| Title | Joint Correlated Compressive Sensing based on Predictive Data Recovery in WSNs |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | K, S., Devi, K. Suganya, Srinivasan, P., Dheepa, T., Arpita, B., singh, L. Dolendro |
| Conference Name | 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) |
| Keywords | composability, compressive sampling, compressive sensing, Cyber-physical systems, Joint Correlation, Kronecker compressive sensing, Predictive Recovery, privacy, pubcrawl, Resiliency, Wireless Sensor Network |
| Abstract | Data 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. |
| DOI | 10.1109/ic-ETITE47903.2020.181 |
| Citation Key | k_joint_2020 |
Joint Correlated Compressive Sensing based on Predictive Data Recovery in WSNs