Communications Range Analysis Simulation Set Up –Single Biological Threat placed in Soldier Field –Communication range varied from meters –Sensor ranges fixed: Tier I – 500m Tier II – 250m Tier III – 100m –Other variables default: Response Times Sensitivity Sensitivity Error False Positive Rate/ Specificity Mix/Max Velocities Move Probability Communications parameters Simulation Execution –255 runs –5 runs per range value – seconds per run General Observations –Using low communications ranges, it typically takes a while for even the fast Epidemic-SI to spread. –Slow starting latency outliers tended to be that no sensors were in range at the start of the simulation. –False Positives did occur. –At high range, it was very easy to see outward spread of communications and very fast.

Typical Simulation Execution Run Bio Threat release in Soldier Field Data disseminates outwardly radially from ground zero Simulation conclusion when data is fused and containment Cordon is established High level of network disconnection

Range vs. Latency 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. Ranges 200m+ do not provide significant added benefit.

Range vs. Hop Count Conclusions: Hop Count decreases faster than Latency (on a power curve) as range increases. This is due to the high level of disconnection in the network. Hop Count variance decreases as range increases. Small World Communications begin at ranges of 300m+ More variance in minimum and maximum hops due to the arrival of buffered data via separate paths. No significant improvement for ranges 300m+ Hop Count never decreases to less than 1

Range vs. Neighbors Conclusions: As expected, neighbor quantity increases exponentially with range. Variance in neighbor quantity increases significantly as range increases. Minimum number of neighbors is still 0 even at 400m. The network is largely disconnected at reasonable ranges.

Range vs. Power Remaining Conclusions: At low range, remaining power has a wide variance. This is due mainly to sensing periods, which has a large impact on power. Low range yielded cases with still very good power conservation in the network. Ranges beyond 250m+ have little and even sometimes a detrimental effect on power conservation.

Range vs. Coverage Conclusions: As expected, communications range has no effect on sensor coverage. Sensor coverage is based solely on default sensor ranges. The coverage variance is due to the random deployment of sensors in each simulation run. Default city district coverage in the model is roughly 53%.

Analysis Conclusions Communications Range –Range has a definite impact on latency, hop count, neighbors and remaining power. –Ranges >250m provide little or no benefit for Biological Sensor Fusion –Ranges >90m may not be realistic for low power, physically small sensors Other Inferences –Hop Count and Latency are not precisely linearly related. Latency could occur while a mobile node is disconnected from the network –Remaining Power is still reasonably high for Epidemic-SI communications with a low distribution of bio threats in the city –A fused DHS Operations Center result is reasonable in under 15 minutes after biological agent release