Distributed source coding in dense sensor networks

TL;DR

This paper investigates efficient data encoding strategies for dense sensor networks monitoring Gaussian fields, demonstrating that constant-rate schemes can achieve accurate field reconstruction regardless of the number of sensors.

Contribution

It introduces a novel two-stage approach with distributed coding and scalar quantization schemes that maintain constant total data rates independent of sensor count.

Findings

Distributed coding achieves bounded total rate for given fidelity.

Scalar quantization scheme also attains constant rate, simpler to implement.

Both schemes are effective for dense sensor network field reconstruction.

Abstract

We study the problem of the reconstruction of a Gaussian field defined in [0,1] using N sensors deployed at regular intervals. The goal is to quantify the total data rate required for the reconstruction of the field with a given mean square distortion. We consider a class of two-stage mechanisms which a) send information to allow the reconstruction of the sensor's samples within sufficient accuracy, and then b) use these reconstructions to estimate the entire field. To implement the first stage, the heavy correlation between the sensor samples suggests the use of distributed coding schemes to reduce the total rate. We demonstrate the existence of a distributed block coding scheme that achieves, for a given fidelity criterion for the reconstruction of the field, a total information rate that is bounded by a constant, independent of the number $N$ of sensors. The constant in general depends on the autocorrelation function of the field and the desired distortion criterion for the sensor samples. We then describe a scheme which can be implemented using only scalar quantizers at the sensors, without any use of distributed source coding, and which also achieves a total information rate that is a constant, independent of the number of sensors. While this scheme operates at a rate that is greater than the rate achievable through distributed coding and entails greater delay in reconstruction, its simplicity makes it attractive for implementation in sensor networks.