Download - BARD / April 2004 1 BARD: Bayesian-Assisted Resource Discovery Fred Stann (USC/ISI) Joint Work With John Heidemann (USC/ISI) April 9, 2004.

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 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