Modeling link correlation in low-power wireless networks

Wireless link correlation can greatly affect the performance of wireless protocols such as flooding, and opportunistic routing. Researchers have proposed a variety of approaches to optimize existing protocols exploiting link correlation. Most existing works directly measure link correlation using packet-level transmissions and receptions. Measurement alone is insufficient because it lacks predictive power and scalability. In this paper, we present CorModel, a model for predicting link correlation in low-power wireless networks. Based on the underlying causes of link correlation, we explore four easily measurable parameters for our modeling. Besides PHY-layer parameters that previous studies have explored, we find that network-layer parameters can also have significant impact on link correlation. We validate our model and illustrate its usefulness by integrating it into existing protocols for more accurate correlation estimation. Experimental results show that our model can significantly increase the accuracy of wireless link estimation, resulting in better protocol performance.