Biological Sensor Fusion for Urban Terrorism Response

1. Research Project / Applications Seminar SYST 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

2. What is Biological Sensor Fusion? “Improve Urban Biological Terrorism Response” Biological Responding to National Biological Threats Sensor Robust Multi-Tiered Detection System Fusion Data Integration for Immediate Application and Response

3. 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

4. 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)

5. Centers for Disease Control (CDC)Listing of Potential Bioterrorism Agents Source:http://www.hsarpabaa.com/Solicitations/HSARPA_RA-03-01_Appendices.pdf http://www.hsarpabaa.com/Solicitations/HSARPA_RA-03-01_Body.pdf

6. 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

7. 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)

8. Use Case Scenarios An individual with infectious Smallpox arrives in Chicago by car, plane or ship A terrorist sprays aerosolized Smallpox into the air at Chicago’s Soldier Field Stadium • Visual Indicators: • Lesions or rash on person, coughing, fever • Actions: • Transportation and border staff likely to alert hospitals and confine person • Visual Indicators: • Minimal, act could be mistaken for a fan with an air horn • Actions: • Nothing for 7-17 days until Smallpox incubation period is complete, and epidemic outbreaks Tier I Sensor in airport and Tier II sensors in arrival/departure zones will detect. Tier I Sensor in stadium and Tier II sensors in parking lot will detect. A terrorist sprays aerosolized Anthrax in a crowded subway station A terrorist sprays aerosolized Tularemia outside a Chicago monument • Visual 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 • Visual Indicators: • Minimal, act might not be noticed by tourists/ passersby • Actions: • Nothing for 3-5 days until Tularemia incubation period is complete, and epidemic outbreaks Tier II Sensor deployed inside or outside subway will detect. Tier II sensors, as they move through vicinity, will detect.

9. Scenario Diagram Operational Event-Trace Description (OV-6c)

10. 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. Millennium Park, Chicago. Photo Courtesy 80s Forum Operational Activity Decomposition (OV-5, Node Tree)

11. 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

12. Biological Sensor Fusion – System Context

13. 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)

14. System Technology • Tier I: Stationary Sensors • Permanent, round-the-clock air-sampling, building installed indoor and outdoor Tier II: Mobile Ad Hoc Sensors • Deployed in emergency response vehicles (Emergency, Police, Fire, HAZMAT, etc.) Example: General Dynamics Biological Agent Warning Sensor (BAWS) Example: Biowatch 3 Bioagent Autonomous Networked Detector (BAND) • 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)

15. 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

16. 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

17. CPN Model

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

19. 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: Photos Courtesy KEMRON and Rescue Response Gear Packet Burst Power used … (repeats) time A B Sense Time Window Communications Time Window A Single Packet: 16 Byte Message Threat Type Threat PPM Source Node ID Dest Node ID Length Error Flags Check sum Detect Latitude Detect Longitude Sensor A and B communicate when in range of each other. 64 bits 64 bits Time required to transmit exactly one packet

20. Ad-Hoc Communications Model Demo Communications Networks (cont.) Systems Node Communications Descriptions (SV-2)

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

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

23. Analysis: Neighbors and Coverage • 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

24. 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

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

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

27. 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.

28. 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 Chicago IllinoisPhoto Courtesy of Destination 360, 2008 • 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

29. 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

30. 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

31. Project Management Available inBackupQuestions?

32. Backup Slides

33. EVMS

34. 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 P2 C1 P3 P1 S1

35. Work Breakdown Structure (WBS)

36. 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 • 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 • 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

37. 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

38. System Parameters • Analysis Results • Latency • Delivery Rate • Hop Count • Coverage • Remaining Power • Neighbors • Algorithm Type • 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

39. 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.

40. 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

41. 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.

42. 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.

43. 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.

44. Analysis: Power Remaining • 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.

45. System Performance Parameters