CCSS: Collaborative Research: Sketching for High Dimensional Data Analysis in IoT

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The proliferation of IoT devices generates enormous amount of data, which leads to a tremendous demand on resources for the transmission, processing and storage. In order to extract useful information and fully achieve the potential of IoT for intelligent decisions, it is critical to develop novel systems to efficiently transmit, store and process this large volume of data. Fortunately, the data generated by IoT and many other applications typically possesses certain low dimensional parsimonious structures. Leveraging on these low dimensional structures of IoT data, this project will develop methods based on low dimensional sketches to infer information of interest. This sketching based framework will enable quick and accurate information extraction with greatly reduced sampling rates, transmission and storage costs.This project will develop a sketching based framework for the efficient processing of high dimensional data generated by IoT and investigate its theoretical and algorithmic properties. The core idea is to take low dimensional projections or sketches of random variables, rather than to directly and fully observe high dimensional random variables. This project will develop both non-adaptive and adaptive low dimensional sketching methods, and will make the following intellectual contributions: 1) deriving fundamental limits on sampling rates for recovering statistical information of high dimensional random variables; 2) designing explicit sampling schemes that achieve the corresponding fundamental limits on sampling rates; 3) providing fast algorithms with performance guarantees for recovering statistical information from sketches of random variables. The PIs will leverage their research experience in compressed sensing, low-rank matrix recovery and sequential analysis in solving these challenging problems. The generated results are expected to extend compressed sensing from sketching of deterministic values to a broader context of sketching of random variables.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.