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

Autor: Ahmad Salahi, Gholamreza Imanian, Mohammad Ali Pourmina
Rok vydání: 2021
Předmět:
Zdroj: Wireless Personal Communications. 118:1635-1654
ISSN: 1572-834X
0929-6212
DOI: 10.1007/s11277-021-08108-9
Popis: 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.
Databáze: OpenAIRE
Externí odkaz: