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NITRD/LSN Workshop On Complex Engineered Networks

NITRD/LSN Workshop On Complex Engineered Networks. September 20-21, 2012 Washington DC. Sponsored by AFOSR NSF DOE. Example A: Detection of Low-level Radiation Sources. Sources of low-level radiation small amounts of radioactive material. Overall Task :

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NITRD/LSN Workshop On Complex Engineered Networks

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  1. NITRD/LSN Workshop OnComplex Engineered Networks September 20-21, 2012 Washington DC Sponsored by AFOSR NSF DOE

  2. Example A: Detection of Low-level Radiation Sources • Sources of low-level radiation • small amounts of radioactive material • Overall Task: • Detect, localize and track sources based on sensor measurements • Different versions of this task are of importance to: • Department of Energy • Domestic Nuclear Detection Office • Others Several underlying foundational areas related to detection networks are open

  3. Difficulty of Detecting Low-level Radiation Sources The radiation levels are only slightly above the background levels and may appear to be “normal” background variations • Varied Background: Depends on local natural and man-made sources and may vary from area to area • Probabilistic Measurements: Radiation measurements are inherently random due to underlying physical process – gamma radiation measurements follow Poisson Process Several solutions are based on thresholding sensor measurements • Well-Studied Problem: for decades using single sensors: analytical and experimental • networks offer “newer” solutions but also raise complex design, analysis, operation and deployment issues • Analysis Area: • Quantification how much “better” a network of sensors performs compared to single-sensor, co-located and collective detectors

  4. Individual, Lo-Located, Collective and Network detection Individual Sensor Detection with thresholds Co-located Sensors: Detection with threshold Collective Detection with threshold Network Detection with Localization: localize the source first and detect it

  5. Relative Performance: Individual, Lo-Located, Collective and Network detectionfor SPRT based detection methods By combining currently available analytical results network with localization sensor with threshold sensor detection with threshold co-located sensors with threshold network with localization superiority due to localization - theory leads to new detection method superiority due to more measurements

  6. Long-haul sensor networks: • sensors distributed across the globe and/or in space • different from well-studied “smaller” sensor networks • Application Areas: • monitoring greenhouse gas emissions using satellite, airborne, ground and sea sensors - DOE • processing global cyber events using cyber sensors over Internet • space exploration using network of telescopes on different continents • target detection and tracking for air and missile defense - DOD • Response time requirements: • seconds: detecting cyber attacks on critical infrastructures - DOD • years: detecting global trends in greenhouse gas emissions - DOE Example B: Long-Haul Networks command and control com ST com com

  7. phenomenon Sensor/ Estimator time-stamped state estimates Sensor State Estimators • Sensor/Estimator: • Sensors/estimators generate state estimates of dynamic targets and send them over long-haul links to fusion center • Dominant Factors: • State estimates have errors with biases • Correlations in sensor estimate errors • Cases: • Single target – single estimator stream • Multiple targets – multiple estimator streams

  8. Network: loss, delay Time window [t,t+W] Correlation and fusion Quality of fused estimates • Sensor estimates that reach fusion center within • used by correlation and fusion algorithms with resultant quality Quality of fused estimate of sensor estimates with allocated time

  9. Quality of fused state: including both network and computation effects • A lower bound on the probability: A Lower Bound: Guaranteed Performance • Message loss probability: • estimated based on network parameters and communication protocols • Expected quality of correlation and fusion: • estimated based on test measurements

  10. Example C: UltraScience Net Experimental network research testbed: To support advanced networking and related application technologies for large-scale projects Currently funded by Department of Defense; by Department of Energy (2004-2007) • Features • End-to-end guaranteed bandwidth channels • Dynamic, in-advance, reservation and provisioning of fractional/full lambdas • Secure control-plane for signaling • Peering with ESnet, National Science Foundation CHEETAH, and other networks • Provides 10 Gbps dedicated connections

  11. Emulation Purpose: Continued functionality of 10G USN (de-commissioned) • Collect measurements on emulated connections • Apply segmented regression to approximate USN measurements • Emulates connection lengths not feasible on USN at much lower cost USN 10G Emulation 10GigE linux host Nexus ANUE 10GigE emulator linux host E300 10GigE switch linux host ANUE OC192 emulator linux host OC192

  12. Overview of Network Simulations, Emulations and Realizations

  13. Differential Regression Method for Cross-Calibration Measurements on OPNET simulated path of distance d Measurements on Anue emulated path of distance d Basic Question: Predict performance on connection length not realizable on USN Example: IB-RDMA or HTCP throughput on 900 mile connection Measurements on USN path distance Measurement Regression: for Regression of measurements on Differential Regression: for Approach: • Collect simulation or emulation measurement for • Apply differential regression to obtain the estimate simulated/emulated measurements point regression estimate

  14. Wide-Area Infiniband Throughput:18 Different Configurations: physical and emulated physical connections Emulations provide good approximation at very low cost emulated connections

  15. Analysis of iperf and disk transfer measurements • Measurements collected ANUE-emulated USN connections used for interpolation/extrapolation – compared with emulated connections • Interpolation/extrapolation: • Apply differential regression to obtain USN predictions • Interpolation: 100 and 150ms • Not feasible on USN • in-between lengths • Extrapolation: 200 ms • Not feasible on USN • too long • Interpolation and extrapolation: • For 10Gbps ANUE network emulators can provide measurements for connection lengths not feasible (too long or in-between) on USN • Enable us to continue 10Gbps testing after 10Gbps USN de-commissioning

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