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

2. BARD / April 2004 2 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

3. BARD / April 2004 3 Data Dissemination in Sensor Nets Resource Discovery –Finding data matching some description –Attribute Matching Routing –Route Establishment –Packet Forwarding –Route Maintenance

4. BARD / April 2004 4 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

5. BARD / April 2004 5 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

6. BARD / April 2004 6 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

7. BARD / April 2004 7 Two-Phase Pull Diffusion Original diffusion algorithm [Intanagonwiwiat et al, 2000] 1. flood interests from sink to source 2. flood exploratory data from source back to sink 3. reinforce preferred gradient(s) from sink to source (tree) 4. send data along reinforced gradients Source Sink Additional source target (could be multiple sinks) control overhead

8. BARD / April 2004 8 Push Diffusion Make sources active to avoid one flood [NEW] flood interests from sink to source 1. flood exploratory data from source back to sink 2. reinforce preferred gradient(s) from sink to source (tree) 3. send data along reinforced gradients Source Sink Additional source target (could be multiple sinks)

9. BARD / April 2004 9 Statistical Approach Correlation in sensor networks –Real-world events create patterns over time –Implicit geography

10. BARD / April 2004 10 Modeling Resource Discovery The Joint Probability Distribution (“joint”) Grows Exponentially

11. BARD / April 2004 11 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

12. BARD / April 2004 12 Bayesian Approach cont… Bayes’ requires conditional independence P[A  N 3 ] = P[A  N 3  S] 

13. BARD / April 2004 13 Implemented as a Diffusion Filter The Filter Architecture in Diffusion, allows BARD to be a selectable service.

14. BARD / April 2004 14 BARD Filter Pre-Processing

15. BARD / April 2004 15 BARD Filter Limited Routing

16. BARD / April 2004 16 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

17. BARD / April 2004 17 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

18. BARD / April 2004 18 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

19. BARD / April 2004 19 Increasing Node Count & Area Simple push overhead grows faster than BARD –45%  53% improvement in control byte overhead

20. BARD / April 2004 20 Increasing Node Density Hop count doesn’t increase, so efficiency increases –62%  73% improvement in control byte overhead

21. BARD / April 2004 21 Complex Example Relative position of sources and sinks matters –28%  47% improvement in control byte overhead

22. BARD / April 2004 22 Increasing Send Rate Control amortizes (convergent) with event count Total transmissions affected by alternate paths

23. BARD / April 2004 23 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

24. BARD / April 2004 24 Ongoing Work More Comprehensive testbed Experiments Testing with limited attribute intersection Complete matching rules

25. BARD / April 2004 25 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