In this paper, we develop a distributed compression technique that has low decoding and encoding computational complexity. The proposed scheme exploits both temporal and spatial correlations between nodes in distributed sensor networks. In case of events occurring, the values of both spatial and temporal might change and the compression technique needs to adjust its rate to the changes automatically. Our proposed algorithm reactively changes its compression rate to adapt to the variations in the correlations. This algorithm uses the well-known compressive sensing algorithm to exploit the spatial correlation. Rate less codes were adopted to generate the measurements. The number of measurements are adjusted based on the temporal correlations among sensors. When sensor readings are changing slowly, the compression rate is improved by reducing the number of measurements. In case of any event that significantly changes the signal readings, the algorithm generates more measurements to guarantee recovery of signal at the base station. The experimental results done over data gathered by 64 temperature sensors and also Matlab simulation results reveal that our algorithm is flexible to adapt the variations in the sensor readings, while it keeps the compression rate the minimum.
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