Open AccessIntegration of Compressive Sensing and Clustering in Wireless Sensor Networks using Block Tridiagonal Matrix Method
Author(s) -
Venkat Rao Pasupuleti,
Ch. Balaswamy
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f1220.0886s219
Subject(s) - wireless sensor network , compressed sensing , cluster analysis , computer science , block (permutation group theory) , tridiagonal matrix , matrix (chemical analysis) , data mining , energy (signal processing) , basis (linear algebra) , diagonal , algorithm , real time computing , machine learning , computer network , mathematics , eigenvalues and eigenvectors , physics , geometry , materials science , statistics , quantum mechanics , composite material
The most dominant applications of wireless sensor networks (WSNs) is Environmental monitoring, it generally needs long time to operate. Although, the energy of inherent restriction has the bottle neck in scale of each WSN applications. This articler demonstrates the framework for an integration of compressive sensing and blocks tri-diagonal matrices (BDMs) for the clustering in WSNs that can be used as the matrices of measurement by the combination of data prediction that is involved with the compression and retrieval to achieve data processing precision and effectiveness in clustered WSNs simultaneously. On basis of the analysis theoretically, this can be designed for the implementation in number of algorithms. The proposed framework furnishes the real world data demonstration which can be utilized to get the simulation results for a solution of cost effective for the applications on basis of cluster in WSNs for environmental monitoring