The impact of spatial correlation on routing with compression in wireless sensor networks

The efficacy of data aggregation in sensor networks is a function of the degree of spatial correlation in the sensed phenomenon. While several data aggregation (i.e., routing with data compression) techniques have been proposed in the literature, an understanding of the performance of various data aggregation schemes across the range of spatial correlations is lacking. We analyze the performance of routing with compression in wireless sensor networks using an application-independent measure of data compression (an empirically obtained approximation for the joint entropy of sources as a function of the distance between them) to quantify the size of compressed information, and a bit-hop metric to quantify the total cost of joint routing with compression. Analytical modelling and simulations reveal that while the nature of optimal routing with compression does depend on the correlation level, surprisingly, there exists a practical static clustering scheme which can provide near-optimal performance for a wide range of spatial correlations. This result is of great practical significance as it shows that a simple cluster-based system design can perform as well as sophisticated adaptive schemes for joint routing and compression.