Determination of an optimal configuration (numbers, types, and locations) of a sensor network is an important practical
problem. In most applications, complex signal propagation effects and inhomogeneous coverage preferences lead to an
optimal solution that is highly irregular and nonintuitive. The general optimization problem can be strictly formulated as
a binary linear programming problem. Due to the combinatorial nature of this problem, however, its strict solution
requires significant computational resources (NP-complete class of complexity) and is unobtainable for large spatial
grids of candidate sensor locations. For this reason, a greedy algorithm for approximate solution was recently introduced
[S. N. Vecherin, D. K. Wilson, and C. L. Pettit, "Optimal sensor placement with terrain-based constraints and signal
propagation effects," Unattended Ground, Sea, and Air Sensor Technologies and Applications XI, SPIE Proc. Vol. 7333,
paper 73330S (2009)]. Here further extensions to the developed algorithm are presented to include such practical needs
and constraints as sensor availability, coverage by multiple sensors, and wireless communication of the sensor
information. Both communication and detection are considered in a probabilistic framework. Communication signal and
signature propagation effects are taken into account when calculating probabilities of communication and detection.
Comparison of approximate and strict solutions on reduced-size problems suggests that the approximate algorithm yields quick and good solutions, which thus justifies using that algorithm for full-size problems. Examples of three-dimensional outdoor sensor placement are provided using a terrain-based software analysis tool.