A Kalman Filter Based Link Quality Estimation Scheme for Wireless Sensor Networks

Communication among wireless sensor nodes that employ cheap low-power transceivers is often very sensitive to the variations of the wireless channel. Sensor network routing protocols thus strive to continually adapt to temporal variations in wireless links in order to avoid wasteful transmissions over low-quality links. Such adaptive routing protocols must rely on a scheme that can not only accurately estimate the quality of wireless links in terms of a quantitative measure, such as the packet success rate (PSR), but also quickly adapt to temporal dynamics of the links. Traditionally, the PSR is estimated from the fraction of successful transmissions over a window of test- packets. However, we demonstrate that counting based methods do not react to changes in the wireless channel fast enough and that the only way to address this problem is to estimate the PSR based on the receiver´s characteristics and on the signal to noise ratio (SNR) at the receiver. We thus propose a scheme that uses a pre-calibrated SNR-PSR relationship and instantaneous SNR estimates to calculate the PSR of the link. In our scheme, each receiver continuously tracks the SNR using a Kalman Filter to minimize the estimation error and uses a locally available SNR- PSR curve to estimate the PSR. Through extensive experiments we demonstrate that our scheme adapts to variations in the channel faster than counting-based PSR estimators and that it also provides better PSR estimates than these counting-based approaches.