Research Project / Applications SeminarSYST 798

FINAL REPORT Brief Dry-Run 3 April 2008

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

Key Sponsor: Dr. Kuo-Chu Chang

Why is Smallpox a Threat ?

• < 1972: Vaccination required before entering school • World Health Organization declare eradication in 1977• Antibodies may decline after 10 years• Population 36 years and younger = 47% USA (approx. 140 million)• Militarized smallpox is only source (USA and Russia)

• Two days of life when released• Variola major epidemics – 30% or higher among unvacinnated

Today1972 2018

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/

Research Conducted

• Researched Technical Paperwork– 60+ articles/papers/books on biological threats, sensors, communications

algorithms, disease characteristics, Chicago city characteristics, etc.– Sponsor provided over 20 technical papers in BSF research area

Earl W. Zuelke Jr., Photo Courtesy Chicago Police Marine Unit

Conducted Subject Matter Expert (SME) Interviews– Mr. Earl Zuelke, Deputy Director, Homeland Security &

Emergency Management for the City of Chicago– Mr. David Hoey, Vice President, Business Development, US

Genomics, DARPA BAND Biosensor Development Program– Mr. Alan Northrup, Chief Technical Officer for Sensors,

MicroFluidic Systems, Inc.– Mr. Paul Cabellon and Ms. Alleace Gibbs, Northrop

Grumman, Aerospace Systems Division, CBRNE Business Area

– Dr. Paul Chew, Cornell University, Delaunay/ Voronoi Algorithm Modeling

– Mr. Abbas Zaidi, CPN Modeling

Research Conducted (cont.)

• Held Sponsor Meetings and Project Demos– 7 Feb, 20 Feb, 6 Mar, 20 Mar, 3 Apr

• 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

BioSensor Fusion Aim

• Objectives: – Minimize the time it takes to

inform the public of a biological attack based on Sensor-determined dispersal of attack

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

• Determine optimal communications parameters and algorithm usage for Sensor Grid

• Model usage of current sensor technology

Problem Statement

• “Improve Urban Biological Terrorism Response”– Lack of detection and fusion today

– Slow response times cost lives

– False positives cost money

• Biological Sensor Fusion proposes solutions for:– 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

Millennium Park, Chicago. Photo Courtesy 80s Forum

BioSensor Fusion Background

• The Modeled Use Case– Smallpox released on the order of 10

billion organisms (~1 g) to contaminate a heavily trafficked urban area

– Terrorists would spray the pathogen into the air

• System Context– Within a city the size of Chicago there is

a potential for 575,000 deaths or more

– 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

Sears Tower, Chicago. Photo Courtesy

Wikimedia Commons

Biological Sensor Fusion – System Context

BioSensor Fusion Project

• BioSensor Team took a 3-Pronged approach to addressing the problem of a Biological Terrorist attack in a domestic Urban environment– Architecture Products

• Provides general information about system as well as providing Context for Algorithm and CPN Models as well as general information on system for client

– JAVA Algorithm Model• Models the communication of the Sensor Grid upon

confirmed detection of a Biological attack in an analysis of the efficacy of 5 different algorithms

– Coloured Petri Nets Model

Analyzes effectiveness of varying numbers and coverage areas of Sensor types, there being 3 types total

System Design

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)

Example: ICX Mesosystems

BioBadge™ 100 Wearable Air

Sampler

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

sampling, building installed indoor and outdoor, high-regret

Tier III: Stationary Ad Hoc Sensors– Scattered after a threat is confirmed

or incorporated in small, personal devices like cell phones

– Provide low-regret tracking of dispersion

System Design (cont.)

• 276 sensors deployed in Chicago District 001– 1 Operations Center and 5 additional Tier I– 120 mobile Tier II– 150 ad-hoc Tier III

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

rival the cost of an actual outbreak (~$750 billion)

Assumptions and Constraints

• The Bioterrorist attack occurs in District 1 of the City of Chicago, where there are approximately 575,000 people circulating as a result of the high density of attractions and tourism associated with this District

• A Biological terrorist attack has occurred and either Tier 1 or Tier 2 Sensors, have detected a Bio-attack of smallpox virus at a minimum of 4 hours before lab analysis can occur and District Cordoning can be implemented. Evacuation plan is executed while Tier 3 sensors are additionally deployed to the specific suspected attack area. The Tier 3 sensors are deployed to further narrow down the location of the attack and isolate further zones for evacuation and cleaning

• The Bioterrorist attack involves the physical dissemination of ~1g of the Smallpox organism (can fit on the head of a pin)

• The smallpox virus initially infects 150 people upon deployment and is deployed to only one street, limited to 1 block of potential dispersion, and will continue to infect people for 24 hrs

• The incubation period for smallpox is at least 7 days long; on average it takes 12 days for someone to be contagious once exposed to the virus, so infection is not being spread from person to person within the context of this system

• Avoiding False Positives is considered to be of prime importance: it was gleaned from our subject matter expert that a full response to a False Alarm of a Bioattack can be just as destructive as the attack itself in monetary terms

EVMS

Actual Vs. Estimated Man Hour Cost

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T i me (Weeks )

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Architecture Products

• All Views– AV-1 (in development)

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

• System Views– SV-1– SV-2– SV-4– SV-5– SV-6– SV-7 (in development)

DHS OperationsCenters

First Responders

Stationary Sensor /Others

Mobile Sens or

Air-bourne Sensor

Pre-deployedBio SensorActivities

"Detect Biothreat"

HealthCareCenter

Activities"Detect Bio

threat"

911 Call

Ad-Hoc SensorNetwork

Pre-deployed Sensor - DHS Operations CenterHealthCare Center - DHS Operations Center

911 Call - DHS Operations Center

Command Center HQ - Stationary Sensor

Stationary Sensor - Command Center HQ

DHS Operations Center - Air-bourne Sensor

Air-bourne Sensor - DHS Operations Center

DHS Operations Center - Mobile Sensor

Mobile Sens or - DHS Operations Center

First Responders - Air-bourne SensorAir-bourne Sensor - First Responders

Mobile Sens or - First Responders

First Responders - Mobile SensorStationary Sensor - First Responders

First Responders - Stationary Sensor

DHS Operations Center - First Res ponders

First Responders - DHS Operations Center

-Number of Sensor type II was cut in half-Sensor Ranges are the same-Increase of latency at early stage

Latency vs Bio Agent DistanceSensor Ratio (I / II / III): (5 / 120/ 150) vs (5 / 60 / 210)

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Bio Agent Distance (m)

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Latency 2

CPN Model

CPN Model

-Number of Sensor type II was cut in half-Sensor Ranges are the same-Number of Hops are the same

Latency vs Number of HopsSensor Ratio (I / II / III): (5 / 120/ 150) vs (5 / 60 / 210)

05

101520253035404550556065707580859095

100105110115120

0 2 4 6 8

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CPN Model

JAVA Model Analysis: Latency

Communications Range Analysis:Range vs. Latency

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Communications Range (meters)

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Conclusions:• Latency includes both re-sense time and communications time.• Latency is statistically bounded for a given range.• Latency decreases logarithmically as range increases.• Latency variance decreases with increased range.• Communications Ranges 200m+ do not provide significant added benefit.• Sensor Ranges 150m+ do not provide significant added benefit.• With optimum communications and sensor ranges, latency is typically 3 minutes or less

Optimized Sensor Range Analysis:Range vs. Latency

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Communications Range Analysis:Range vs. Hop Count

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Analysis: Hop Count

Optimized Sensor Range Analysis:Range vs. Hop Count

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Conclusions:• Hop Count decreases faster than Latency (nonlinear) as range increases. This is due to the high level of disconnection in the network at low ranges.• Hop Count variance decreases as range increases.• Variance in minimum and maximum hops due to the arrival of buffered data via separate paths.• No significant improvement for ranges 250m+• Hop Count never decreases to less than 1• Hop count (when optimized) is six degrees of separation or less: “Small World Communication”

Analysis: Neighbors and Coverage

Communications Range Analysis:Range vs. Number of Neighbors

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Optimized Sensor Range AnalysisRange vs. Sensor Coverage

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Sensor Range (meters)

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%)

Coverage %

Log. (Coverage %)

Conclusions:• Neighbor quantity increases exponentially with communications range, but is not affected by sensor range• Coverage increases logarithmically with sensor range, but is not affected by communications range• In both cases, variance increases as range increases• With optimal ranges, neighbors will typically be 0-25 (largely disconnected), and coverage 30% or less

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

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Communications Range Analysis:Range vs. Tier III Remaining Power

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Analysis Conclusions

• Communications Range – Optimal 250m+– This is feasible with a 5 watt 2.4 GHz transmitter on ad-hoc sensors

• Sensor Range – Optimal 150m+– This is feasible e for current biological sensors in development– Low sensor ranges provided the best geo-location accuracy

• Hop Count – Optimal <6– Hop Count and Latency are not precisely linearly related. Latency could occur

while a mobile node is disconnected from the network– For speedy delivery performance, “Small World Communications” is needed

• Coverage – Optimal <30%– Only impacted by Sense Range

• Neighbors – Optimal 0-25, includes disconnection– Only impacted by Communications Range

• Power – [Data Needed]– Algorithm xxx provides best power conservation– Less than 10% Tier III power on average is needed when optimally configured

• Fusion– A fused DHS Operations Center result is reasonable in under 5 minutes after

biological agent detection.– Sense time has the most impact on overall time to respond to a biological threat.

Final Thoughts

• Biological Sensor Fusion– Research into prior Biological attack/outbreak

scenarios lead us to project an economic loss of $750M and approximately 35 deaths given a status quo, lack of swift response

– Our model dictates a full response within 24-36 hours, allowing no deaths and significantly lower cost for vaccinations, cleanup, and decontamination as a result of Sensor Grid Geo-Location of threat

– Our model demonstrates the fusion of data for responders to target a threat real-time, 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

Future Work

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

• Sensor Research/ Design– Advanced Technologies: UV Fluorescence, Laser-Induced fluorescence,

isothermal arrays, genetic classification, electromagnetic spectroscopy, and microfluidics

– Deployment Scenarios

• 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

Backup Slides

CPN Model

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