1. Skyline Queries Against Mobile Lightweight Devices in MANETs Zhiyong Huang 1 Christian S. Jensen 2 Hua Lu 1 Beng Chin Ooi 1 1 National University of Singapore, Singapore 2 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. Example M 1 to M 4 hold different hotel relations M 2 is interested in cheap and good hotels within the circle area M1M1 M2M2 M3M3 M4M4

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 –{M 1, M 2, …, M m } Local relation R i on each device M i – Skyline issued by a device M org – id: network id of query originator M org pos: position of M org 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 M org –Executing a local skyline query: SK org –Sends query to other mobile devices –Merges results when receiving them A mobile device M i –Executing a local skyline query too –Sends result SK i back to M org –Instead of sending whole R i

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

12. Filtering Strategy Any tuple tp i in SK i –SK is dominated by some tuple(s) tp j in SK Where to find such tp j s? –Pick from M org ’ s local result –Send as query –M i filters out tuples using tp j Which one to pick? –Dominating region

13. Dominating Region The ability of tp j to dominate others –Tuple value –Data space boundaries Volume of dominating region –VDR j =∏ k (b k -p jk ) Choose from SK org tp flt with max VDR j –Indep. distribution Dominating Region 0 b 1 b 2 p j1 p j2 Max corner of data space p 1 p 2 tp j

14. Dominating Ability HotelPriceRating h11207 h12405 h13807 h14804 h151007 h161003 HotelPriceRating h21603 h22802 h231201 h241402 h251004 Two hotel relations –Price range (20..200) –Smaller rating means better (1..10) Relation R 1 Relation R 2 (M org ) VDR (200-60)*(10-3)=980 (200-80)*(10-2)=960 (200-120)*(10-1)=720 − −

15. Estimated Dominating Region Over-estimation –VDR j =∏ k (max k -p jk ) –max k :pre-specified larger value Under-estimation –VDR j =∏ k (h k -p jk ) –h k :local maximum

16. Dynamic Filtering Tuples HotelPriceRating h31603 h32805 h331204 HotelPriceRating h41802 h421201 h431402 Three hotel relations –M 4 -> M 3 -> M 1 Relation R 3 Relation R 4 (M org ) HotelPriceRating h11207 h12405 h13807 h14804 h151007 h161003 Relation R 1 VDR 31 =980VDR 41 =960

17. Query Log Mechanism To avoid the same query more than once on any device M i Add a tag cnt to query issued by M org – M i records/checks/updates –Processes and forwards only cnt log <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 –MBR i (x max, y max, x min, y min ) Attribute values –Ascending domains –IDs –Sort p 1 xyp1p1 …pnpn 1.331103.670…2 1.329103.591…0 1.412103.772…j …………… 1.429103.95k…3 Relation R i v0v0 v1v1 … vkvk Sorted domains … p1p1 pnpn v0v0 v1v1 … vjvj

21. Local Skyline Computing Sptial check –mindist(pos org, MBR i ) > d Skyline computing –Comparison of IDs instead of true values of float type –p 2 to p n only Update filtering tuple if necessary –Choose the one with larger VDR value

22. Assembly on Query Originator When M org receives SK i 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 SK org and SK i

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

24. Experiment Parameters Number of mobile device3 2, 4 2, …, 10 2 Cardinality of global reln100K, 200K, …, 1000K Cardinality of local reln10K, 20K, …, 100K Local storage modelFlat, Hybrid Number of non-spatial attr2, 3, 4, 5 Non-spatial attri range[0.0, 9.9], [0,1000] Spatial extent of global reln1000 X 1000 Attribute distributionIndep., Anti-Correl. Query distance of interest100, 250, 500

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 m 1/2 by m 1/2 –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 – –SK i ’ 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 M org M4M4 M1M1 M5M5 M2M2 M3M3 Query message Result message

33. Response Time - DF Depth-First forwarding –Serialized Query ends when originator finds all neighbors have processed the query M4M4 M org M1M1 M5M5 M2M2 M3M3 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!