Abstract
This paper presents a novel systems approach to compressing sensor network data. Unlike previous data compression methods, the proposed lossless linear predictor-based sensor data compression method utilizes structural system information to minimize the signal correlation in sensor network data. In the proposed method, linear predictor is derived in a system identification framework in which auto-regressive (AR) model is used as its model structure and the instrumental variables (IV) method is used to calculate the predictor parameters. A parametric study was carried out to study the effects of changes in system property, number of sensors, and sensor noise level on the compression performance of the proposed method. Both numerical simulation and experimental results show that the proposed sensor data compression method has a better compression performance than conventional linear predictor-based data compression method for single sensor.
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