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Existing solutions allow individual sensor nodes to determine the sensing direction for maximum target coverage which produces sensing coverage redundancy and much overhead.
We prove that this CH-based solution yields a better result for the problem of maximum target coverage with minimum number of sensing nodes.
Maximum target coverage with minimum number of sensor nodes, known as an MCMS problem, is an important problem in directional sensor networks (DSNs).
In the distributed greedy algorithm (DGA) [14], the authors addressed the problem of maximum target coverage with minimum number of sensors (MCMS), which is an NP-complete problem.
The major contributions of this paper are summarized as follows: The novelty of this work lies in designing a CH-based solution model for the problem of maximum target coverage using minimum number of sensor nodes in directional sensor networks.
The novelty of this work lies in designing a CH-based solution model for the problem of maximum target coverage using minimum number of sensor nodes in directional sensor networks.
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For the PTVs, the maximum dose, minimum dose, mean dose, target coverage (TC), conformity index (CI), and homogeneity index (HI) were compared.
This is because all maximum target thresholds are set below 100% population coverage and this target is applied after exception reported patients have been excluded.
A two-way sensitivity analysis considered different estimates of two parameters: the coverage rate and the maximum target age of the routine childhood immunisation programme.
With 94%, VC of 1st MMR was relatively high, but the 2-dose MMR coverage of 69% at maximum indicates that this young age-group is still far below the 2-dose target coverage of ≥95% required for the regional elimination of measles and rubella by 2015.
Planned dose can be optimized to uniformly irradiate a maximum intensity projection of the target volume to a lower dose, which would ensure a minimum level of target coverage that includes microscopic extension of disease.
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