CIF: Small: Data Reduction for Networked Inference

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Technological advances in devices, computing, communication, and networking have allowed the development and deployment of networked sensing systems of varying scales with diverse applications. The voluminous data collected in these networked systems, however, present some unique challenges. Ensuring that useful data be available at the right location in a timely fashion is difficult in a large scale network because of various limitations inherent in such systems. In addition, the collected data in its raw form often lacks clarity for making an informed decision. The primary goal of the project is to develop theory and methodology that allow the raw data to be reduced to the extent such that 1) their transmission to the desired destination is compliant to the system limitation, and 2) there is no loss of information with respect to the mission of the networked system. The project pursues a comprehensive treatment of data reduction in such inference networks and aims to develop the sufficiency principle and related theory and methods to overcome these challenges. Intimate connection between data reduction for inference and data compression for transmission is exploited and expanded in the network setting. New interpretations using the sufficiency principle for some classical multi-terminal source coding problems will be developed; they provide a new venue for the exploration of the connection between statistics and information theory. The development of theory and methods that guide data reduction in a networked inference system also has the potential to unify existing works that are tailored toward specific inference problems.