Scalable data aggregation for dynamic events in sensor networks

  • Authors:

  • Kai-Wei Fan;Sha Liu;Prasun Sinha

  • Affiliations:

  • The Ohio State University;The Ohio State University;The Ohio State University

  • Venue:
  • Proceedings of the 4th international conference on Embedded networked sensor systems
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 which 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 a semi-structured approach that uses a structureless technique locally followed by Dynamic Forwarding on an implicitly constructed packet forwarding structure to support network scalability. The structure, ToD, is composed of multiple shortest path trees. After performing local aggregation, nodes dynamically decide the forwarding tree based on the location of the sources. 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 2000 nodes network and real experiments on a 105 nodes Mica2-based network, we conclude that efficient aggregation in large scale networks can be achieved by our semi-structured approach.