NITRD/LSN Workshop On

Complex Engineered Networks

September 20-21, 2012Washington DC

Sponsored byAFOSR

NSFDOE

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• OthersSeveral underlying foundational areasrelated to detection networks are open

Difficulty of Detecting Low-level Radiation SourcesThe 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 measurementsWell-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

Individual, Lo-Located, Collective and Network detection

: , ; 1, 2, , ;L H ii i N n n L

ˆ:;

Sn nNww

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Individual Sensor Detection with thresholds

Co-located Sensors: Detection with threshold

Network Detection with Localization: localize the source first and detect it

: , ;L H

n n L

Collective Detection with threshold

: , ;L H

n nN wwL

Relative Performance: Individual, Lo-Located, Collective and Network detectionfor SPRT based detection methods

: ,ˆ:

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: , : ,

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in Mi S

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network with

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sensor with threshold

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superiority due to localization

superiority due to more measurements

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sensor detection with threshold

co-located sensors with threshold

network with localization

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By combining currently available analytical results

- theory leads to new detection method

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

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commandand

control

STcom

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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-stampedstate estimates

Sensor/Estimator

phenomenon

7

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

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

Example C: UltraScience NetExperimental 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

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 linuxhost

linuxhost

ANUE10GigE

emulator NexusNexus

linuxhost

linuxhost

ANUEOC192

emulator

E30010GigEswitch

OC192

Overview of Network Simulations, Emulations and Realizations

Implementation strengths limitations

Analytical modeling

mathematical models and software

rigorous analysis and design

challenge to achieve right abstraction

SimulationsOPNET and OMNINET

software on workstations

broad what-if capability

limited reflection of networks

ANUE emulations

laboratory hardware

closer to actual network

high-cost; non-mobile

Fiber loop or USN connections

laboratory hardware, fiber spools

closest to actual network

highest cost; non-mobile

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 USNExample: IB-RDMA or HTCP throughput on 900 mile connection

( )SM d Measurements on OPNET simulated path of distance d

Measurements on USN path distance

d

d

( )EM d Measurements on Anue emulated path of distance d

( )U iM did

(.)AM Regression of measurements on

, ,A S E UMeasurement Regression: for

Differential Regression: for

, (.) (.) (.)A B A BM M M

, , , , ,A S E U B S E U

,ˆ ( ) ( ) ( )U C C UM d M d M d

,C S E

simulated/emulatedmeasurements

point regressionestimate

Wide-Area Infiniband Throughput:18 Different Configurations: physical and

emulatedphysical connections

emulated connectionsEmulations provide good approximation at very low cost

• 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