Visible to the public Joint Correlated Compressive Sensing based on Predictive Data Recovery in WSNs

TitleJoint Correlated Compressive Sensing based on Predictive Data Recovery in WSNs
Publication TypeConference Paper
Year of Publication2020
AuthorsK, S., Devi, K. Suganya, Srinivasan, P., Dheepa, T., Arpita, B., singh, L. Dolendro
Conference Name2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE)
Keywordscomposability, compressive sampling, compressive sensing, Cyber-physical systems, Joint Correlation, Kronecker compressive sensing, Predictive Recovery, privacy, pubcrawl, Resiliency, Wireless Sensor Network
AbstractData sampling is critical process for energy constrained Wireless Sensor Networks. In this article, we proposed a Predictive Data Recovery Compressive Sensing (PDR-CS) procedure for data sampling. PDR-CS samples data measurements from the monitoring field on the basis of spatial and temporal correlation and sparse measurements recovered at the Sink. Our proposed algorithm, PDR-CS extends the iterative re-weighted -ℓ1(IRW - ℓ1) minimization and regularization on the top of Spatio-temporal compressibility for enhancing accuracy of signal recovery and reducing the energy consumption. The simulation study shows that from the less number of samples are enough to recover the signal. And also compared with the other compressive sensing procedures, PDR-CS works with less time.
Citation Keyk_joint_2020