Abstract

Data dissemination in sensor networks requires four components: resource discovery, route establishment, packet forwarding, and route maintenance. Resource discovery can be the most costly aspect if meta-data does not exist to guide the search. Geographic routing can minimize search cost when resources are defined by location, and hash-based techniques like data-centric storage can make searching more efficient, subject to increased storage cost. In general, however, flooding is required to locate all resources matching a specification. In this paper, we propose BARD, Bayesian-assisted resource discovery, an approach that optimizes resource discovery in sensor networks by modeling search and routing as a stochastic process. BARD exploits the attribute structure of diffusion and prior routing history to avoid flooding for similar queries. BARD models attributes as random variables and finds routes to …

Date

March 13, 2005

Authors

Fred Stann, John Heidemann

Conference

Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies.

Volume

2

Pages

866-877

Publisher

IEEE