1. Comb, Needle, and Haystacks: Balancing Push and Pull for Information Discovery Xin Liu Department of Computer Science University of California, Davis Joint work with Q. Huang & Y. Zhang, PARC

2. Berkeley, 04/20/05 Comb-Needle Query Structure Objective Simple, reliable, and efficient on-demand information discovery mechanisms Constraints Limited communication capacity and battery power

3. Berkeley, 04/20/05 Where are the tanks?

4. Berkeley, 04/20/05 Pull-based Strategy

5. Berkeley, 04/20/05 Pull-based Cont’d

6. Berkeley, 04/20/05 Push-based Strategy

7. Berkeley, 04/20/05 Comb-Needle Structure

8. Berkeley, 04/20/05 Application Scenarios On-demand information query Any node can be the query entry point Queries may be generated at anytime Events can happen anywhere and anytime Examples: Firefighters query information in the field Surveillance Assume sensor nodes know their locations

9. Berkeley, 04/20/05 When an Event Happens

10. Berkeley, 04/20/05 When a Query is Generated Event Query Event

11. Berkeley, 04/20/05 Tuning Comb-Needle

12. Berkeley, 04/20/05 Reverse Comb When query frequency > event frequency

13. Berkeley, 04/20/05 The Spectrum of Push and Pull PullPush Global pull +Local push Global push +Local pull Push & Pull Inter-spike spacing increases Reverse comb Relative query frequency increases

14. Berkeley, 04/20/05 Comparison

15. Berkeley, 04/20/05 Simulations Radio model Path loss and random error Topology model Regular grid with random shifts Routing Constrained Geographical Flooding (CFG) for random topology Based on simulator Prowler

16. Berkeley, 04/20/05 An instance of connectivity

17. Berkeley, 04/20/05 Simulation Cont’d f_e=1 f_q=0.1

18. Berkeley, 04/20/05 Simulation Cont’d f_e=1 f_q=1

19. Berkeley, 04/20/05 Random Topology

20. Berkeley, 04/20/05 Constrained geographical flooding Needles and combs have certain widths

21. Berkeley, 04/20/05 Success Rate

22. Berkeley, 04/20/05 Power consumption

23. Berkeley, 04/20/05 A few issues Adaptive scheme Reliability Single fixed query entry point Yes-or-No query

24. Berkeley, 04/20/05 Adaptive Scheme Comb granularity depends on the query and event frequencies Nodes estimate the query and event frequencies Important to match needle length and inter-spike spacing Comb rotates Load balancing Broadcast information of current inter-spike spacing

25. Berkeley, 04/20/05 An illustration Regular grid Communication cost: hop counts No node failure Adaptive scheme

26. Berkeley, 04/20/05 Event & Query Frequencies

27. Berkeley, 04/20/05 Tracking the Ideal Inter-Spike Spacing

28. Berkeley, 04/20/05 Simulation Results Gain depends on the query and event frequencies Even if needle length < inter-spike spacing, there is a chance of success. Tradeoff between success ratio and cost 99.33% success ratio and 99.64% power consumption compared to the ideal case

29. Berkeley, 04/20/05 Strategies for Improving Reliability Local enhancement Interleaved mesh Routing update Spatial diversity Correlated failures Enhance and balance query success rate at different geo-locations

30. Berkeley, 04/20/05 Spatial Diversity Query x Event

31. Berkeley, 04/20/05 Fixed-Node Query Only one fixed query entry point Depends on relative frequency Depends on the length of the query E.g., 5 seconds vs. 30 minutes

32. Berkeley, 04/20/05 Numerical illustration

33. Berkeley, 04/20/05 Binary query Is there a tank in the field? Ans: Yes or No. If not delay sensitive Sequential query process Optimal comb width is shorter Intuition: can stop earlier

34. Berkeley, 04/20/05 Numerical illustration

35. Berkeley, 04/20/05 Summary Balance query cost vs. event report cost Adapt to system changes PullPush Global pull +Local push Global push +Local pull Push & Pull Relative query frequency increases

36. Berkeley, 04/20/05 Future work Data compression A more realistic model for communication cost Build a fixed comb structure for random networks for better success rate What if no/limited location knowledge? Consider delay tradeoff Accommodate sleep-awake pattern

37. Berkeley, 04/20/05

38. Joint work with P. Mohapatra, C. Chuah, P. Cheng On the Deployment of Wireless Sensor Networks

39. Berkeley, 04/20/05 Many-to-One Communication

40. Berkeley, 04/20/05 Network Deployment Many-to-one communication Data from all nodes directed to a sink node/fusion center Unbalanced traffic load Uneven power consumption Limitations on network lifetime if uniformly distributed “Important” nodes in the route die quickly Capacity bottleneck and Power bottleneck Desire for long-lived sensor networks Linear and planar networks

41. Berkeley, 04/20/05 Precise placement With access Expensive nodes Higher layer of a hierarchical structure Random placement No access Cheap nodes Lower layer of the hierarchy Coverage and connectivity properties Precise vs. Random Placement

42. Berkeley, 04/20/05 Maximize coverage area Given the desired lifetime and # of node available Maximize the lifetime of the network Given the number of nodes and coverage area Minimize the number of nodes required Given the coverage area and the desired lifetime Consider large networks with long lifetime requirements Objectives

43. Berkeley, 04/20/05 Why linear networks? Applications: Traffic monitoring, border line control, train rail monitoring, etc. Abstract model for narrow-and-long applications Duck island Tractability, insights for general cases Highly asymmetric traffic load & location-dependent power consumption Focus on communications What options do we have? Linear Networks

44. Berkeley, 04/20/05 Possible Solutions More energy for nodes with heavier load More nodes in the area closer to the sink Nodes closer to each other Load balancing Placement involves topology control, routing, power allocation

45. Berkeley, 04/20/05 System Model

46. Berkeley, 04/20/05 Total energy constraint: (n-1)E Energy can be arbitrarily allocated among nodes The network dies when no energy left Thus, i Total Energy Constraint

47. Berkeley, 04/20/05 Problem Formulation Numerical results as benchmark

48. Berkeley, 04/20/05 Homogenous initial energy allocation Observation: longer hops consume more energy “jump” may not be a good idea Observation: we do not want residual energy when the network dies. Power consumption per unit time should be the same for all nodes Consider large T (desired lifetime) A Greedy Algorithm

49. Berkeley, 04/20/05 A Greedy Algorithm

50. Berkeley, 04/20/05 Numerical Result

51. Berkeley, 04/20/05 Performance Analysis Lifetime, power, and coverage  =4, 19% more node to double lifetime  =4, 138% more node to double coverage

52. Berkeley, 04/20/05 Extensions Miscellaneous power consumption P T =c 1 + R  d  P R = c 2 Transmit at max power at max rate to near nodes Similar results hold Intuition: shorter links, higher rate, less time for T/R. Non-uniform traffic density Estimation errors on traffic density during the deployment

53. Berkeley, 04/20/05 The effect of arbitrary energy allocation is negligible Greedy algorithm Compensate for nodes with heavy load by reducing communication distance Performs very well and adapts to various conditions 2-D case Data aggregation Summary

54. Berkeley, 04/20/05