Balanced aggregation trees for routing correlated data in wireless sensor networks

In a sensor network, the data collected by different sensors are often correlated because they are observations of related phenomena. This property has prompted many researchers to propose data centric routing to reduce the communication cost. In this paper, we design heuristic algorithms for combined routing and source coding with explicit side information. We build a data rate model upon the observation that in many physical situations the side information that provides the most coding gain comes from a small number of nearby sensors. Based on this model, we formulate a problem to determine the optimal routes for transmitting data to the fusion center. The overall optimization is NP hard because it has minimum Steiner tree as a sub-problem. We then propose a heuristic algorithm that is inspired by balanced trees that have small total weights and reasonable distance from each sensor to the fusion center. The average performance of the algorithm is analyzed and compared to other routing methods through simulations