CAREER: Information-Scaling Laws, ``Bit-conservation'' Principles, and Robust Coding Architectures in Sensor Networks

Trustees Of Boston University, Boston

Investigators

Abstract

With a vision for architecting the next generation video-based sensor networks capable of real-time ``superresolution imaging'' using a large distributed network of poor-resolution wireless cameras, this project introduces new paradigms for distributed sampling and video coding that lie in the frontier of signal processing and network information theory. The new data-acquisition, compression, and inference methods developed in this project are expected to influence the architecture and evolution of large-scale wireless sensor networks and advance the state-of-the-art in applications requiring active monitoring of telemetry data such as surveillance, homeland security, intelligent transportation, and environmental monitoring. This project 1) studies fundamental performance limits and constructive approaches for integrating poor device precision and large-scale deployment with distributed sampling and compression using new results in nonharmonic Fourier analysis, dithered sampling, and Wyner-Ziv coding and 2) develops new real-time distributed video-coding architectures that are amenable to flexible distribution of processing complexity and sensing resolution and are robust to information loss and correlation uncertainty. A novel ``bit-conservation'' principle is introduced for characterizing the tradeoffs between sensor precision and sensor density in distributed sampling. The research effort is complemented by an educational effort to train young engineers to become skilled in the design and development of distributed information systems through 1) curriculum development, with a new course on distributed information processing and communication having an emphasis on raw-research and project development, 2) the compilation of a corpus of striking toy research puzzles illustrating fundamental concepts in distributed signal processing and network information theory integrated into the undergraduate and graduate curricula, and 3) the creation of a new introductory graduate-level text-book on information theory in distributed signal processing.