the National Natural Science Foundation of China under Grant No. 61772136 and 61370210, the Fujian Provincial Nature Science Foundation of China under Grant No. 2019J01245, the Fujian Collaborative Innovation Center for Big Data Application in Governments, the Fujian Engineering Research Center of Big Data Analysis and Processing.

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Correspondence to Zhiyong Yu.

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Cheng, H., Wu, L., Li, R. et al. Data recovery in wireless sensor networks based on attribute correlation and extremely randomized trees. J Ambient Intell Human Comput 12, 245–259 (2021).

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  • Wireless sensor network
  • Data recovery
  • Extremely randomized trees
  • Attribute correlation