A different approach to decentralized compression, compressed sensing for networked data, has been considered and revolves around large-scale distributed sources of data and their storage, transmission and retrieval. The new approach does not require any specific prior signal knowledge and is an effective strategy in each of the situations described above. It can be used to reconstruct compressible or sparse networked data in a variety of practical settings, including general multihop networks and wireless sensor networks. The reason that it is promising is that CS provides universal sampling and decentralized encoding.
|Original language||English (US)|
|Number of pages||10|
|Journal||IEEE Signal Processing Magazine|
|State||Published - Mar 2008|
Bibliographical noteFunding Information:
Robert Nowak received the B.S. (with highest distinction), M.S., and Ph.D. degrees in electrical engineering from the University of Wisconsin-Madison in 1990, 1992, and 1995, respectively. He is currently the McFarland-Bascom Professor of Engineering at the University of Wisconsin-Madison. He was an associate editor for IEEE Transactions on Image Processing and is an associate editor for ACM Transactions on Sensor Networks and the secretary of the SIAM Activity Group on Imaging Science. He was a technical program chair for the IEEE Statistical Signal Processing Workshop and the IEEE/ACM International Symposium on Information Processing in Sensor Networks. He received the General Electric Genius of Invention Award in 1993, the National Science Foundation CAREER Award in 1997, the Army Research Office Young Investigator Program Award in 1999, the Office of Naval Research Young Investigator Program Award in 2000, and IEEE Signal Processing Society Young Author Best Paper Award in 2000. His research interests include statistical signal processing, machine learning, imaging and network science, and applications in communications, biomedical imaging, and genomics.