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 applicationindependent 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 modeling 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 clusterbased system design can perform as well as sophisticated adaptive schemes for joint routing and compression.
Our analysis of the representative routing schemes is based on using an empirically motivated model for the joint entropy as a function of intersource distances REF and shows that there exist efficient correlation independent routing structures.
The impact of spatial correlation on routing with compression in wireless sensor networks
IPSN '04
Computer Science