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  1. Query Aggregation for Providing Efficient Data Services in Sensor Networks Wei Yu*, Thang Nam Le+, Dong Xuan+ and Wei Zhao* *Computer Science Department Texas A&M University +Department of Computer Science and Engineering The Ohio State University IEEE Mobile Ad-hoc and Sensor Systems (MASS), 2004 Shin_wei Ho

  2. Outline • Introduction • Query Aggregation-Based Data Service Frameworks • Weighted Zone-based Query Aggregation Algorithm • Performance Evaluation • Conclusion

  3. Introduction • The wireless sensor networks are required to provide efficient data services as a distributed database. • The application can submit its requests as queries.

  4. Introduction (cont’d) • Sensor networks are deployed for monitoring the environment consisting of • Temperature sensors • Humidity sensors • Wind sensors • Such networks typically need to support a large number of users.

  5. Introduction (cont’d) • There are salient features that all of the above application share: • query rate can be high • the energy consumption spent on sending and routing queries may far exceed • For these class of applications, optimizing query dissemination is critical to improve performance of the sensor network.

  6. Introduction (cont’d) • In the traditional query dissemination model, applications forward queries to the base station of the sensor networks. • processes the queries one by one • This simple approach suffers from shortcomings: • Applications may pose duplicate queries • Overlapping queries

  7. Introduction (cont’d)

  8. Query Aggregation-Based Data Service Frameworks • Two major problems • aggregating the queries • routing queries efficiently to proper regions • We discuss three frameworks to solve these problems: • Purely Sensor Network-based Framework (PSNF) • Purely Base Station-Oriented Framework (PBSOF) • Integrated Query Aggregation Framework (IQAF)

  9. Query Query Query Query Aggregation-Based Data Service Frameworks-- Purely sensor network-based framework (PSNF) send the same data multiple times to reply for different queries Without conducting query aggregation decision Base Station

  10. Query Query Query Query Query New Query Query Aggregation-Based Data Service Frameworks-- Purely base station-oriented framework (PBSOF) makes the query aggregation decision based on the input query information. Base Station

  11. Query Aggregation-Based Data Service Frameworks-- Integrated query aggregation framework (IQAF) • We consider the fact • base station has a global picture of all input queries • sensor network can take certain roles to execute the aggregated query plan • Thus, a number of sensor nodes as access nodes are selected as the query proxy.

  12. Query Aggregation-Based Data Service Frameworks-- Integrated query aggregation framework (IQAF) (cont’d)

  13. Weighted Zone-based Query Aggregation Algorithm-- Problem Definition

  14. Weighted Zone-based Query Aggregation Algorithm Process the input queries in set Q by filtering queries with full cover property. Q4(v4) Q1(v1) Q6 Q3(v3) Q5(v5) Q2(v2) Q: Input query V: Attribute information : Query region

  15. Weighted Zone-based Query Aggregation Algorithm (cont’d) Calculate the overlapping zone and assign the weight Q4(v4) Q1(v1) Q3(v3) O3 O4 Q5(v5) O5 Q2(v2) O1 O2 Q: Input query V: Attribute information : Query region

  16. Weighted Zone-based Query Aggregation Algorithm (cont’d) Consolidate overlapping zones in O Q4(v4) Q1(v1) Q3(v3) O3 O1 O4 Q5(v5) O5 Q2(v2) O1 O2 Q: Input query V: Attribute information : Query region

  17. Weighted Zone-based Query Aggregation Algorithm (cont’d) Sort the weights and assign queries to corresponding zone Q4(v4) Q1(v1) Q3(v3) O3 O1 O4 Q5(v5) O5 Q2(v2) O1 O2 Q: Input query V: Attribute information : Query region

  18. Weighted Zone-based Query Aggregation Algorithm (cont’d) New aggregated queries Query 1:{Q1, Q2, Q3} Query 2:{Q4 ,Q5} Q4(v4) Q1(v1) Q6 Q3(v3) O3 O1 O4 Q5(v5) O5 Calculate the access point Q2(v2) O1 O2 Q: Input query V: Attribute information : access point : Query region

  19. Performance Evaluation-- Experimental Model • A grid-topology network • 1500m x 1500m • Grid size is 5m x 5m • N queries, each of which is m-bit long • Each query uniformly request the data from area of S (=200). • Query messages are combined with compression ratio(0.7).

  20. Performance Evaluation-- Experimental Model (cont’d) • The energy consumption of sending message is calculated by • The energy consumption of receiving a message is calculated by

  21. Performance Evaluation

  22. Performance Evaluation(cont’d)

  23. Performance Evaluation(cont’d)

  24. Conclusion • Query Aggregation • A multi-layer overlay-based framework for efficient sensor data service • can support other routing protocols • An effective query aggregation mechanism • do not consider the existing topology and distribution of sensors • query buffer