Abstract
Compressive sensing (CS) is a novel approach to achieve much lower sampling rate for sparse signals. In order to reduce the number of data transmissions and save more energy, we apply CS theory to gather and reconstruct the sparse signals in energy-constrained large-scale wireless sensor network(WSN).Instead of sending full pair-wise measurement data to a sink, each sensor transmits only a small number of compressive measurements. The processes of CS aggregation in WSN are given. The relationship between Observations and reconstruct MSE are also discussed. Simulation result shows that our scheme can recovery the unknown data accurately as well as reduce global scale cost.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
