Optimal Worst-Case Coverage of Directional Field-of-View Sensor Networks

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Abstract
Sensor coverage is a fundamental sensor networking design and use issue that in general tries to answer the questions about the quality of sensing (surveillance) that a particular sensor network provides. Although isotropic sensor models and coverage formulations have been studied and analyzed in great depth recently, the obtained results do not easily extend to, and address the coverage of directional and field-of-view sensors such as imagers and video cameras. In this paper, we present an optimal polynomial time algorithm for computing the worst-case breach coverage in sensor networks that are comprised of directional "field-of-view" (FOV) sensors. Given a region covered by video cameras, a direct application of the presented algorithm is to compute "breach", which is defined as the maximal distance that any hostile target can maintain from the sensors while traversing through the region. Breach translates to "worst-case coverage" by assuming that in general, targets are more likely to be detected and observed when they are closer to the sensors (while in the field of view). The approach is amenable to the inclusion of any sensor detection model that is either independent of, or inversely proportional to distance from the targets. Although for the sake of discussion we mainly focus on square fields and model the sensor FOV as an isosceles triangle, we also discuss how the algorithm can trivially be extended to deal with arbitrary polygonal field boundaries and sensor FOVs, even in the presence of rigid obstacles. We also present several simulation-based studies of the scaling issues in such coverage problems and analyze the statistical properties of breach and its sensitivity to node density, locations, and orientations. A simple grid-based approximation approach is also analyzed for comparison and validation of the implementation

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