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Fig. 1 Sampling jitter.
Data cleaning includes the missed data patching, sampling jitter correction, and outliers and correction.
The impact and mitigation of sampling jitter on self-interference suppression was considered in [28,29].
The inaccuracy of data due to node sampling jitter is eliminated with regular sampling of WSN datasets.
The influence of the channel cyclic short-term variations and the sampling jitter on the system performance is assessed.
This result can be interpreted as a robust stability analysis with respect to arbitrary time-varying sampling intervals, which may be useful in the case of uncontrolled sampling, or in the presence of phenomenon such as sampling jitter.
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Similar to the above experiment, the sample jitter is eliminated twice on the same dataset in order to guarantee that the time-dependent indicator of the dataset gradually increases.
As shown in Fig. 6, the cleaning process by eliminating the sample jitter will enhance the time-dependent as well as the correctness indicator, while the completeness indicator remains unchanged.
The cleaning costs of the two cleaning strategies are the same and abnormal data detection and correction, missing data mending, and linear interpolation cleaning operation for eliminating sample jitter are respectively performed.
As we can see from the first value in Fig. 8, there are many errors, such as data loss, gross error, and sample jitter in dataset D of node 7. The quality metrics Q is 65.34%.
Elimination of sample jittering is mainly used for the time-related indicators, while missing data processing for data integrity indicators.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com