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- Title
Information-Based Node Selection for Joint PCA and Compressive Sensing-Based Data Aggregation.
- Authors
Imanian, Gholamreza; Pourmina, Mohammad Ali; Salahi, Ahmad
- 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.
- Subjects
WIRELESS sensor networks; PRINCIPAL components analysis; ACQUISITION of data; ENERGY consumption
- Publication
Wireless Personal Communications, 2021, Vol 118, Issue 2, p1635
- ISSN
0929-6212
- Publication type
Article
- DOI
10.1007/s11277-021-08108-9