Programmable Data Collection Networks. Gurdip Singh Computing and Information Sciences Kansas State University Manhattan, KS 66506

Transcription

1 Programmable Data Collection Networks Gurdip Singh Computing and Information Sciences Kansas State University Manhattan, KS 66506

2 Data collection networks Application domains Real-time target tracking Environment monitoring Surveillance Global information grid applications : :

3 Bold Stroke System: Avionics Display Applications Periodic & Aperiodic O(10 6 ) Lines of Code Hard & Soft Real-Time Data Links Nav Sensors Weapon Management Vehicle Mgmt Mission Computer Radar Weapons 1000s of Components

4 Tracking time critical targets (Capstone demo for the DARPA PCES program) - System of systems CAOC

5 Precision Agriculture

6 Data Collection Systems Core Infrastructure - Servers - Storage Servers Data Analysis Data Collection Network feedback/ coordination control Sensor Field Precision Agriculture Environment monitoring Target tracking Fixed sensors Mobile robots Actuators

7 Research Challenges Real time information delivery Anonymous communication Large systems of systems Energy consumption Failures Unreliable communication

8 Model-driven Synthesis Development environments 1ms Deployed System 5hz Annotations for specifying requirements Configured Middleware Middleware - Select Services - Generate glue code - Generate deployment code - Static adaptation - Dynamic adaptation

9 Case study: Publish/subscribe middleware Attributes: rate, location, Event type: # consumers, # producers Correlated events: e1 and e2 Mode behavior Analysis Algorithms Assembled Application Configured Event Channel XML Metadata Configuration Code synthesis Code weaving FRAMES: Configurable event channel Subscriber lists: Direct Dispatch vs EC dispatch Gateways for distributed notifications Correlation Distributed Correlation: Filter placement Mode-awareness

10 Time taken for 1000 pushes OEP Scenarios - MediumSP gps: BM DeviceComponent Medium Size, Sing... Full Channel Events What does the FRAMES algorithms tell us? 7. Update() Component time triggered at 20Hz ins: BM DeviceComponent adc: BM DeviceComponent 1. Push() 3. Push() 2. Push() Events Correlated 12. Get() 11. Get() 9. Update() 5. Push() airframe: BM LazyActiveComponent 8. Get() navdisplay: BM DisplayComponent Container correlation 13. Get() 4. Push() 10. Get() 6. Push() 72. Update() radar1 : BM DeviceComponent hud : BM DisplayComponent 14. Push() track1 : BM OpenEDComponent tracksensor1 : 18. Get() BM DeviceComponent 15. Push() 16. Push() 26. Get() 34. Get() 17. Push() track2 : BM OpenEDComponent 19. Get() 70. Get() 48. Push() 20. Get() 22. Push() 23. Push() 27. Get() tracksensor2 : 24. Push() BM DeviceComponent 25. Push() 28. Get() 21. Get() 30. Push() 31. Push() 35. Get() tracksensor3 : BM DeviceComponent 29. Get() 32. Push() 44. Get() 33. Push() 47. Get() 36. Get() 42. Push() 39. Push() 73. Get() Update() track3 : BM OpenEDComponent 50. Push() Events Correlated 5800 tacticaldisplay1: 52. Push() BM DisplayComponent 71. Push() track4 : BM OpenEDComponent 54. Push() 74. Push() 49. Get() Get() 78. Update() 56. Push() 53. Get() 51. Get() 77. Push() track5 : BM ClosedEDComponent tacticalsteering : 55. Get() tacticaldisplay2: : BM OpenEDComponent Push() BM DisplayComponent 57. Get() 79. Get() track6 : BM OpenEDComponent 60. Push() 59. Get() 67. Get() Get() 61. Get() 62. Push() track7 : BM ClosedEDComponent 65. Get() 69. Get() Push() track8 : BM ClosedEDComponent Component time 68. Push() triggered at 20Hz 4800 (milliseconds) tracksensor4 : BM DeviceComponent 40. Push() 41. Push() 38. Push() Events Correlated 45. Get() 46. Get() 43. Get() track9 : BM ClosedEDComponent 66. Push() 37. Get() track10 : BM ClosedEDComponent radar2 : BM DeviceComponent Correlation in EC Using Correlator component Correlation in Container Hybrid Correlation in EC

11 Optimizing Network Algorithms Information Extraction Tools - Precision Agriculture Application Domains Target Tracking Application Properties Customization Tools Customized Algorithms Customized Algorithms Generic Network Algorithms

12 Configurable Infrastructure Sensors Wireless Modules (200+) Gateways Robots - Flexible topology setup - Remote reprogramming - Remote monitoring - Protocols for middleware services Configurable Sensor Grid

13 Model-driven development Application Application Domains Target tracking Workplace applications Middleware Infrastructure (collection of algorithms) 1. Identify subsystems 2. Select algorithms for each subsystem 3. Rules for dynamic adaptation Configured Test-bed Selected Algorithms

14 Real-Time Sensor Systems Education Graduate Certificate Program in Real-time Embedded System Design (with EECE,BAE). NSF CRCD - Combined Research and Curriculum Development. Sensor Networks courses

15 Sensor Networks program: Partially sponsored by K-State Targeted Excellence Program. Multidisciplinary projects involving several departments from Engineering, VetMed, and Agriculture. Leveraging expertise already developed Hydrology Heat treatment technology with Grain Science Micro-climate monitoring Precision Agriculture : :

16 Conclusion Programmable middleware infrastructure Selection from alternative implementations of services in each sub-system Optimization of individual implementations Multidisciplinary applications Embedded systems education