GAMPS: compressing multi sensor data by grouping and amplitude scaling
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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Motivated by sensor networks, we consider the fusion storage of correlated sources in a database, such that any subset of them may be efficiently retrieved in the future. Only statistical information about future queries is available during encoding and storage. Fusion coding of correlatedsources poses new challenges due to the conflicting objectives of exploiting inter-source correlations and enabling efficient {\em selective} retrieval. Practical signal compression imposes additional constraints on system complexity. We propose a shared-descriptions approach for the design of lossy fusion coding systems, to managethe precise tradeoffs between storage rate, retrieval rate,distortion and system complexity, within one unified framework. An iterative descent algorithm is derived for the design of such fusion coders. The optimized system provides significant gains over traditional quantization techniques that are not directly optimized for fusion coding.