Barrier resilience

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Barrier resilience. This instance has resilience = 1 (it is possible to connect the green regions by a path that crosses only one barrier, the blue one) but the barrier must be crossed twice by the path.

Barrier resilience is an algorithmic optimization problem in computational geometry motivated by the design of wireless sensor networks, in which one seeks a path through a collection of barriers (often modeled as unit disks) that passes through as few barriers as possible.

The barrier resilience problem was introduced by Kumar, Lai & Arora (2005) (using different terminology) to model the ability of wireless sensor networks to detect intruders robustly when some sensors may become faulty. In this problem, the region under surveillance from each sensor is modeled as a unit disk in the Euclidean plane. An intruder can reach a target region of the plane without detection, if there exists a path in the plane connecting a given start region to the target region without crossing any of the sensor disks. The barrier resilience of a sensor network is defined to be the minimum, over all paths from the start region to the target region, of the number of sensor disks intersected by the path. The barrier resilience problem is the problem of computing this number by finding an optimal path through the barriers.[1]

A simplification of the problem, which encapsulates most of its essential features, makes the target region be the origin of the plane, and the start region be the set of points outside the convex hull of the sensor disks. In this version of the problem, the goal is to connect the origin to points arbitrarily far from the origin by a path through as few sensor disks as possible.

Another variation of the problem counts the number of times a path crosses the boundary of a sensor disk. If a path crosses the same disk multiple times, each crossing counts towards the total. The barrier thickness of a sensor network is the minimum number of crossings of a path from the start region to the target region.[1]

Computational complexity

Notes

References

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