Research Project / Applications SeminarSYST 798

FINAL REPORT Second Dry-Run 24 April 2008

Team: Tom Hare Ali Raza Brian Boynton Eric Ho Matt Maier

Key Sponsor: Dr. Kuo-Chu Chang

What is Biological Sensor Fusion?

“Improve Urban Biological Terrorism Response”

Biological

Sensor

Fusion

Responding to National

Biological Threats

Robust Multi-Tiered Detection

System

Data Integration for Immediate

Application and Response

Sponsor Information

• GMU SEOR: Homeland Security and Military Transformation Lab

• Dr. Kuo-Chu Chang, Professor, GMU

– KChang@gmu.edu– http://ite.gmu.edu/~kchang/

• Dr. Kathryn Blackmond Laskey, Professor, GMU

– klaskey@gmu.edu– http://ite.gmu.edu/~klaskey/

• Held Sponsor Meetings and Project Demos

– 7 Feb, 20 Feb, 6 Mar, 20 Mar, 3 Apr, 16 Apr

Why is a Biological Attack a Threat ?

“Smallpox has killed more people than any other disease in history, including bubonic plague, at least 300M in the 20th Century.” (U.S. Public Health Service)

Centers for Disease Control (CDC)Listing of Potential Bioterrorism Agents

Category A Category B Category C

- High priority- Easily transmitted from person to

person- High mortality rates- Potential for major public health

impact- Can cause public panic/ social

disruption- Requires special action for public

health preparedness

- Second highest priority- Moderately easy to disseminate- Moderate morbidity and low

mortality rates- Specific diagnostics required- Enhanced disease surveillance

- Third highest priority- Emerging pathogens- Could be engineered for mass

dissemination- Available- Easy production and

dissemination- Potentially high morbidity and

mortality- Major health impact

AnthraxBotulismPlagueSmallpoxTularemiaViral Hemorrhagic Fevers

BrucellosisGlandersMelioidosisPsittacosisQ FeverTyphus FeverViral EncephalitisToxinsFood SafetyWater Safety

NipahHantavirus

Source:http://www.hsarpabaa.com/Solicitations/HSARPA_RA-03-01_Appendices.pdf http://www.hsarpabaa.com/Solicitations/HSARPA_RA-03-01_Body.pdf

Research Conducted

• Technical Paperwork– 60+ articles/papers/books to include 20+ sponsor-

provided technical papers

• Subject Matter Expert (SME) Interviews– Use Case: Chicago – Sensor Technology– Modeling Methods

• Research Areas– Detection: Tiered sensor grid– Fusion: Data Aggregation and Geo-Location– Communication: Epidemic, gossip, and geographic

algorithms– Response: Real-time cordon mapping in changing

environment– Technology: State of the art in 2008 and forthcoming by

2020

Earl W. Zuelke Jr., Deputy Director, Homeland Security & Emergency

Management for the City of Chicago

Photo Courtesy Chicago Police Marine Unit

Objective

• Objectives: – Minimize the time it takes to

inform the public of a biological attack

– Design End-to-End System for constant monitoring of urban environment

• Determine system architecture

• Optimize communication parameters and algorithm usage

• Model usage of current sensor technology

• Architectural products will be incorporated through out the presentation

Operational Concept Graphic (OV-1)

Use Case Scenarios

An individual with infectious Smallpox arrives in Chicago by

car, plane or shipVisual Indicators:• Lesions or rash on person, coughing, feverActions:• Transportation and border staff likely to alert hospitals and confine person

Tier I Sensor in airport and Tier II sensors in arrival/departure zones will detect.

A terrorist sprays aerosolized Smallpox into the air at

Chicago’s Soldier Field Stadium

Visual Indicators:• Minimal, act could be mistaken for a fan with an air hornActions:• Nothing for 7-17 days until Smallpox incubation period is complete, and epidemic outbreaks

Tier I Sensor in stadium and Tier II sensors in parking lot will detect.

A terrorist sprays aerosolized Anthrax in a crowded subway

stationVisual Indicators:• Cloud of dry spores evident• Nearby people would get dust on clothes and shoes Actions:• Maybe none, depending on how obvious actions are• 2001+ people sensitive to powders

Tier II Sensor deployed inside or outside subway will detect.

A terrorist sprays aerosolized Tularemia outside a Chicago

monument

Visual Indicators:• Minimal, act might not be noticed by tourists/ passersbyActions:• Nothing for 3-5 days until Tularemia incubation period is complete, and epidemic outbreaks

Tier II sensors, as they move through vicinity, will detect.

Scenario Diagram

Operational Event-Trace Description (OV-6c)

High-Level Requirements

• “Improve Urban Biological Terrorism Response”– Lack of detection and fusion today– Slow response times cost lives– False positives cost money

• Biological Sensor Fusion System Requirements:– The Biological Sensor Fusion Group shall create a representative architecture of a

BioSensor System in an urban environment that will include a model that depicts the fusion of data and establishment of an effective cordon.

– The Biological Sensor Fusion Group shall design a solution to provide for fast Data Fusion to facilitate effective command decision-making.

Operational Activity

Decomposition (OV-5, Node Tree)

Millennium Park, Chicago. Photo Courtesy 80s Forum

BioSensor Fusion Context

• System Context– Within Chicago (Pop. 2.9 mil) there is a

potential for 575,000 deaths or more if unchecked

– Realistically, given fast emergency response, roughly 35+ fatalities would occur

– Exorbitant expense– Current response plans would not allow

for detection or response before 3-4 days

– Our Model will investigate employment of both current, and state of the art technology that will not be put into operation for another 10 years

Anthrax Spores, Photo Courtesy of Wired, 10 October

2001 AP Photo

Smallpox Effects, Photo Courtesy of PBS NOVA Online,

Bioterror, November 2001

Biological Sensor Fusion – System Context

Assumptions and Constraints

• Accuracy prioritized over fast detection– False alarms that shut down facilities and

displace people can rival the cost of an actual outbreak ($750+ million)

• Technology Limitations– Tier 1 Sensors require 24 hours to scan the air

within their range, Tier 2 (Mobile) Sensors require 4 hours to make a positive identification, and Tier 3 Sensors can detect a Biological Agent within 2 hours of deployment.

• Biological Attack Versatility– The Sensor Technology Deployed can scan and

positively identify a large number of potential Biological Agents, not just smallpox, so this project can generally be considered a template in modeling a response to any type of Biological attack.

Overview and Summary

Information (AV-1)

System Technology

Tier II: Mobile Ad Hoc Sensors– Deployed in emergency response

vehicles (Emergency, Police, Fire, HAZMAT, etc.)

Example:Biowatch 3 Bioagent

Autonomous Networked Detector (BAND)

Example:General

Dynamics Biological

Agent Warning Sensor (BAWS)

Tier I: Stationary Sensors – Permanent, round-the-clock air-

sampling, building installed indoor and outdoor

Tier III: Stationary Ad Hoc Sensors

– Scattered after a threat is confirmed

– Provide tracking of dispersion

Example:Future

Sensors, Pacific

Northwest National

Laboratory Systems Performance Parameters Matrix (SV-7)

Modeling and Simulation

• Colored Petri Nets Model– Analyzes effectiveness of Small World Communication (6

degrees of separation)– Evaluates Delivery Rate vs. Data Buffer Size

• JAVA Algorithm Model– Models the ad-hoc communication of the sensor network– Evaluates 6 different algorithms

• Epidemic-SI, Epidemic-SIS, Epidemic-SIR, Gossip, Gossip Enforced Ending, Geographic Forwarding Geocast

– Evaluates Range, Latency, Hop Count, Coverage, Neighbors, and Remaining Power

– Emulates an Operations Center User Interface

CPN Methodology

• Small World Networks– Data can be transferred from any node in the network to any

other node in the network in 6 hops or less.

Small World Networks, Courtesy George Washington University

School of Engineering and Applied Science

CPN Model

CPN Analysis

Conclusions:• Optimal Delivery Rate is achieved with Data Buffer Size <300 packets• Upcoming Ad-Hoc Communications Model uses 276 as buffer size

Delivery Rate vs Number of Packets

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

0 150 300 450 600 750 900 1050 1200 1350 1500

Data Buffer Size (# of Packets)

Del

iver

y R

ate

(%)

Communications Networks

• Dedicated/ Fixed – Connection Oriented– Between Tier I Sensors– Between Operations Center and CDC and WHO Headquarters– Between Operations Center and Emergency Response / HAZMAT

Elements– Between Emergency Response Elements– Use Current Technology: Land-line Phone, Trunk Mobile Radio,

WiMax, Fixed LOS, etc.

• Ad Hoc - Connectionless– Between Tier II Sensors and Operations Center/Tier I– Between Tier III Sensors when deployed– Simple Multicast Protocol:

Sense Time Window Communications Time Window

Packet Burst

DestNode ID

SourceNode ID

Detect LongitudeDetect Latitude

A Single Packet: 16 Byte Message

Checksum

Length

64 bits 64 bits

Sensor A and B communicate when in range of each other.

Time required to transmit exactly one packet

… (repeats)time

Powerused

A B

Photos Courtesy KEMRON and Rescue Response Gear

ErrorFlags

ThreatType

ThreatPPM

Ad-Hoc Communications Model Demo

Systems Node Communications Descriptions (SV-2)

Communications Networks (cont.)

Analysis: Range vs. Latency

Communications Range Analysis:Range vs. Latency

0

300

600

900

1200

1500

1800

0 100 200 300 400 500

Communications Range (meters)

La

ten

cy

(s

ec

on

ds

)

Min

Max

Log. (Max)

Conclusions:• Optimal Communications Range: 250m+• Optimal Sensor Range 150m+• Latency can be reduced to under 5 minutes

Optimized Sensor Range Analysis:Range vs. Latency

0

300

600

900

1200

1500

0 100 200 300 400 500

Sensor Range (meters)

La

ten

cy

(s

ec

on

ds

)

Min

Max

Analysis: Hop Count

Optimized Sensor Range Analysis:Range vs. Hop Count

0

1

2

3

4

5

6

7

8

0 100 200 300 400 500

Sensor Range (meters)

Ho

p C

ou

nt

Re

qu

ire

d t

o R

ec

eiv

e A

ll B

io

Th

rea

ts

Min

Max

Conclusions:• Hop Count is roughly linear with Latency• Hop count (when optimized) is six degrees of separation or less: “Small World Communication”

Hop Count vs. Latency, Unoptimized

0

10

20

30

40

50

60

70

0 200 400 600 800 1000 1200 1400

Latency (seconds)

Ho

p C

ou

nt (

Qty

)

Analysis: Neighbors and Coverage

Communications Range Analysis:Range vs. Number of Neighbors

0

10

20

30

40

50

60

0 100 200 300 400 500

Communications Range (meters)

Nu

mb

er

of

Ne

igh

bo

rs

Min Max

Conclusions:• Neighbors increases exponentially with communications range• Coverage increases logarithmically with sensor range• With optimal ranges, neighbors will typically be 0-25 (largely disconnected), and coverage 75% or less

Optimized Sense Range AnalysisRange vs. Sensor Coverage

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

0 100 200 300 400 500

Sense Range (meters)

To

tal D

istr

ict

Se

ns

or

Co

ve

rag

e (

%)

Coverage %

Epidemic-SI

0

0.2

0.4

0.6

0.8

1

0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200

Latency (s)

Del

iver

y R

ate

(%)

Epidemic-Susceptible Infective

Conclusions:• As previously demonstrated, Epidemic-SI is quite fast and data can be expected to arrive in 5 minutes or less.• 100% Delivery Rate was achieved for all runs

Algorithm Conclusions

Latency by Algorithm

0

5

10

15

20

Epidemic-SIR Epidemic-SI Gossip EnforcedEnding

Geocast Epidemic-SIS Gossip

Algorithm

Late

ncy

(Min

utes

)

Delivery Rate by Algorithm

0

20

40

60

80

100

Epidemic-SI Gossip EnforcedEnding

Geocast Epidemic-SIS Gossip Epidemic-SIR

Algorithm

Deliv

ery

Rate

(%)

Conclusions:• Epidemic-SIR is best for Latency but worst for Delivery Rate• Epidemic-SI is best overall

Power Conservation by Algorithm

Remaining Tier III Sensor Energy

8450

5080

8680

3150

7150 7050

94309150 8980

82508550

9425

9920 99109560 9720 9800 9900

0

2000

4000

6000

8000

10000

Epidemic-SI Epidemic-SIS Epidemic-SIR Gossip Gossip EnforcedEnding

Geocast

Algorithm Type

Rem

ain

ing

En

erg

y (J

ou

les)

Conclusions:• Epidemic-SI, Epidemic-SIR, Gossip Enforced Ending, and Geocast all have >90% Energy Conservation

Optimal Communications Analysis Conclusions

• Parameters are feasible for current biological sensors in development

• Low sensor ranges provided the best geo-location accuracy

• For speedy delivery performance, “Small World Communications” is best

• A fused DHS Operations Center result is reasonable in under 5 minutes after biological agent detection.

Parameter Optimal Value

Algorithm Epidemic-SI

Latency < 5 Minutes

Communications Range

250+ meters

Sensor Range 150+ meters

Hop Count < 6

Coverage < 75%

Neighbors 0-25, includes disconnection

Delivery Rate > 98%

Data Buffer Size < 300 Packets

Final Thoughts

• Biological Sensor Fusion– Lack of swift response to biological

attack/outbreak scenarios could produce economic loss estimated conservatively at $750M+ and 35+ deaths

– Our system could provide full response within 24-36 hours, preventing deaths and significantly lowering cost for vaccinations, cleanup, and decontamination

– Fusion of data is necessary for responders to target a biological threat real-time

– System demonstrates the use of ad-hoc and mobile ad-hoc communications in a real-world scenario, and is a high interest research area in DoD and DHS

Chicago IllinoisPhoto Courtesy of Destination 360, 2008

Future Work

• Prevention and Treatment– Vaccination Distribution Scenarios– Counter-proliferation Options– Isolation and Treatment Options– Emergency Response Training

• Sensor Research/ Design– Deployment Scenarios– Advanced Technologies:

• UV Fluorescence, Laser-Induced fluorescence, isothermal arrays, genetic classification, electromagnetic spectroscopy, and microfluidics

• Additional Modeling– Biological agent dispersal/ movement– Local sensor processing and data fusion algorithms– Fusion of hospital/medical practitioner data with sensor data– Buffer Size, Cache, Anti-Entropy analyses– Modification of model for other types of EW, ISR or CBRNE sensors– Military Applications

Future Sensors, Pacific

Northwest National

Laboratory

Acknowledgments

• GMU SEOR Homeland Security and Military Transformation Lab– Dr. K.C. Chang

• GMU Faculty and Staff– Dr. Kathryn Blackmond Laskey– Dr. Abbas Zaidi– Others

• City of Chicago– Mr. Earl Zuelke

• U.S. Genomics– Mr. David Hoey

• Cornell University– Dr. Paul Chew

Project Management Available inBackup

Questions?

Backup Slides

EVMS

Addressing Risk

• C1: Actual Hours exceed Plan– Mitigation Strategy: (a) Track EVM Weekly to ensure

work is appropriately applied to WBS Tasks; (b) Determine value added tasks on critical path; (c) Discuss analyses tradeoffs with program sponsor

– Risk Closure: End of Performance (EOP)• S1: Actual hours don’t match planned

requirements in WBS categories– Mitigation Strategy: (a) Realign tasks for best

utilization of manhours; (b) Frequent coordination with program sponsor on project scope

– Risk Closure: EOP• P1: Modeling efforts do not support analyses

needed– Mitigation Strategy: (a) Update models with

parameters as needed to support robust analyses; (b) Scope analysis and results with program sponsor

– Risk Closure: EOP• P2: Current Sensor Technology does not support

fast data collection and fusion at an Operations Center– Mitigation Strategy: (a) Coordinate sensor

characteristics with current vendors; (b) Investigate future sensor development plans; (c) Address sensor limitations through modified communications strategies

– Risk Closure: EOP

• P3: Communications Method chosen fails to be adequate for biological threat response– Mitigation Strategy: (a) Alter communications

methodology for Tier I/II/III as needed; (b) Design communications parameters to produce best response times

– Risk Closure: EOP

C1

S1P1

P2

P3

Work Breakdown Structure (WBS)

Research Conducted

• DoD and DHS Requests for Proposal (RFPs) on Future Biosensors– Feb 2006: DARPA Biological Warfare Defense Project, $750M+ FY08-FY11– Apr 2004: HSARPA Bioagent Autonomous Networked Detectors (BAND), Rapid

Automated Biological Identification System (RABIS), $48M 18mo periods of performance

• Researched Future Biosensor Development– Johns Hopkins University's

Applied Physics Laboratory of Laurel, MD

– Ionian Technologies, Inc. of Upland, CA

– Goodrich Corporation of Danbury, CT

– Battelle Memorial Institute of Aberdeen, MD

– Physical Sciences, Inc. of Andover, MA

– Research Triangle Institute of Research Triangle Park, NC

– Northrop Grumman Systems Corporation of Linthicum, MD

– MicroFluidic Systems, Inc. of Pleasanton, CA

– Science Applications International, Inc. of San Diego, CA

– U.S. Genomics, Inc. of Woburn, MA

– IQuum, Inc. of Allston, MA – Nanolytics, Inc. of Raleigh, NC – Sarnoff Corporation of Princeton,

NJ– Brimrose Corporation of

Baltimore, MD

Architecture Products

• All Views– AV-1

• Operational Views– OV-1– OV-2– OV-3– OV-5: Node Tree & IDEF0– OV-6c

• System Views– SV-1– SV-2– SV-3– SV-4– SV-5– SV-6– SV-7

System Parameters

• Communications Parameters– Algorithm Used– Range– Burst Time– Tx/Rx Power– Reliability– Data Buffering– Fusion Cordon

• Sensor Parameters– Quantity– Sensor Lat/Long– Sensor Movement– Range– Sensitivity– Specificity– False Positive Rate– Sense Time– Sense Power– Coverage

• Analysis Results– Latency– Delivery Rate– Hop Count– Coverage– Remaining

Power– Neighbors– Algorithm Type

Epidemic-SIS

0

0.2

0.4

0.6

0.8

1

0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200

Latency (s)

Del

iver

y R

ate

(%)

Epidemic-Susceptible Infective Susceptible

Conclusions:• Epidemic-SIS is slower, with Latency results in 1-15 minutes• Two runs only had 90% Delivery Rate, likely due to the delay in becoming susceptible again, although it can be inferred that this data could eventually arrive as long as sensor power remains.

Epidemic-SIR

0

0.2

0.4

0.6

0.8

1

0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200

Latency (s)

Del

iver

y R

ate

(%)

Epidemic-Susceptible Infective Removed

Conclusions:• Epidemic-SIR is the fastest of all algorithms, with Latencies of 3 minutes or less• It is also the worst for Delivery Rate, with 13% of data on average not received

Gossip

0

0.2

0.4

0.6

0.8

1

0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200

Latency (s)

Del

iver

y R

ate

(%)

Gossip

Conclusions:• Gossip is the slowest of all algorithms, with Latencies as high as 20 minutes• One run only achieved 90% Delivery Rate, although it can be inferred that this data could eventually arrive as long as sensor power remains.

Gossip Enforced Ending

0

0.2

0.4

0.6

0.8

1

0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200

Latency (s)

Del

iver

y R

ate

(%)

Gossip Enforced Ending

Conclusions:• Gossip Enforced Ending speeds up latency from regular gossip, but still is only moderately fast, with Latencies in the 3-8 minute range.• One run only achieved 90% Delivery Rate• 100% delivery is not ensured with this algorithm.

Geographic Forwarding Geocast

0

0.2

0.4

0.6

0.8

1

0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200

Latency (s)

Del

iver

y R

ate

(%)

Geographic Forwarding Geocast

•Conclusions:• Geographic Forwarding is slightly faster that Gossip Enforced Ending but not as good as Epidemic-SI• Latencies are typically 3-9 minutes• One run only achieved 90% Delivery Rate• 100% delivery is not ensured with this algorithm.

Conclusions:• At low range, remaining power has a wide variance. This is due mainly to many communications hops and sensing periods, which has a large impact on power.• Low range yielded cases with still very good power conservation in the network.• Communications Ranges beyond 250m+ have little and even sometimes a detrimental effect on power conservation.• With optimal communications, Sensor Range has a slight impact on remaining power, only at ranges <100meters.

Analysis: Power Remaining

Optimized Sensor Range Analysis:Range vs. Tier III Remaining Power

4

5

6

7

8

9

10

0 100 200 300 400 500

Sensor Range (meters)

Re

ma

inin

g P

ow

er

(kJ

ou

les

)

Min

Max

Communications Range Analysis:Range vs. Tier III Remaining Power

0

1

2

3

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5

6

7

8

9

10

0 100 200 300 400 500

Communications Range (meters)

Re

ma

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(kJ

ou

les

)

Min

Max

System Performance Parameters

Sensor Design Parameter Optimum Value Communications Design Parameter

Optimum Value

Tier I Sensor Quantity 6 Communications Algorithm Epidemic-SI

Tier II Sensor Quantity 120 Communications Range 250m+

Tier III Sensor Quantity 150 Communication Time Window 20 Sec

Sensor Range 150m+ Communications Burst Time (per packet)

5mS

Sensitivity 99.9%+ Minimum Time Step 1mS

Sensitivity Error <0.5% Tier I Transmission Power 50W

Specificity 99.9999999%+ Tier II Transmission Power 5W

False Positive Rate <1E-7% Tier III Transmission Power 10mW

Sense Time <4 hours Receive Energy 10J

Sense Data Updates Every 120 Secs Multi-path Loss 2.5%

Tier III Sense Energy 50J Latency <5 mins

Tier III Total Available Energy 10000J Hop Count <6

Sensor Coverage 35%+ Delivery Rate 100%

Neighbor Quantity 0-25, Includes Disconnection Data Buffer Size <300