An extensible model for the deployment of non-isotropic sensors

Existing approaches for determining the optimal deployment positions of sensors suffer from a number of critical drawbacks. First, homogeneous deployment models have been commonly assumed, but in practice deployments of heterogenous sensors are typical. Second, existing approaches assume isotropic sensing ranges but it has been found that hardware and environmental conditions cause imperfections in sensing. Third, existing models are very application-dependent. We propose an extensible modeling framework for the problem of determining optimal deployment positions for a set of heterogeneous, non- isotropic sensors to cover a set of points in an area. The problem is formulated using a genetic algorithm where the objective is to minimize the cost to cover all points. Our technique is to decouple the coverage determination method from the sensor deployment model. This allows the sensor deployment model to remain consistent and address the critical drawbacks of previous models. A homeland security application is presented to illustrate the capabilities of our approach.