1. 1 Towards A Holistic Approach for System Design in Sensor Networks Chair:Prof. Nael Abu-Ghazaleh Committee Members:Prof. Kenneth Chiu Dr. Tony Fountain Prof. Wendi Heinzelman Prof. Kyoung-Don Kang Prof. Michael Lewis Sameer Tilak

2. 2 Outline WSN Applications and Challenges Summary of Contributions Holistic Principle WSN critical Subsystems and Services Holistic Framework Architecture Abstractions and Virtualization Conclusion and Future Work

3. 3 Enablers – Micro-sensors Small (coin->matchbox->PDA range) Limited resources Battery operated Embedded processor (8-bit to PDA-class processor) Memory: Kbytes—Mbytes range Radio: (Kbps – Mbps; often small range) Storage (none to a few Mbits)

4. 4 Sensor Network Applications Embed Embed numerous distributed devices to monitor and interact with physical world: hospitals, homes, vehicles, and “the environment” Network these devices so that they can coordinate to perform higher-level tasks. Requires robust distributed systems of hundreds or thousands of devices. Disaster Response

5. 5 Sensor Node Specific Challenges Low Battery power Low bandwidth Error-prone air medium Low computing power and memory Heterogeneous software and Hardware architectures

6. 6 Sensor Network Challenges Large-scale fine-grained heterogeneous sensing 100s to 1000s of nodes providing high resolution Spaced a few feet to 10s of meters apart Collaborative Each sensor has a limited view Spatially In terms of sensed data type Distributed Communication is expensive Localized decisions and data fusion necessary

7. 7 Summary of Contributions WSN critical subsystems and services Information dissemination Storage Management Localization Holistic Framework Abstraction and virtualization

8. 8 Holistic Principle Data centric, resource constrained operation effective operation requires careful balancing of application level utility and cost (Principle 1) Communication is expensive Localized interactions -- produce local estimates of utility and cost (Principle 2) Local estimates of application level utility and cost can significantly differ from actual value observed globally Information available globally that significantly impacts local estimates are called context Identify and track context when it is worth it to do so (Principle 3)

9. 9 Non-Uniform Information Dissemination WSN Critical Subsystems/Services ICNP 2003, NCA 2004, NCA 2005, TR

10. 10 Non-Uniform Information Dissemination Loss in precision as a function of distance is acceptable

11. 11 Intuition Forward packets less aggressively the further away you are from the event deterministically: e.g., forward every nth packet, n increase with distance probabilistically: e.g., forward packets with a probability that drops with distance from event Here, the context is spatial and can be efficiently tracked by tracking the distance from the source on the event packet Significant energy saving results, while keeping information accurate close to the event source

12. 12 Randomized Protocols Biased protocol: Packet forwarding decisions are based on a coin toss and relative distance from source. Forwarding probability is higher for physically closer sources. (Context) Simple, low overhead and very scalable

13. 13 Weighted Energy-Error Study

14. 14 High-level concluding remarks Energy-efficient light-weight protocols that capitalize on non-uniform information granularity Context embedded in the form of TTL (distance from an event source).

15. 15 WSN Critical Subsystems/Services Storage Management IJAHUC 2005, Wiley Book chapter 2005, TR

16. 16 Intuition Exploit spatio-temporal redundancy Coordinate for redundancy control Context is Spatial

17. 17 Candidate Protocols Local Storage Local-Buffer Clustering CBCS: Aggregate and Store and Cluster Head Context: CLS: Coordinate and store locally CCS: Combined CBCS + CLS

18. 18 Clustering Only CH stores data Rotate CH Distributed storage, medium fault-tolerance Spatial aggregation possible Round 1 (time = 0) Round 2 (time = 20) Round 1 (time = 40)

19. 19 CCS Round 1 (time = 0) Round 1 (time = 40) Feedback Data CH Feedback provides context for data utility CH has more global view than individual sensor Samples

20. 20 Storage and Energy Study

21. 21 High-level concluding remarks Collaborative protocols are scalable, light-weight, does load balancing and increase storage lifetime Context provided in terms of feedback for redundancy control Context in space

22. 22 Localization IEEE IWSEEASN 2005 WSN Critical Subsystems/Services

23. 23 Mobile Sensor Localization Localization: Determine physical coordinates of a given sensor Existing research considers static sensor nets. Mobile sensors Energy versus accuracy trade-offs Protocols SFR (Static Fixed Rate) DVM (Dynamic Velocity Monotonic) MADRD (Mobility Aware Dead Reckoning Driven)

24. 24 Existing Research on Localization Assumes Static Sensor Network Focus on How to Carry Localization and not When Range/Direction Based Calculate distance from anchors and triangulate Received Signal Strength (e.g. RADAR) Time of Arrival (e.g. GPS) Time Difference of Arrival (Cricket, Bat) ProximityBased Centroid ATIP DV HOPS MDS

25. 25 Motivation What about Mobile Sensor Networks ? Interesting Energy-Accuracy trade off !

26. 26 Problem Definition

27. 27 SFR Localize every t seconds Very simple to implement Once Localize tag data with those coordinates till next localization Energy expenditure independent of Mobility Performance varies with Mobility Existing Projects such as Zebranet use this approach (3 minutes).

28. 28 DVM Adaptive Protocol Sensor Adapts its localization frequency to Mobility Goal maintain error under application-specific tolerance Compute current velocity and use it to decide next localization period Once Localize tag data with those coordinates till next localization Upper and Lower query threshold Energy expenditure varies with Mobility Performance almost invariant of Mobility

29. 29 MADRD Predictive Protocol Estimate mobility pattern and use it to predict future localization Localization triggered when actual mobility and predicted mobility differs by application-specific tolerance Tag data with predicted coordinates (differs from SFR and DVM) Changes in mobility model affect the performance Upper and Lower query threshold Energy expenditure varies with Mobility Performance almost invariant of Mobility

30. 30 MADRD State Diagram

31. 31 High-level Summary of Analysis Error in non-predictive protocols increase with any mobility that moves the node away from its last localization point Error in Predictive protocols increase only when the predictive Model is inaccurate Model estimation in incorrect Model changes (pause, direction change)

32. 32

33. 33 Medium Speed (4-5 m/s)

34. 34 Error versus Pause time

35. 35 Summary Studied energy versus accuracy tradeoff in localization for mobile sensors DVM and MARD are completely distributed scalable protocols DVM and MADRD outperform SFR Context Temporal

36. 36 Resource Management BW Storage Energy Context Application Local Resources Non-Local resources Data utility (Principle 3) HOLISTIC APPROACH Operation Management Data Samples Local utility estimate Principle 1 Principle 2 Initial Idea

37. 37 Context Localized decisions leads to scalability Local estimate of utility of data can differ from global measurement Absence of relevant global knowledge: Context Feedback can be used to improve accuracy of local estimate

38. 38 Patterns & Types Context Patterns Context in time Context in space Application-specific (domain knowledge) Resource related (src-dest path) Types Utility context Cost context

39. 39 Research Challenges Data Utility Assessment Resource Cost Assignment Utility-Cost Normalization Tracking, Building, and Maintaining Context Middleware

40. 40 Holistic Framework Architecture Point in design space

41. 41 Holistic Framework Components Benefit Estimator Cost Calculator Planner

42. 42 Benefit Estimator Data Significance Data scope in Time, Space App-specific (observer interest) Data Quality Data Freshness, accuracy, resolution, App-specific measures Output Benefit Vector (BV) MAUT

43. 43 Cost Calculator Sensing, Transmission, Storage, Computational, Reception. Output Cost Vector (CV): Vector of pre- determined transformations MAUT

44. 44 Planner Rule based engine. Input BV, CV Incorporates application state Objective: Maximize Benefit/Cost ratio

45. 45 Instance of a Planner Algorithm IF (Utility == HIGH) { IF(STATE== NEED_LOCALIZATION) LOCALIZE AND THEN TRANSMIT } If (Utility == MEDIUM) Transmit If (Utility == MEDIUM) Low drop.

46. 46 Component Placement Trade-offs Middleware versus Application Benefit Estimator? Application-specific knowledge Cost Calculator? App/infrastructure communication Resource cost in middleware Planner? Single versus multiple apps

47. 47 Abstraction EESR 2005

48. 48 Related Work Database Abstraction Programming abstractions e.g. Nesc TinyOS Plan-9, Inferno

49. 49 File System Abstraction Treat entire sensor network as a distributed file system Application-specific namespaces Well understood interface Heterogeneity Applications get fine grained control over resources

50. 50 Application-specific Namespaces Making Abstractions efficient Default resource namespace

51. 51 Applications Monitoring and Calibration Debugging Sense & Respond Data Centric Application etc. Sample Usages mount /dev/network /network ls /network/cluster1/sensors/ cat /network/cluster1/s1/remaining-energy echo 2.5 > /network/cluster1/s1/control

52. 52 query /network/cluster1/Location Query Execution Scenario Fine-grained resource control Exposes cost and utility explicitly Optimized query planner (Below DB)

53. 53 Sense & Respond System Heterogeneity

54. 54 Abstraction & Virtualization WSN Virtualization NCUS 2005

55. 55 Virtual Sensor Networks

56. 56 Micro-sensor Hardware Berkeley Motes (Mica2) Pasta nodes Mantis-nymph WINS

57. 57 Software Architectures Operating Systems TinyOS Linux Windows CE MOS Programming Languages C nesC JAVA

58. 58 Motivation for Resource Discovery Middleware Mobile and Ad-Hoc sensor networks Rescue operations Battlefield scenarios Seamless Integrating of sensors to Grids Sensors control, configuration (remote)

59. 59 Mobile Sensor Network  Ad-Hoc Network  Self-Configuration

60. 60 Service Discovery Protocols Scalable to thousands to sensors Energy-efficient Standardization Design Space Proactive versus reactive Distributed versus centralized Tuned for Sensor network characteristics Minimize Transmission and reception of messages Sensors have low duty cycle radios

61. 61 Our Approach Register Message Query Message Resource Info. message Cluster-Head Resource registry

62. 62 XML versus proprietary format 10 10 100 100 -40 -40 100 100 -1 -1 Describing resource in XML consumes 10 times more power than proprietary binary message

63. 63 Message Formats Power efficient Communication overhead Standardization Proprietary format HLL XMLLHH XML with compression M-H H Binary-XMLM-HL- MH

64. 64 Conclusions Application-specific, light-weight, energy-efficient protocols for critical services and subsystems Holistic principle and framework Abstraction and virtualization

65. 65 Future Work Holistic Framework VSN ICTs for developing regions

66. 66 Questions ???

67. 67 Thank You

68. 68 Direction Change

69. 69 Analysis of the Proposed Protocols Constant Velocity model SFR and DVM error increases linearly MADRD estimates location precisely (no error) Constant Velocity + pause SFR and DVM error increasely linearly and stays there MADRD has 0 initial error and then it increases linearly Contant Vecloty + change in direction SFR: performs better if the turn is towards the prev localization point (turn 90deg to 270 deg) MADRD: otherwise performs better (DOES NOT increase/decrease linearly)

70. 70 Direction Change