The imminent growth of user-centric, pervasive sensing environments promotes sink mobility in an increasing num- ber of event-based, sensor network applications including rescue missions, intrusion detection and smart buildings. In these settings, one of the most critical challenges to- ward supporting quality of service, is effective distributed congestion avoidance. Congestion control techniques have been proposed in sensor networks mostly in the context of a static sink. In our work, we study the problem of conges- tion avoidance in the context of sensor networks with a mo- bile sink. Under sink mobility, various new challenges arise that need to be effectively addressed. Adaptation to sink mobility requires agile as well as effective load estimation techniques. In addition, unlike static networks, path relia- bility often fluctuates due to path reconfigurations. Thus, in- jecting traffic during transient periods of poor path quality might wastefully detain network resources. Our approach jointly considers the network load and path quality varia- tions to facilitate intelligent, mobility-adaptive rate regula- tion at the sources. We implemented our techniques over a Mica2 testbed and through extensive experimental results, we demonstrate the working and benefits of our approach.