Traffic Management 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 traffic management 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. In this work, we first study the effect of sink mobility on traffic load in sensor networks. We then propose adaptive routing as well as load estimation techniques that effectively adapt to sink relocations. A novel aspect of our approach is that it jointly considers the network load as well as path quality variations to facilitate intelligent, mobility-adaptive rate regulation at the sources. We provide a thorough study of the trade-offs induced due to persistent path quality variations and conduct extensive real MICA2-based testbed experiments to study the performance of the sensor network under sink mobility.