A flexible framework for covering and partitioning problems in indoor spaces

Abstract

Utilizing indoor spaces has become important with the progress of localization and positioning technologies. Covering and partitioning problems play an important role in managing, indexing, and analyzing spatial data. In this paper, we propose a multi-stage framework for indoor space partitioning, each stage of which can be flexibly adjusted according to target applications. One of the main features of our framework is the parameterized constraint, which characterizes the properties and restrictions of unit geometries used for the covering and partitioning tasks formulated as the binary linear programs. It enables us to apply the proposed method to various problems by simply changing the constraint parameter. We present basic constraints that are widely used in many covering and partitioning problems regarding the indoor space applications along with several techniques that simplify the computation process. We apply it to particular applications, device placement and route planning problems, in order to give examples of the use of our framework in the perspective on how to design a constraint and how to use the resulting partitions. We also demonstrate the effectiveness with experimental results compared to baseline methods.