Skyline Queries Against Mobile Lightweight Devices in MANETs

1. Skyline Queries Against Mobile Lightweight Devices in MANETs Zhiyong Huang1 Christian S. Jensen2 Hua Lu1 Beng Chin Ooi1 1 National University of Singapore, Singapore2 Aalborg University, Denmark

2. Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

3. Introduction • Skyline query • Operator based on dominance • MANET • Self-organizing, wireless mobile ad-hoc networks • Physical environment of this work • Lightweight devices

4. Skyline Queries in MANETs • Assumptions • Each resource-constrained device holds a portion of the entire dataset • Devices communicate through MANET • A mobile user is only interested in data of a limited geographical area, though the query involves data stored on multiple mobile devices

5. M1 M2 M3 M4 Example • M1 to M4 hold different hotel relations • M2 is interested in cheap and good hotels within the circle area

6. Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion and Future Work

7. Problem Setting • MANET of m mobile devices • {M1, M2, …, Mm} • Local relation Ri on each device Mi • <x, y, p1, p2, …, pn> • Skyline issued by a device Morg • <id, posorg, d> • id: network id of query originatorMorg • pos: position of Morg • d:distance (from pos) of interest

8. Technical Challenges • Slow and unreliable wireless channels compared to wired connections • To reduce data transferred between devices • Resource-constrained devices • Storage and processing saving techniques on mobile devices

9. Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

10. Straightforward Strategy • Query originator Morg • Executing a local skyline query: SKorg • Sends query to other mobile devices • Merges results when receiving them • A mobile device Mi • Executing a local skyline query too • Sends result SKi back to Morg • Instead of sending whole Ri

11. Discussion • Final skyline result: SK • SK≠USKi, SK USKi • FSK = USKi–SK • FSK contains all those tuples that are not in SK but sent between devices • Identify SKi–SK on device Mi • Inspiration of semi-join U|

12. Filtering Strategy • Any tuple tpi in SKi–SK is dominated by some tuple(s) tpj in SK • Where to find such tpjs? • Pick from Morg’s local result • Send <id, posorg, d, tpj> as query • Mi filters out tuples using tpj • Which one to pick? • Dominating region

13. p 2 M a x c o r n e r o f d a t a s p a c e b 2 D o m i n a t i n g R e g i o n p j 2 t p j p 1 0 p b j 1 1 Dominating Region • The ability of tpj to dominate others • Tuple value <pj1, pj2, …, pjn> • Data space boundaries • Volume of dominating region • VDRj=∏k(bk-pjk) • Choose from SKorgtpflt with max VDRj • Indep. distribution

14. Dominating Ability • Two hotel relations • Price range (20..200) • Smaller rating means better (1..10) Relation R1 Relation R2 (Morg)

15. Estimated Dominating Region • Over-estimation • VDRj=∏k(maxk-pjk) • maxk:pre-specified larger value • Under-estimation • VDRj=∏k(hk-pjk) • hk:local maximum

16. Dynamic Filtering Tuples • Three hotel relations • M4 -> M3 -> M1 VDR31=980 VDR41=960 Relation R3 Relation R4 (Morg) Relation R1

17. Query Log Mechanism • To avoid the same query more than once on any device Mi • Add a tag cnt to query issued by Morg • <id, cnt, posorg, d, tpflt> • Mi records/checks/updates <id, cnt> • Processes and forwards only cntlog<cnt • cnt can be a byte to save cost • A device can issue 256 queries • Reset after a period, say one day

18. Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

19. Dataset Storage • Goals • Space efficient • Local processing efficient • Operations • Spatial extent check • Distinct coordinates • Attribute value comparison • Floats • Duplicates

20. Hybrid Storage Model • Spatial coordinates • Real values • MBRi(xmax, ymax, xmin, ymin) • Attribute values • Ascending domains • IDs • Sort p1 Relation Ri Sorted domains p1 pn …

21. Local Skyline Computing • Sptial check • mindist(posorg, MBRi) > d • Skyline computing • Comparison of IDs instead of true values of float type • p2 to pn only • Update filtering tuple if necessary • Choose the one with larger VDR value

22. Assembly on Query Originator • When Morg receives SKi from others • Duplication elimination • False positive removing • A simple nested loop is enough • Comparing coordinates • Identify duplicates • Comparing attribute values • Identify false positive reports from both SKorg and SKi

23. Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

24. Experiment Parameters

25. Studies on Local Optimization • HP iPAQ h6365 pocket PC • MS Windows Mobile 2003 • 200MHz TI OMAP1510 processor • 64MB SDRAM (55MB user accessible) • SuperWaba • Java-based open-source platform for PDA and smartphone applications • www.superwaba.org

26. Time vs Local Cardinality • Flat Storage vs Hybrid Storage • Anti-Correlated vs Independent • HS incurs less processing cost

27. Time vs Local Dimensionality • Average of costs on both distributions • Coz they are close to each other • HS still performs better

28. Performance in Simulation • Simulated MANET • JiST-SWANS • A Jave based MANET simulator • http://jist.ece.cornell.edu/ • Pentium IV desktop PC • MS Windows XP • 2.99GHz CPU • 1 GB memory

29. Settings • Device setting • Data partitioned and allocated to devices using a grid of m1/2 by m1/2 • 1-5 queries per device • MANET settings • Total simulation time: 2 hours • Speed range: 2 unit/s – 10 unit/s • Holding time: 120 seconds • Wireless routing protocol: AODV

30. Data Reduction Efficiency • Data Reduction Rate • SKi’ is the local skyline after filtering • Pre-tests in static setting • Forwarding query out recursively • Findings • No significant difference between exact VDR and estimated VDRs • Dynamic filtering is more powerful

31. Data Reduction Rate

32. Response Time - BF • Breadth-First query forwarding • Parallel • Time receiving answers from 80% other devices • Cannot ensure all devices are always reachable and available in MANETs M2 M1 M3 Morg M5 M4 Query message Result message

33. Response Time - DF • Depth-First forwarding • Serialized • Query ends when originator finds all neighbors have processed the query M2 M3 M1 M4 M5 Morg Query message Result message

34. Response Time

35. Query Message Count • Only mobile device number affects the query message count obviously • Better performance of BF is not free

36. Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

37. Conclusion • Problem setting • MANET of lightweight devices • Skyline queries with spatial constraints • Solution highlights • Filtering based distributed query processing strategy to reduce communication cost • Specialized local storage and algorithm to speed up local processing • Experimentally verified performance

38. Q & A Thanks!