A Multiobjective Optimization Tool Chain for 3-D Indoor Beacon Placement Problem

Over the past few decades, finding an optimal spatial configuration of localizing sensors has been mainly approached as a single-objective optimization (SOO) problem with a 2-D perspective for indoor designs. This article presents a novel multiobjective optimization (MOO) approach that analyzes the beacon placement problem (BPP) for the three-dimensional coordinate point cloud representation of indoor environments. The present research targets wireless localization scenarios with static obstacles and noisy range measurements. The proposed methodology is an optimization tool chain that explores a set of Pareto-optimal beacon configurations using the nondominated sorting genetic algorithm (NSGA)-II. The derived nondominant solutions are analyzed by simulations for their performance for accuracy and coverage over different indoor designs. Finally, a performance-based ranking system is presented to direct users in achieving a single optimal solution.