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Similarity-Aware Query Processing in Sensor Networks

Similarity-Aware Query Processing in Sensor Networks. Ping Xia Panos K. Chrysanthis Alexandros Labrinidis Advanced Data Management Technologies Lab University of Pittsburgh. Disasters happen… What next?. Use sensor networks to predict and mitigate disasters

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Similarity-Aware Query Processing in Sensor Networks

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  1. Similarity-Aware Query Processing in Sensor Networks Ping Xia Panos K. Chrysanthis Alexandros Labrinidis Advanced Data Management Technologies Lab University of Pittsburgh

  2. Disasters happen… What next? • Use sensor networks to predict and mitigate disasters • Use sensor networks to respond to disasters efficiently • Use sensor network data to improve response in the future ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  3. Q1 Q3. Q2 Pitfalls of Base Station Architecture ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  4. Q4 Q1: Temp>200 AND 20< Light Level <40 Q3 Q2: Temp>250 AND 25< Light Level <35 Data Centric Storage (GHT) Some queriesare similar! Consolidator-Node ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  5. Roadmap • Motivation • Similarity-Aware Query Processing • Evaluation • Conclusions ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  6. Q1: Temp>200 AND 20< Light Level <40 Q2: Temp>250 AND 25< Light Level <35 Similarity-Aware Query Processing (SAQP) O-Node I-Node Q-Node M-Node O-Node Q-Node ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  7. SAQP Algorithm Three steps: • A Q-node sends a query to an I-node. • I-node checks list of candidate O-nodes and list of candidate M-nodes, from which a set of nodes is selected as responder set. • Query is forwarded to nodes in the responder set and satisfactory events are sent from those nodes to the Q-node. ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  8. Issues • Query split • Query range [(5, 30), (10, 25)] M-View with range [(10, 25), (15, 20)] • Candidate selection • How to determine the responder set from the candidate O-nodes and candidate M-nodes? ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  9. Query split Q: r 5 10 25 30 10 r1 r2 r3 15 r4 r5 r6 20 r7 r8 r9 25 Q1: r5 --- Send to the corresponding M-node Q2: ? --- For further evaluation Solution: r1+r2+r3+r4+r6+r7+r8+r9 ? Solution: r – r5 ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  10. Candidate Selection A greedy algorithm: 1. Responder = Φ, CandidateONodes = {Onodes with satisfactory events} , CandidateMNodes = {Mnodes such that their ranges overlap with query range}. 2. If set CandidateMNodes != Φ, pick one and add it into set Responder. Meanwhile, remove some Onodes from set CandidateOnodes if their events are covered. 3. If there are energy saving, keep the change. Otherwise, undo the change. 4. Do step 2 and 3 until CandidateMNode = Φ 5. Responder = Responder + (remaining) CandidateONodes ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  11. Roadmap • Motivation • Similarity-Aware Query Processing • Evaluation • Conclusions ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  12. Evaluation • Energy Model • Etransmit = Etrans * k + Eamp * d2 • Ereceive = Erec * k • Metrics • Energy Cost • Response time (# of hops) • 3 schemes: • GHT (Geographic Hash table, WSNA 02) • IGHT (Index-based GHT) • SAQP ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  13. Experiments Sensitivity analysis on: • Query Skewness • Q(VLow:VHigh, t-Delta:t), VHigh-VLow is fixed and the center (VHigh+VLow)/2 follows zipf distribution. • Time Interval • Q(VLow:VHigh, t-Delta:t) • Query Locality • Confining factor C restrict queries issued from a region with size (C*X) x (C*Y) • Event Size • Number of Queries • Number of Events ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  14. Simulation parameters ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  15. Query Skewness Energy Consumption Response Time Higher energy savings if queries are more skewed (more similar) ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  16. Time Interval Energy Consumption Response Time Higher energy savings compared to IGHT, if query range is large ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  17. Query Locality Energy Consumption Response Time Higher energy savings if queries initiated from same region ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  18. Roadmap • Motivation • Similarity-Aware Query Processing • Evaluation • Conclusions ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  19. Conclusions • We proposed a similarity-aware query processing (SAQP) scheme that • creates materialized views in sensor networks • utilizes the materialized views to answer future queries that are similar to past ones. • By using our query split strategy and candidate selection algorithm, SAQP: • reduces energy consumption, • with a slight increase in response time, • without compromising QoD. ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  20. Thank You Questions? ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  21. BACKUP ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  22. Our Framework • O-node (Original node): Where the events are stored (locally). • Q-node (Query node): The node who issued the current query. • M-node (M-view node): A Q-node that has issued a query in the past becomes a M-node for future queries. (Query results) M-Views are stored at M-nodes. • I-node (Index node): Where the indexes to events and directories of materialized-views are stored. Each directory is associated with a query processed in the past. ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  23. The framework (cont.) • Events: O-nodeId, scalar attributes, event details, timestamp • Indexes: O-nodeId, scalar attributes, timestamp • M-View directories: M-nodeId, range, timestamp ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  24. QoD • Two events (e1, e2 in time order) are detected by the same O-Node • A Query (q) is initiated after the two events are detected. • Both events and the query are forwarded to the I-Node, but it reaches the I-Node in the order: e1, q and e2. • In GHT, only e1 will be returned. • In SAQP, the O-Node might be selected as a responder, both e1 and e2 will be returned. • Conclusion: SAQP achieves better QoD ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  25. Event size ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  26. Number of Queries ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  27. Number of Events ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  28. GPSR – Greedy Forwarding D x y ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  29. GPSR – Greedy Forwarding Failure ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

  30. GPSR – Perimeter Forwarding d z z e e c c a a f x x b ADMT Lab, Univ. of Pittsburghhttp://db.cs.pitt.edu

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