1. NITRD/LSN Workshop On Complex 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 Network Detection with Localization: localize the source first and detect it Collective Detection with threshold

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

6. Example B: Long-Haul Networks 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 6 command and control ST com

7. 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 time-stamped state estimates Sensor/ Estimator Sensor/ Estimator phenomenon 7

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

9. A lower bound on the probability: A Lower Bound: Guaranteed Performance Quality of fused state: including both network and computation effects 9 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. USN 10G Emulation 10GigE 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 linux host linux host ANUE 10GigE emulator NexusNexus linux host linux host ANUE OC192 emulator E30010GigEswitch OC192

12. Overview of Network Simulations, Emulations and Realizations Implementationstrengthslimitations Analytical modeling mathematical models and software rigorous analysis and design challenge to achieve right abstraction Simulations OPNET and OMNINET software on workstations broad what-if capability limited reflection of networks ANUE emulations laboratory hardwarecloser to actual network high-cost; non- mobile Fiber loop or USN connections laboratory hardware, fiber spools closest to actual network highest cost; non-mobile

13. Approach: 1.Collect simulation or emulation measurement for 2.Apply differential regression to obtain the estimate Differential Regression Method for Cross-Calibration Basic Question: Predict performance on connection length not realizable on USN Example: IB-RDMA or HTCP throughput on 900 mile connection Measurements on OPNET simulated path of distance d Measurements on USN path distance Measurements on Anue emulated path of distance d Regression of measurements on Measurement Regression: for Differential Regression: for simulated/emulated measurements point regression estimate

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

15. 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 Analysis of iperf and disk transfer measurements