Guide to the Biological Sensor Fusion Java Model Creation, v0.2

1. Guide to theBiological Sensor FusionJava Model Creation, v0.2 Matt Maier 3 Mar 2008

2. Tiered Sensor Approach • Recall that our BSF Model has three Tiers • Tier I • Pre-positioned large multi-mode sensors capable of detecting a variety of nuclear, biological and chemical agents. • Tier I are the large fixed sites, like hospitals, police stations, bridges, etc. and are “Fixed” (meaning they do not move and the communications infrastructure can be pre-determined) • Tier I uses two types of data: biological threats sensed by multimode sensors and epidemic reports from doctors ready to be exchanged with the Ops Center • Characteristics: Always powered, high-bandwidth, long range point-to-point communications, might also include air cleaning capabilities, such as HEPA filters and pathogen capture and elimination mechanisms • Tier II • Tier II are vehicle mounted sensors on ambulances, police cars, etc. and are “Mobile Ad Hoc” (meaning they move and location cannot be predetermined) • Used where a biological threat is suspected or has already been confirmed. • Characteristics: Have limited power based on vehicle, have mid-range capabilities, and are mobile • Tier III • Tier III are small sensors deployed in areas known to be infected with biological outbreaks to track the spread of these agents closely. • Tier III are “ad hoc” meaning their position can not be pre-determined, but they do not move. • Delivery mechanisms might include dispersal from aircraft or the tops of surrounding buildings. • Characteristics: Small, low power, short range, can only sense things in their own footprint and pass the data to neighbor nodes.

3. Let’s give our sensors Icons • Tier I * • Tier II • Tier III * NOTE: For the purposes of this model, we could call Tier I “data collectors” because one type of data collected is a medical report, which would be entered by a doctor in a database rather that a sensor actually “sensing” something.

4. Also, Let’s give our sensors Range* Sensors can “sense” anything in their footprint and can pass data to one another. Note that “medical reports” could conceivably include any location in the city, and so I added a second footprint for Tier I (e.g. “The patient is infected and said he was downtown today”) * NOTE: These are notional ranges; we would have to do some work on what is realistic for sensors of these types and how long it would take for them to process biological data accurately. Likely, the larger the range, the longer the accurate sense time.

5. Now let’s consider Geography • Our Use Case is a smallpox outbreak is downtown Chicago, Police District 001.

6. Geography (cont.) • Our city has already some “data collector” sites, even without sensors. These would get phone calls or emergency information that an outbreak existed. H P

7. Geography (cont.) • Maybe we also have set up some Tier I sensors in key public places too. Biowatch uses 50 of these Tier I sensors per city today. H P

8. The Scenario • A terrorist releases smallpox pathogen infecting people in the nearby vicinity… H P

9. The Scenario • Let’s see it without the city overlay • So far, a small portion of the city’s residents have been exposed. Note there could be as many as 150,000 residents in Chicago, District 1. H P

10. The Scenario • Over time, more residents get infected as the biological agent disperses in the general direction of wind. • Also, people who don’t know they are sick yet give it to each other as they travel about the city (after the resultant incubation period). • In today’s cities, no one would know anything bad had happened until people started showing up sick at hospitals 3-4 days later. The BAM model equates this to 24% people dead or disabled and 99% of people in the city affected adversely by the outbreak in 40 days or less.

11. The Scenario • Our job is to prevent this from happening with a robust sensor grid. • Here are the Tier I sensors again. H P

12. The Scenario • Let’s add the local DHS operations center, where fusion needs to happen. H DHS P

13. The Scenario • Also, let’s add the pre-defined fixed communications paths. These are long haul point-to-point networks and provide the network “backbone” for our “small world network”. H DHS P

14. The Scenario • Let’s also add two Tier II mobile ad hoc sensors, one ambulance and one police cruiser. These follow random ground tracks that conform to the city’s roads. H DHS H P P

15. H A Biological Outbreak! • As soon as the first sensor collects a biological threat, it passes the data to the DHS for fusion. H DHS DHS H P P P

16. H P H The Operation • DHS issues an emergency evacuation of the area, and issues the deployment of Tier III sensors. Also, Tier II might also be alerted to begin response. H DHS DHS P H P P

17. H The Operation • Tier III ad hoc sensors are deployed randomly to track the outbreak and they also pass data back H DHS DHS P H P P

18. H The Operation • This data too is fused quickly and a containment “cordon” (or perimeter) around the outbreak area can be defined, for cleanup and citizen vaccination/treatment. This would have to be large enough for first responders to have time to respond. H DHS DHS P H P P

19. The Conclusion • This is presumably much faster than waiting for people to show up in hospitals. • But how much faster? And can the “cordon” be minimized?

20. That’s where Algorithms come in • Let’s look at some possible communications methods for our sensor network • Let’s say we have eight (8) ad hoc sensors, deployed randomly in the city. • Sensor 1 will notice the bio threat and no other sensors are in range (yet) • We will investigate the problem in our model in steps of time • Sensor 8 is closest to the DHS for the purposes of this explanation. • It is in our best interests to report even one instance of bio threat data to the DHS as quickly as possible before it spreads further. 1 3 2 7 5 6 4 8

21. Algorithms (cont.) • Epidemic-SI – A communications model between nodes that passes data immediately to all nodes in its footprint. • Step 1 • Sensor 1 senses a threat and passes data immediately to sensor 3 • Step 2 • Sensor 3 passes to 5 (and 1 again), both are in its footprint • Sensor 1 passes again to 3 • Step 3 • Sensor 5 passes to 3, 4 and 6 • Sensor 1 passes again to 3 • Sensor 3 passes again to 1 and 5 • Step 4 • Sensor 6 passes to 5, 7 and 8 • Sensor 4 passes again to 5 • Sensor 5 passes again to 3, 4 and 6 • Sensor 1 passes again to 3 • Sensor 3 passes again to 1 and 5 • Etc. • This algorithm is fast but bandwidth intensive - a lot of data is repeated, using up bandwidth and power • Note that sensor 2 did not get any data even though it was close to the original threat. 1 3 2 7 5 6 4 8 • Obviously, a lot of redundant retransmissions are occurring. This could be limited using Epidemic-SIS or Epidemic-SIR instead.

22. Algorithms (Cont.) • Gossip – A communications model between nodes where pairs of nodes randomly exchange data. • Step 1 • Sensor 1 senses a threat and exchanges data with sensor 3 • Step 2 • Sensor 3 exchanges data with 5 • Step 3 • Sensor 5 exchanges data with 4 • Meanwhile, sensor 3 exchanges with 1 again • Step 4 • Sensor 4 exchanges data with 5 again • Etc. • This algorithm is somewhat slower, and data may not reach the destination as quickly • However, it is not as bandwidth intensive and can be optimized to send data to notes that haven’t received data yet or using Gossip-Enforced-Ending to stop gossiping after a period of time. • Note: still sensor 2 did not get any data even though it was close to the original threat. 1 3 2 7 5 6 4 8 • Again, some redundancy here. This protocol is also somewhat slower than Epidemic.

23. Algorithms (Cont.) • Gossip-Enforced-Ending – a.k.a. Rumor-Mongering, Similar to gossip except nodes exchange data for a period of time before timing out. • Step 1 • Sensor 1 senses a threat and exchanges data with sensor 3 • Step 2 • Sensor 3 exchanges data with 5 • Sensor 1 exchanges data with 3 again; it’s a “hot rumor” • Step 3 • Sensor 5 exchanges data with 6 • Sensor 1 exchanges data with 3 again • Step 4 • Sensor 6 exchanges data with 7 • Sensor 5 exchanges data with 6 again • Sensor 1 stops sending data altogether because 3 has heard the “rumor” enough • Etc. • This algorithm is a variant of gossip and probably is best at conserving power during communications. It is likely to be slow, however. 1 3 2 7 5 6 4 8

24. Algorithms (Cont.) • Geocasting – Nodes send data to the furthest node in a specified geographic area • Step 1 • Sensor 1 senses a threat • Knowing that the data must go geographically SW (towards nodes 7 and 8), it sends data to the furthest one in its footprint, sensor 3 • Step 2 • Sensor 3 sends to the furthest one in that direction, 5 • Step 3 • Sensor 5 sends to 6 • Step 4 • Sensor 6 sends to 8 • Etc. • This presumes the sensors know where they are or which general direction they need to send data towards • Our BSF sensors all include onboard GPS/DGPS to determine their own locations in the city to within a meter. 1 3 2 7 5 6 4 8

25. Algorithms (Cont.) • At this point lets add some Tier 1 Backbone infrastructure… 1 3 2 7 5 6 4 8 9 10

26. Algorithms (Cont.) • Routing – Nodes use tables to see which paths are best. • Step 1 • Node 1 senses a threat, it only has one communications path, to node 3 • Step 2 • Sensor 3 routes the data to 5, realizing the data came from 1 • Step 3 • Sensor 5 routing tables suggest 6 is a communications bottleneck and it sends data instead to 4 • Step 4 • Sensor 4 is on the backbone and immediately sends data to 10 for long haul communication to 9 • Etc. • Note that Sensor 2 is also now on the backbone, and could immediately send data to node 10 if it sensed something 1 3 2 7 5 6 4 8 9 10

27. Algorithms • Some other algorithm modifications: • Rendezvous: Nodes pre-establish that data communications is possible. The source node sends a ‘ready to send’ signal and the destination replies with ‘ready to receive’ before data is passed. This handshake eliminates data loss, as algorithms like epidemic and gossip don’t check to make sure the data was received. • Anti-Entropy: Nodes auto-correct data arriving at themselves by comparing the data to other data already available at the arriving node or by comparing held data through data exchange with other nodes. This helps resolve differences in local data correlation. • Local Data Aggregation: Nodes add data passed to them to any data already on hand and send the fused result on to the next node. Every node learns what data is at every other node.

28. How BSF Communications Works Packet Burst Power used … (repeats) time Sense Time Window Communications Time Window A B A Single Packet: Source Node ID Dest Node ID Length Check sum Detect Latitude Detect Longitude Lets say we have two sensors, A and B, in range of each other (i.e. neighbors). Sensor A senses a biological threat. Sensor B does not, but has 5 packets of data from other sensors already in it’s buffer. 64 bits 64 bits 64 bits Time required to transmit exactly one packet (commsTxTime) Smallest time slot in window (minTimeStep) • Node A communicates its one packet to B: • Node A senses a threat during the sense time window using up power • There are 10/0.05 = 200 possible timeslots in the communications window • A needs to transmit one packet * 3 timeslots = 3 slots needed • To ensure B does not transmit over top of A, use 3*3-2 = 7 timeslots • If B transmits in any of these 7 slots, it could garble A’s transmission • Reliability that A’s transmission was received successfully at B = 1-(7/200) = 96.5% • If B doesn’t already know A’s data, it adds it to its own buffer, for 6 total. • Node B communicates its five packets to A: • Node B senses during the sense time window using up power but does not sense anything • There are 10/0.05 = 200 possible timeslots in the communications window • B needs to transmit five packets * 3 timeslots = 15 slots needed • To ensure A does not transmit over top of B, use 15*3-2 = 43 timeslots • If A transmits in any of these 43 slots, it could garble B’s transmission • Reliability that B’s transmission was received successfully at A = 1-(43/200) = 78.5% • If A doesn’t already know B’s data, it adds it to its own buffer, for 6 total. • Assume the following parameters: • senseTime = 30 secs • commsTimeWindow = 10 secs • minTimeStep = 50 mS • commsTxTime = 150 mS • One simulation time advance = 30 + 10 = 40 secs • Further, Assume: • The communications protocol is connectionless multicast; no handshake (similar to UDP) • There is no concept of packet acknowledgment, retransmission or timeout • Neither A nor B know how much data the other will send • A and B both pick a random set of contiguous timeslots and transmit their data in a burst during the comms window • Local data aggregation is performed at all nodes

29. Things that could be computed in Analysis • Elapsed Time/ Latency (Avg. & Max.) • How long did it take for all packets to be received at the DHS Ops Center? • % Delivery Rate • How many packets were received at DHS Ops Center out of all possible? • Hop Count (Avg./Max.) • How many nodes did the data have to go through? • Coverage (single sensor/ total of all sensors) • What percent of the city did sensors cover?

30. Summary • Our job will be to model communications flow using one or many of these algorithms • From - every sensor that senses something • To - the DHS Operations Center for fusion • Quickly - So that a “cordon” can be “drawn” around all sensors that sense something • Focus will be on the ad hoc (Tier III) and mobile ad hoc (Tier II), although backbone (Tier I) is necessary for “small world networks” communication (6 hops or less) • The finished end-to-end system should allow for modification of communication parameters to optimize speed of data delivery and fusion • The level of modeling described in this briefing will be complicated, but possible to implement