Improving the Energy Efficiency of Directed Diffusion Using Passive Clustering
Directed diffusion is a prominent example of data-centric routing based on application layer data and purely local interactions. In its functioning it relies heavily on network-wide flooding which is an expensive operation, specifically with respect to the scarce energy resources of nodes in wireless sensor networks (WSNs).
One well-researched way to curb the flooding overhead is by clustering. Passive clustering is a recent proposal for on-demand creation and maintenance of the clustered structure, making it very attractive for WSNs and directed diffusion in particular.
The contribution of this paper is the investigation of this combination: Is it feasible to execute directed diffusion on top of a sensor network where the topology is implicitly constructed by passive clustering?
A simulation-based comparison between plain directed diffusion and one based on passive clustering shows that, depending on the scenario, passive clustering can significantly reduce the required energy while maintaining and even improving the delay and the delivery rate. This study also provides insights into the behavior of directed diffusion with respect to its long-term periodic behavior, contributing to a better understanding of this novel class of communication protocols.
Unable to display preview. Download preview PDF.
- 5.Silva, F., Heidemann, J., Govindan, R.: Network routing application programmer’s interface (API) and walk through 9.0.1. Technical report, USC/ISI (2002)Google Scholar
- 6.UCB/LBNL/VINT: Network simulator - ns-2, http://www.isi.edu/nsnam/ns/
- 9.Xu, Y., Bien, S., Mori, Y., Heidemann, J., Estrin, D.: Topology control protocols to conserve energy in wireless ad hoc networks. Technical Report 6, University of California, Los Angeles, Center for Embedded Networked Computing (2003) (submitted for publication)Google Scholar
- 10.Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proc. of the 33rd Hawaii Intl. Conf. on System Sciences (2000)Google Scholar