Sensor placement for grid coverage under imprecise detections

We present a resource-bounded optimization framework for sensor resource management under the constraints of sufficient grid coverage of the sensor field. We offer a unique "minimalistic" view of distributed sensor networks in which sensors transmit/report a minimum amount of sensed data. The proposed theory is aimed at optimizing the number of sensors and determine their placement to support such minimalistic sensor networks. We represent the sensor field as a grid (two- or three-dimensional) of points. The optimization framework is inherently probabilistic due to the uncertainty associated with sensor detections. The proposed algorithm addresses coverage optimization under constraints of imprecise detections and terrain properties. The issue of preferential coverage of grid points (based on relative measures of security and tactical importance) is also modeled. Experimental results for an example sensor field with obstacles demonstrate the application of our approach.