Abstract / truncated to 115 words (read the full abstract)

In today's society, we are flooded with massive volumes of data in the order of a billion gigabytes on a daily basis from pervasive sensors. It is becoming increasingly challenging to locally store and transport the acquired data to a central location for signal/data processing (i.e., for inference). To alleviate these problems, it is evident that there is an urgent need to significantly reduce the sensing cost (i.e., the number of expensive sensors) as well as the related memory and bandwidth requirements by developing unconventional sensing mechanisms to extract as much information as possible yet collecting fewer data. The first aim of this thesis is to develop theory and algorithms for data reduction. We develop ... toggle 8 keywords

sparse sensing sensor networks sampling estimation detection filtering localization synchronization

Information

Author
Chepuri, Sundeep Prabhakar
Institution
Delft University of Technology
Supervisors
Publication Year
2016
Upload Date
Feb. 2, 2016

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