Convex Voronoi-inspired space partitioning for heterogeneous networks: a coverage-oriented approach

Convex Voronoi-inspired space partitioning for heterogeneous networks: a coverage-oriented approach

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This study addresses the problem of space-partitioning in heterogeneous sensor networks, where the nodes have uniform symmetric sensing patterns, although their maximum distance differs. Emphasis is given in the inappropriateness of classical spatial Voronoi tessellation for coverage purposes, compared to the proposed space-partitioning technique, which takes into account this heterogeneity. The latter's definition is reflected in a way that the assigned regions are convex sets (contrary to weighted Voronoi diagrams), their construction is computationally efficient, while special properties of Voronoi diagrams, which hold for homogeneous networks, are kept active. The proposed Voronoi definition degenerates into the classical one when the sensing radii of the nodes are equal. Examples are provided in order to emphasise in the efficacy of the proposed region-assignment scheme when dealing with heterogeneous networks, in contrast with classical Voronoi tessellation.


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