= h(j)$. The task of finding hyperplanar grouping function in 2D from given UDG, and hyperplanes is called *hyperplanar substructures recognition*. In case where hyperplanar substructures are realized, *i.e.* the mapping $h$ is given, we are able to perform a lower dimensional localization for each hyperplanar group separately, and then localize the groups globally with respect to each other.

In this paper, we show that discovering the mapping function $h$ is an -hard problem, even if the hyperplanes are given as a part of the input by their equations. Then, we describe an algorithm to perform an efficient localization in case where $h$ is given as input. Afterwards, we describe an algorithm to utilize the grouping function, when available, and show that our algorithm is able to localize a network which cannot be localized by using only pairwise distances.

Motivation
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The physical world we live in is a 3D environment. Therefore, many applications require wireless noeds to be localized in 3D. Most of the scenarios in real-world WSN applications that need localization have usually deployments where the sensor nodes sit on planar regions to form sets of planar clusters. Examples of these surfaces include structures seen in both indoor 