Sensor placement for urban homeland security applications

We simulated a sensor network that detects and tracks the release of harmful airborne contaminants in an urban environment. The simulation determines sensor placement in that environment. The effort required integration of models from computational fluid dynamics (CFDs), combinatorial optimization, and population mobility dynamics. These CFD models, coupled with population mobility models, facilitate estimation of the effect of released contaminant on civilian populations. We studied the effects of a contaminant, chlorine gas, as a function of urban environment, prevailing winds, and likely attack locations. The models predictions optimized sensor node locations, providing mitigation of contaminant effects on human population. Results show that higher fidelity dispersal predictions increase sensor placement effectiveness. Incorporation of civilian evacuation models helps to minimize the overall impact of an attack when compared to a static population. Moreover, results show the benefits of using seasonal sensor configurations to maximize detection capabilities, taking into account prevailing seasonal wind conditions.