1. 1 Similarity aware query processing in sensor networks PingXia, PanosK.Chrysanthis, and AlexandrosLabrinidis Proceedings of the 14th International Workshop on Parallel and Distributed Real-Time Systems, April 2006. (WPDRTS'06)

2. 2 Outline Introduction Similarity aware query processing –Query processing scheme –Split a query –Candidate selection Simulation Conclusion

3. 3 Base station architecture BS

4. 4 Data Centric Storage (GHT) (11, 28) (11,28)=Hash(“temp”) Get(“temp”) Put(“temp”) (11,28)=Hash(“temp”)

5. 5 Motivate (11, 28) Q1 : Temp > 200 and 20 < light level < 40 12p.m.-12:30p.m. Q2 : Temp > 250 and 25 < light level < 35 12p.m.-12:30p.m. Some queries are similar The basic idea The results (events) for previously issued queries as materialized views Utilized the materialized view to answer similar queries

6. 6 System model (36, 40) Use a hash function to determine the index node O-nodeIDattributes (range)timestamp 10temp12p.m. Index entry M-nodeIDattributes (range)timestamp 2100-20012:30p.m. M-view directory entry Mobile agent Location Index node--I-node Original storage node--O-node Materialized view node--M-node

7. 7 Query Processing scheme (36, 40) 5 2 20 15 9 10 O-nodeIDattributestimestamp 10temp12p.m. 15temp12:30p.m. 20temp12:30p.m. Index entry M-nodeIDattributestimestamp 525-3512:00p.m.-12:10p.m. 9100-25012:00p.m.-12:30p.m. 2100-20012:15p.m.-12:30p.m. M-view directory entry Mobile agent Location Index node--I-node Original storage node--O-node Materialized views node--M-node Event : fire Range: 100-200 Time: 12:00p.m.-12:30p.m. Candidates of O-node : 15, 20 Candidates of M-view-node : 9, 2 Selecting a set of nodes as responders

8. 8 Split a query Expect to find an M-view entry –Answer the query completely In most case the range of an M-view entry –Partially overly Split the original query –Avoid duplicates Query range 2040 M-view entry 1 1530 M-view entry 2 2540

9. 9 Split a query 1.[a, b] contains [x, y] Query range ab The range of an M-view entry xy Q1: on range [x, y] Q2: on range [a, x] v [b, y] 2.[x, y] contains [a, b] Query range ab The range of an M-view entry xy Needn’t to split the query 3.[a, b] intersection [x, y] Query range ab The range of an M-view entry xy Q1: on range [a, y] Q2: on range [y, b]

10. 10 Candidate selection O-nodeIDattributestimestamp 10temp12p.m. O-nodeIDattributestimestamp 1temp12: 03p.m. 10temp12p.m. O-node candidate set M-nodeIDattributestimestamp 2200-24012:30p.m. 5100-20012:30p.m. 24150-25012:00p.m. M-node candidate set order Distance 5 → 2 → 24 closefar highlow Priority Select this M-node to be a responder M-nodeIDattributestimestamp 2200-24012:30p.m. 24150-25012:00p.m. Event : fire Range: 100-250 Time: 12:00p.m.-12:30p.m.

11. 11 Candidate selection O-nodeIDattributestimestamp 10temp12p.m. O-node candidate set M-node candidate set order Distance 5 → 2 → 24 closefar highlow Priority Select this M-node to be a responder M-nodeIDattributestimestamp 2200-24012:30p.m. 24150-25012:00p.m. Responders 1.Selected M-nodes (5, 2) 2.Remaining O-node candidates (10)Compare 1.Cost with M-view 2.Cost without M-view result Event : fire Range: 100-250 Time: 12:00p.m.-12:30p.m.

12. 12 Candidate selection nodeIDcost N1C1C1 N2C2C2 N3C3C3 : : NnCn responder set Cost 1.The sum of the energy cost of forwarding the query to the node 2.The energy cost of returning the results back to Q-node Minimize Total_cost Total_cost = x1 * C1 + x2 * C2 + ．．． + xn * Cn where x1, x2,…,xn represent the percentage of the range in a M-view answer a query

13. 13 Simulation E send = E trans × k + E amp × d 2 –E trans : Transmitter electronics– 50nJ/bit –E amp : Transmit amplifier– 0.1nJ/bit/m 2 E receive = E rec × k –E rec : Receiver electronics– 50nJ/bit Sensing region : 400m × 400m Num of sensor : 400 Num of events : 100 Num of queries : 100 Event size : 8bytes Range size : 4bytes Index size : 4bytes Skewness of zipf distribution : 0.5

14. 14 Simulation – Event size

15. 15 Simulation – Query skewness

16. 16 Simulation – Total queries

17. 17 Simulation – Total event

18. 18 Conclusion Propose a similarity-aware query processing scheme –Creates materialized views –To answer future queries that are similar to past ones –Reduces energy consumption