1. 1 Load Balance and Efficient Hierarchical Data-Centric Storage in Sensor Networks Yao Zhao, List Lab, Northwestern Univ Yan Chen, List Lab, Northwestern Univ Sylvia Ratnasamy, Intel Research

2. 2 Outline Background and Motivation Hierarchical Voronoi Graph based Routing –Basic routing algorithm –Practical design issues Evaluation Conclusions and Future Work

3. 3 Generic Storage Schemes External Storage Local Storage Data-Centric Storage (DCS)

4. 4 Generic Storage Schemes External Storage –Hotspot problem (if no need to store all events ) Event

5. 5 Generic Storage Schemes Local Storage –Overhead of flooding Event

6. 6 Generic Storage Schemes Data-Centric Storage [CCR03] –Good to avoid hotspots and flooding overhead in some scenarios Event

7. 7 Motivation Routing Primitive for Data-Centric Storage vs Any-to-any Routing –DCS doesn’t require any-to-any routing E.g. in pathDCS [NSDI06], not all nodes are routable –Any-to-any routing may not be suitable for DCS E.g. BVR[NSDI05] and S4[NSDI07] –Only a few any-to-any routing can be DCS routing E.g. VRR [Sigcomm06], GEM[Sensys03]

8. 8 Motivation Routing Primitive for Data-Centric Storage vs Any-to-any Routing Desirable Properties of DCS Routing –No GPS (or other location device) –Scalability –Efficiency Path stretch (routing path length / shortest path length) –Load Balancing In routing (forwarding overhead) In Storage Our Goal –Design routing primitive for DCS with the above properties

9. 9 Outline Background and Motivation Hierarchical Voronoi Graph based Routing –Basic routing algorithm –Practical design issues Evaluation Conclusions and Future Work

10. 10 Hierarchical Voronoi Graph based Routing Basic Routing Algorithm –Hierarchical coordinate –Region oriented routing –Name based routing for DCS Practical Issues –Landmark selection –Path stretch reduction –Handling dynamic changes

11. 11 Voronoi Graph

12. 12 Hierarchical Coordinate Divide the network based on the hop distance to landmarks Irregular borderline in realilty

13. 13 Hierarchical Coordinate Divide the network based on the hop distance to landmarks In smallest region, nodes know each other

14. 14 Overhead of Building Coordinate Initialization Overhead –Each Layer O( mN ) messages where m is the number landmarks splitting a region, and N is the number of nodes –K Layers K ~ O(log N ) –Total Overhead O( mN· log N ) messages Memory Usage –Km ~ O( m· log N )

15. 15 Name Based Routing S has an event E –Take a hash function H 1 and get j = H 1 (E)%3 –S sends E to the jth 1 st level landmark and enter L j ’s region via node a –Node a compute H 2 (E)%3 to determine the next landmark s d L1L1 L 1,2 L 1,2,3 L2L2 L3L3 a Bypass landmarks

16. 16 Load Balancing in Storage Load Balancing Problem –In naïve name based routing, non-uniform division of regions causes non-uniform storage distribution –To divide regions uniformly is very hard Our Approach: Non-uniform Hash Function –Collect the number of nodes in each region –Hashed value is proportional to the population of possible sub-regions

17. 17 Outline Background and Motivation Hierarchical Voronoi Graph based Routing –Basic routing algorithm –Practical design issues Evaluation Conclusions and Future Work

18. 18 Evaluation Simulation Setup –C++ implementation –Simple MAC without collision –Unit disk graph model in 2D space (communication range 1) –Baseline simulation 3200 nodes Density: 3π neighbors in average –Simulate HVGR, HVGR+ and VRR[Sigcomm06] m = 6 (number of landmarks splitting a region) Metrics –Path stretch –Load balancing: CDF for visualization –Route table size –Initialization overhead –Maintenance overhead

19. 19 Efficiency The stretch of HVGR doesn’t increase as N increase.

20. 20 Scalability The route table size and initialization overhead increase logarithmically.

21. 21 Routing Load Balancing The routing load balancing feature of HVGR is close to that of shortest path routing.

22. 22 Storage Load Balancing The storage load balancing feature of HVGR is close to that of ideal hash based storage.

23. 23 Conclusion Design HVGR/HVGR+ –Topology based routing (No GPS) –Good scalability (log N memory) –High efficiency (close to shortest path routing) –Balanced load in both routing and storage Future Work –Theoretical analysis –Tinyos implementation

24. 24 Thanks! Q&A?

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27. 27 Backup

28. 28 Name Based Routing for DCS Convert Name to Label –Event name S –A series of hash functions H i –Order the m landmarks –Let j = H i (S) mod m, the i th level label is the j th landmark

29. 29 Voronoi Graph

30. 30 Voronoi Graph Divide the regions based on the closest landmark rule.

31. 31 Number of Landmark (m) in Each Level m is not critical

32. 32 Number of Landmark (m) in Each Level The larger the m, the more overhead. We pick m=6 finally.

33. 33 Desirable Properties of DCS DCS without Location Information –No GPS or other location devices Scalability –Memory usage –Control message overhead Efficiency –Path stretch (routing path length / shortest path length) Load Balancing –In routing (forwarding overhead) –In Storage

34. 34 Outline Background and Motivation Hierarchical Voronoi Graph based Routing –Basic routing algorithm –Practical design issues Evaluation Conclusions and Future Work

35. 35 Region Oriented Routing From s to d with label (L 1, L 1,2, L 1,2,3 ) s d L1L1 L 1,2 L 1,2,3 Bypass landmarks a

36. 36 Hierarchical Coordinate Divide the network based on the hop distance to landmarks