bard / april 2004 1 bard: bayesian-assisted resource discovery fred stann (usc/isi) joint work with...
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BARD / April 20041
BARD: Bayesian-AssistedResource Discovery
Fred Stann (USC/ISI)
Joint Work With
John Heidemann (USC/ISI)
April 9, 2004
BARD / April 20042
Motivation
• Problem: Efficiency of Data Dissemination in Sensor Networks
– Data producers and data consumers must connect with each other
– Exhaustive search (a.k.a. flooding) required• In lieu of meta-data or a priori knowledge
• Solution: BARD uses Bayesian techniques – Use prior distribution to limit flooding
BARD / April 20043
Data Dissemination in Sensor Nets
• Resource Discovery– Finding data matching some
description– Attribute Matching
• Routing– Route Establishment– Packet Forwarding– Route Maintenance
BARD / April 20044
Name-Based vs. Attribute-Based Routing
• IP & Ad Hoc Routing– Name-based routing with Resource Discovery
layered on top (e.g. DNS, Google)
• Diffusion– Attribute-based routing combined with
Resource Discovery
BARD / April 20045
Related Work
• Route Caching (DSR, AODV)– Cached paths are refreshed as needed
• Data Centric Storage (DCS/GHT)– Hash to location aware nodes
• Geographic Assist (GEAR)– Greedy forwarding toward target
• Target Tracking (Spatio-Temporal Mcast)– Predict target path and delivery zone
• Probabilistic (Gossip)– Forwarding with fixed probability
BARD / April 20046
Related Work Summary
• Each technique works well for a subset of the problem space comprised of all diffusion applications
• We desired a more general approach
BARD / April 20047
Two-Phase Pull Diffusion
• Original diffusion algorithm [Intanagonwiwiat et al, 2000]
1. flood interests from sink to source2. flood exploratory data from source back to sink3. reinforce preferred gradient(s) from sink to source (tree)4. send data along reinforced gradients
Source
Sink
Additional source
target
(could bemultiple sinks)
controloverhead
BARD / April 20048
Push Diffusion
• Make sources active to avoid one flood [NEW] flood interests from sink to source1. flood exploratory data from source back to sink2. reinforce preferred gradient(s) from sink to source (tree)3. send data along reinforced gradients
Source
Sink
Additional source
target
(could bemultiple sinks)
BARD / April 20049
Statistical Approach
• Correlation in sensor networks– Real-world events create patterns over time
– Implicit geography
X
Road
N1
N2
N3
N4
BARD / April 200410
Modeling Resource Discovery
• The Joint Probability Distribution (“joint”)
• Grows Exponentially
Node N4
Node N3
Node N2
Node N1
AcousticSeismic
BARD / April 200411
Bayesian Approach
• Combine prior probability with a sample.– Keep track of reinforcements per attribute
per neighbor as Conditional Probability Tables (CPTs)• Simpler to maintain than a joint probability
distribution.
– Current Sample• Set of attributes in exploratory packet.
– Forward to high probability neighbors
BARD / April 200412
Bayesian Approach cont…
• Bayes’ requires conditional independence
• P[AN3] = P[AN3S]
][
]3[]3|[]|3[
ASP
NPNASPASNP
]|3[ ASNP ]3|[]3|[]3[ NAPNSPNP
BARD / April 200413
Implemented as a Diffusion Filter
The Filter Architecture in Diffusion, allows BARD to be a selectable service.
Push DiffusionRouting Filter
Bayes Probability Calcutation
BARD Filter Post-Processing(Flooding Limitation)
BARD Filter Pre-Processing(History)
BARD / April 200414
BARD Filter Pre-Processing
NODENeighbor 2
ConditionalProbability
Table
Neighbor Attributes A | B | Positive Reinf
(Attr A, B, C)
Exploratory Msg
(Attr A, B
, C)
Exploratory Msg(Attr A, B, C)
Neighbor 1
BARD / April 200415
BARD Filter Limited Routing
ConditionalProbability
Table
Neighbor Attributes A | B |
Neighbor 2Exploratory Msg(Attr A, B, C)
Neighbor 1
Exploratory Msg
(Attr A, B, C)
NODE
BARD / April 200416
BARD Flooding
• Flooding When CPTs Empty– Build up CPTs
• Periodic Flooding– Updating CPTs in response to changing
conditions– Sliding time window– Compensation for Hysteresis– Low fidelity real-time events
BARD / April 200417
BARD Simulation Experiments
• Increasing node count (and area)• Increasing density• Varying the number of sources• Varying the number of sinks• Sensitivity to transmission error• Increasing send frequency• Moving target
BARD / April 200418
ns-2 Results Summary• BARD - 28% to 78% reduction in
control traffic• BARD results improve with
– Higher node counts– Greater node density– Lower send rates
• BARD results are limited by– Increased number of sources– Dispersion of sources– Higher send rates– High error rates
BARD / April 200419
Increasing Node Count & Area
• Simple push overhead grows faster than BARD– 45% 53% improvement in control byte overhead
BARD / April 200420
Increasing Node Density
• Hop count doesn’t increase, so efficiency increases– 62% 73% improvement in control byte overhead
BARD / April 200421
Complex Example
• Relative position of sources and sinks matters– 28% 47% improvement in control byte overhead
BARD / April 200422
Increasing Send Rate
• Control amortizes (convergent) with event count• Total transmissions affected by alternate paths
BARD / April 200423
Stayton Test Bed Experiment
• Results as expected– Limited routing to “thin” side 100% by BARD
– Multiple paths on “fat” side
– Ns-2 simulation had qualitatively similar results
BARD / April 200424
Ongoing Work
• More Comprehensive testbed Experiments
• Testing with limited attribute intersection
• Complete matching rules
BARD / April 200425
Conclusions
• Applications with complex on-demand queries, and low data rates can benefit
• Efficiency gain is proportional to correlation of events over time
• Ratio of flooding to limited flooding presents a tradeoff of real-time response vs. efficiency gain