Information-Based Node Selection for Joint PCA and Compressive Sensing-Based Data Aggregation

Abstract

Recently it has been shown that when Principal Component Analysis is applied as a dictionary learning technique to Compressive Sensing-based data aggregation, using a Deterministic Node Selection method for data collection in Wireless Sensor Networks can outperform Random Node Selection ones. In this paper, a new scheduling method for selection of measured nodes in a data collection round, called “Information-Based Deterministic Node Selection”, is proposed. Simulation results for synthetic and real data sets show that the proposed method outperforms a reference DNS method in terms of energy consumption per reconstruction error. Correlation (or covariance) matrix estimation is necessary for DNS strategies which are accomplished by gathering data from all network nodes in a few initial time slots of collection rounds. In this regard, we also propose the use of a particular type of shrinkage estimator in preference to the standard correlation matrix estimator. With the aid of the new estimator, we can obtain data correlations with the same accuracy of standard estimator while we need less number of observations. Our numerical experiments demonstrate that when the number of measured nodes is less than 50% of the total nodes, using shrinkage estimator causes extra energy savings in sensor nodes.

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Notes

  1. 1.

    If actual data of nodes are available in any previous time slot we utilize these values instead of reconstructed data.

  2. 2.

    The value of H should not be great for any non-stationary signal. As shown in reference [18], the best results are obtained for the real signals by selecting the number 2 for H.

  3. 3.

    Refer to Appendix A in reference [22] for detailed information on how to calculate the quantity \(\mathop {Var}\limits ^{\wedge } (s_{ij} ) \)

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Correspondence to Mohammad Ali Pourmina.

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Imanian, G., Pourmina, M.A. & Salahi, A. Information-Based Node Selection for Joint PCA and Compressive Sensing-Based Data Aggregation . Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08108-9

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Keywords

  • Wireless sensor networks
  • Compressive data aggregation
  • Dictionary learning
  • Shrinkage estimator