A Rate-Distortion Based Aggregation Method Using Spatial Correlation for Wireless Sensor Networks

摘要

Today we are witnessing an amazing growth of wireless sensor networks due to many factors including but limited to reducing cost of semiconductor components, rapid deployment of wireless networks, and attention to low-power aspect that makes these networks suitable for energy sensitive applications to a large extent. The power consumption requirement has raised the demand for the new concepts such as data aggregation. Data correlation plays an important role in an efficient aggregation process. This paper introduces a new correlation-based aggregation algorithm called RDAC (Rate Distortion in Aggregation considering Correlation) that works based on centralized source coding. In our method, by collecting correlated data at an aggregation point while using the Rate-Distortion (RD) theory, we can reduce the load of data transmitted to the base station by considering the maximum tolerable distortion by the user. To the best of our knowledge, nobody has yet used the RD theory for the data aggregation in wireless sensor networks. In this paper, a mathematical model followed by implementations demonstrates the efficiency of the proposed method under different conditions. By using the unique features of the RD theory, the correlation matrix and observing the behavior of the proposed method in different network topologies, we can find the mathematical upper and lower bounds for the amount of aggregated data in a randomly distributed sensor network. The bounds not only determine the upper and lower limits of the data compressibility, it also makes possible the estimation of the required bit count of the network without having to invoke the aggregation algorithm. This method therefore, allows us to have a good estimation of the amount of energy consumed by the network.