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Baljeet Malhotra Supervisors Ioanis Nikolaidis Mario A. Nascimento

Query Processing in WSN: The Dilemma of Suppressions and Failures. Baljeet Malhotra Supervisors Ioanis Nikolaidis Mario A. Nascimento Communication Networks and Database Systems Computing Science Department Graduate Student’s Workshop on Network’s Research, Nov 17, 2009.

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Baljeet Malhotra Supervisors Ioanis Nikolaidis Mario A. Nascimento

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  1. Query Processing in WSN: The Dilemma of Suppressions and Failures Baljeet Malhotra Supervisors Ioanis Nikolaidis Mario A. Nascimento Communication Networks and Database Systems Computing Science Department Graduate Student’s Workshop on Network’s Research, Nov 17, 2009

  2. In-Network Query Processing in WSN In-network Processing Query Result Decision Support system

  3. Background • Queries • Variations of Top-k (Min, Max, Avg.) • Nearest Neighbor • Join queries • Techniques • Exploit the wireless broadcast nature of WSN • Aggregation, Pruning, Filtering …. • Schedules • Sensors should not be in ‘idle listening mode” • Contentious our slotted time • Failures • Unreliable communication • ?

  4. Top-k Query : Problem Definition Given a set of N sensors, S = {si : i =1, 2,….., N} Sp,jbe the set of sensors that produced the pth highest value during the jthround v(Sp,j) be the pth highest value The problem is to find: Top-k values: Di = {v(Sp,j): p =1, 2,….., k} Top-k sensors: Sp,j

  5. Top-k Query Processing Sink = Root A Shortest Path Tree (SPT)

  6. Convergecast + Aggregation = TAG* Sink = Root A *TAG, Madden et. al., OSDI’02, 2002

  7. Convergecast + Aggregation Sink = Root A Total # of messages used = n-1

  8. Convergecast + Aggregation + Filtering Sink = Root A τ (threshold) = min of top-k

  9. Threshold (τ) Broadcast Sink = Root A

  10. Convergecast + Aggregation + Filtering Sink = Root A send iff v(si) ≥ τ; or if top-k node

  11. Broadcast via SPT Sink = Root A Total # of messages used = 8

  12. Broadcast via DST Sink = Root A Dominating Set Tree (DST) Total # of messages used = 4

  13. Some Results Varying k Tree topology makes a difference EXTOK performs better on both synthetic and Intel dataset Synthetic data Real data

  14. Convergecast Scheduling Sink = Root A TDMA slots => Precedence Constraints

  15. Convergecast Scheduling Sink = Root A 7 6 8 5 4 5 3 2 3 1 TDMA slots => Conflict Free + Precedence Constraints

  16. Some Results Varying Ψ (node density) SDA, Chen et. al, LNCS, vol. 3799, 2005 SAS, Wan, et. al., MOBIHOC, 2009 DAS, B. Yu et. al., INFOCOM, 2009 PAS, X. Yu et. al., SSDB, 2007 Synthetic data Real data

  17. Convergecast Scheduling + Filtering Sink = Root A 7 6 8 5 4 5 3 2 3 1 send iff v(si) ≥ τ; or if top-k node

  18. Convergecast Scheduling + Filtering + Failure Sink = Root A 7 6 8 5 4 5 3 2 3 1 send iff v(si) ≥ τ; or if top-k node

  19. Convergecast Scheduling + Failure Recovery Sink = Root A 7 6 8 5 4 5 3 2 3 1

  20. Some Results Application Perspective Just Brodcast

  21. Conclusions • Infrastructure • Clusters and Routing Trees • Dominating Sets • Techniques • Aggregation, Pruning, and Filtering • How to use them in the best possible way for a particular problem ? • Can we use one single infrastructure for every thing ? • Schedules • Every task must be done in a systematic fashion while minimizing the response time • Failures • What happens when parts of our infrastructure breaks down ? • How and When to fix these problems ?

  22. Acknowledgements • This research is partially supported by NSERC, i-Core, FGSR, VLDB, GSA, and Walter John Scholarship. • Contact baljeet@cs.ualberta.ca for more details. THANK YOU !

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