Dynamic Forwarding over Tree-on-DAG for Scalable Data Aggregation in Sensor Networks

Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Moreover, structured approaches are sensitive to the waiting time that is used by nodes to wait for packets from their children before forwarding the packet to the sink. Although structureless approaches can address these issues, the performance does not scale well with the network size. We propose tree on DAG (ToD), a semistructured approach that uses dynamic forwarding on an implicitly constructed structure composed of multiple shortest path trees to support network scalability. The key principle behind ToD is that adjacent nodes in a graph will have low stretch in one of these trees in ToD, thus resulting in early aggregation of packets. Based on simulations on a 2,000-node network and real experiments on a 105-node Mica2-based network, we conclude that efficient aggregation in large-scale networks can be achieved by our semistructured approach.