A new scheme on link quality prediction and its applications to metric-based routing

Efficient routing in wireless sensor networks entails the establishment of high quality links. Recent research has shown that metric-based routing, such as ETX [6]can significantly improve routing performance by tracking various link-quality metrics. Such metrics, however, may fail to capture link quality at relatively high traffic rates. This poster describes how machine learning techniques can be leveraged to help estimate link quality in those adverse scenarios. We also present MetricMap, a metric-based protocol using our learning-enabled link quality assessment method. Experimental results on MoteLab show that MetricMap can achieve up to 400% improvement on data delivery rate in a high traffic rate application, with no negative impact on other performance metrics, such as data latency.