Placement optimization of Positioning Nodes: Maximizing the distinction of Indoor Zones

Abstract

The performance of an Indoor Positioning System is highly related to the placement of the transmitting nodes that are used as references for the positioning estimations. Within this graduation project, we propose a methodology that can be used to optimize such a deployment and thus, increase the performance of an Indoor Positioning System that a) is based on Received Signal Strength Fingerprinting and b) is orientated towards providing location or zone estimations instead of exact positioning. The optimization process involves 4 fundamental components. Firstly, the modeling of the obstructions in the indoor environment and also the zone modeling. Then, the definition of the performance metric that can be used to evaluate each different deployment scenario, in which case, our proposed metric considers the separation area and distances between the zones in the RSS vector space. The third component is the radio propagation model, required for simulating the transmitted signals from each node, where a model based on the ray tracing technique is selected. Finally, the last component is the selection of the optimization function that will control and drive the whole optimization process by choosing which deployment schemes to evaluate. For that, the utilization of a Genetic Algorithm has been selected. The evaluation of our methodology showed that the most problematic regions in terms of localization accuracy are, as expected, those where different zones become adjacent. Yet, comparisons between regular node deployments and our optimized solutions indicated that, regardless the number of nodes, our optimization introduced in each case an overall localization improvement that was especially concentrated at the most problematic regions.