Abstract
A risk-based sensor placement methodology is proposed to solve the
problem of optimal location of sensors or detectors to protect
population against the exposure to and effects of known and/or
postulated chemical, biological, and/or radiological threats. Risk is
calculated as a quantitative value representing population at risk
from exposure against standard exposure levels.
Historical meteorological data are used to characterize weather
conditions as the frequency of wind speed and direction pairs.
The meteorological data drive atmospheric transport and dispersion
modeling of the threats, the results of which are used to calculate
risk values. Sensor locations are determined via an iterative
dynamic programming algorithm whereby threats captured or detected
by sensors placed in prior stages are removed from consideration in
subsequent stages.
In addition to the risk-based placement algorithm, the proposed
methodology provides a quantification of the marginal utility of each
additional sensor or detector. Thus, the criterion for halting the
iterative process can be the number of detectors available, a threshold
marginal utility value, or the cumulative detection of a minimum factor
of the total risk value represented by all threats. The methodology
quantifies the effect of threat reduction measures, such as reduced
probability of one or more threats due to administrative and/or
engineering controls.