BARD: Bayesian-Assisted Resource Discovery In Sensor Networks. Stann, F. & Heidemann, J. Technical Report ISI-TR-2004-593, USC/Information Sciences Institute, July, 2004.
BARD: Bayesian-Assisted Resource Discovery In Sensor Networks [link]Paper  abstract   bibtex   
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 modelling 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 arbitrary value sets via Bayesian estimation. Results of occasional flooded queries establish a baseline probability distribution, which is used to focus additional queries. Since this process is probabilistic and approximate, even partial matches from prior searches can still reduce the scope of search. We evaluate the benefits of BARD by extending directed diffusion and examining control overhead with and without our Bayesian filter. These simulations demonstrate a 28% to 73% reduction in control traffic, depending on the number and locations of sources and sinks.
@TechReport{Stann04a,
	author = 	"Fred Stann and John Heidemann",
	title = 	"BARD: Bayesian-Assisted Resource Discovery In Sensor Networks",
	institution = 	"USC/Information Sciences Institute",
	year = 		2004,
	sortdate = "2004-05-01",
	project = "ilense, scadds, whumls",
	jsubject = "chronological",
	number =	"ISI-TR-2004-593",
	month =		jul,
	location =	"johnh: pafile",
	keywords =	"bard, baysian, diffusion routing",
	url =		"http://www.isi.edu/%7ejohnh/PAPERS/Stann04a.html",
	pdfurl =	"http://www.isi.edu/%7ejohnh/PAPERS/Stann04a.pdf",
	otherurl =	"http://www.isi.edu/%7efstann/papers/ISI-TR-2004-593.p",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	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 modelling 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 arbitrary value
sets via Bayesian estimation. Results of occasional flooded queries
establish a baseline probability distribution, which is used to focus
additional queries. Since this process is probabilistic and
approximate, even partial matches from prior searches can still reduce
the scope of search. We evaluate the benefits of BARD by extending
directed diffusion and examining control overhead with and without our
Bayesian filter. These simulations demonstrate a 28\% to 73\%
reduction in control traffic, depending on the number and locations of
sources and sinks.
",
}

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