Joint Correlated Compressive Sensing based on Predictive Data Recovery in WSNs
- Resource Type
- Conference
- Authors
- K, Sekar; Suganya Devi, K; Srinivasan, P; Dheepa, T; Arpita, Banik; Dolendro singh, L
- Source
- 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020 International Conference on. :1-5 Feb, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
General Topics for Engineers
Signal Processing and Analysis
Wireless communication
Wireless sensor networks
Prediction algorithms
Minimization
Market research
Iterative algorithms
Sensors
Wireless Sensor Network
Joint Correlation
Compressive sensing
Predictive Recovery
Kronecker compressive sensing
- Language
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 $-\ell_{1}(IRW-\ell_{1})$ 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.