Facilitating Congestion Avoidance in Sensor Networks with a Mobile Sink

The imminent growth of user-centric, pervasive sensing environments promotes sink mobility in an increasing number of event-based, sensor network applications including rescue missions, intrusion detection and smart buildings. In these settings, one of the most critical challenges toward 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 congestion avoidance in the context of sensor networks with a mobile 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 reliability often fluctuates due to path reconfigurations. Thus, injecting traffic during transient periods of poor path quality might wastefully detain network resources. Our approach jointly considers the network load and path quality variations to facilitate intelligent, mobility-adaptive rate regulation 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.