Effects of A-D conversion nonidealities on distributed sampling in dense sensor networks

We address the effect of the errors occurring at the analog-to-digital converter (ADC), from quantization noise, circuit noise, aperture uncertainty and comparator ambiguity, on the accuracy of sensor field reconstruction. We focus on the oversampling of bandlimited sensor fields in a distributed processing environment. It has previously been shown that pulse code modulation (PCM) style sampling fails to decrease the quantization error above some finite sampling rate. We show that the dither-based scheme, developed to decrease the quantization error, fails to decrease random errors associated with circuit noise, aperture uncertainty and comparator ambiguity. We propose an advanced dither-based sampling scheme with the goal of reducing both kinds of errors by increasing the density of the sensor nodes. It is based on distributing the task of improving the quantization error and random error among the nodes. The error of the scheme is shown to be O(1/r/sup 1/2 /) for oversampling rate r. The maximum energy consumption per node is O(log(r)). Finally, the bit rate of the scheme is O(1/r/sup 1/2 /log(r)) and it offers robustness to node failures in terms of a graceful degradation of reconstruction error.