Integration of Compressive Sensing and Clustering in Wireless Sensor Networks using Block Tridiagonal Matrix Method
Venkat Rao Pasupuleti1, Ch. Balaswamy2
1Venkat Rao Pasupuleti, Department of ECE, Lakireddy Balireddy College of Engineering, Mylavaram (Andhra Pradesh), India.
2Ch. Balaswamy, Department of ECE, Gudlavalleru Engineering College, Gudlavalleru (Andhra Pradesh), India.
Manuscript received on 15 September 2019 | Revised Manuscript received on 24 September 2019 | Manuscript Published on 10 October 2019 | PP: 901-906 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F12200886S219/19©BEIESP | DOI: 10.35940/ijeat.F1220.0886S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Compressive Sensing, Data Prediction, Environmental Monitoring, Matrix Based Compression, Wireless Sensor Networks (Wsns).
Scope of the Article: Wireless ad hoc & Sensor Networks