1. Attribute Allocation in Large Scale Sensor Networks Ratnabali Biswas, Kaushik Chowdhury, and Dharma P. Agrawal International Workshop on Data Management for Sensor Networks(DMSN), 2005, Trondheim, Norway.

2. OUTLINE Introduction Attribute allocation methodology Related work Simulation Conclusion

3. Scenario Large-scale sensor network serving multiple applications Large number of attributes sensed For each application, pre-defined set of queries

4. Basic idea A data–centric storage scheme for storing attributes –Instead of specific user-defined events An attribute included in many events Values have to be replicated Stored at different places for each individual event Propose a scheme for minimizing cost of data retrieval Use event name to hash a location

5. Attribute allocation methodology- Query processing Sink Control Node Storage Node A6A6A6A6 A1A1A1A1 A9A9A9A9 A 11 A4A4A4A4 A 20 A 16 A7A7A7A7 A5A5A5A5 A 14 A 17 A 19 A 15 A3A3A3A3 A 12 A2A2A2A2 A8A8A8A8 A 10 A 18 A 13 The entire network divided into a √m × √m grid Control Node –Maintain indexes about attribute values stored in storage nodes to facilitate data retrieval Storage Node –Responsible for storing values of corresponding attribute Q7 = {A2, A15, A19} Compute the optimal route Use the minimum spanning tree

6. A 19 A 15 A2A2 A 17 A 16 A7A7 A9A9 A5A5 A3A3 A1A1 A 20 A4A4 A 14 A6A6 A8A8 A 12 A 18 A 11 A 13 A 10 Attribute allocation methodology- Determining correlations Use to determine correlations between attributes

7. Attribute allocation methodology- Determining correlations The correlation represented –A tree of attributes –Edge weights between a pair of attributes –Call the correlation tree

8. Attribute allocation methodology- Determining correlations Attributes accessed more frequently stored closer to the sink The individual access probability P(A i ) of an attribute A i

9. Attribute allocation methodology- Determining correlations To create a correlation tree (heap-like) 1.Represent each query 2.Combine these individual query tree

10. Queries in order of priorities Attributes access probabilities Query for 30 A6A6 Query for 29 A 12 A 19 A 19 Query for 24 A 10 A 16 A 16 0.0001

11. Query for 30 6 Query for 29 19 12 0.0001 Query for 24 16 10 0.0001 Query for 27 12 18 0.0001 Combine Combine 1 19 12 0.0001 18 0.0001

12. Query for 18 19 4812 0.0001 Query for 30 6 Query for 24 16 10 0.0001 Combine 1 19 12 0.0001 18 0.0001 Combine Combine 2 19 1284 0.0001 18 0.0001

13. 19 1013 0.0007 711 0.0007 Special consideration –An attribute has different parents in the query tress Attribute allocation methodology- Determining correlations 1 611 0.0005

14. Attribute allocation methodology- Determining correlations

15. Attribute allocation methodology- Allocating attributes Correlation tree –Determine the distribution of attributes to the grid cell –More frequently accessed closer to sink –Higher correlation stored closer to each other –Maximum access probability allocated to the central-most grid cell

16. Attribute allocation methodology- Allocating attributes A 19 A 15 A2A2 A 17 A 16 A7A7 A9A9 A5A5 A3A3 A1A1 A 20 A4A4 A 14 A6A6 A8A8 A 12 A 18 A 11 A 13 A 10 Access probabilities A 19 > A 15 > A 17 > A 2 > A 16 > A 7 > A 5 > A 9 > A 3 > A 1 > A 20 > A 6 >A 4 > A 8 > A 12 > A 11 > A 14 > A 10 > A 18 > A 13

17. Related work- TAG: A tiny aggregation service for ad-hoc sensor networks

19. Simulation

20. Simulation- Varying the query rate

22. Conclusion Proposed a scheme –Determining a distribution of attributes –Minimizes query access cost Future work –Develop detailed protocols Query dissemination Data update Retrieval –Fault-tolerant –Load balance