IndoLoR -- Uma Plataforma Adaptável para Localização em Ambientes Internos

Abstract

Resumo: Location systems have become increasingly part of people's lives. For outdoor environments, GPS appears as standard technology, widely disseminated and used. However, people usually spend most of their daily time in indoor environments, such as: hospitals, universities, factories, buildings, etc. In these environments, GPS does not work properly causing an inaccurate positioning. Currently, to perform the location of people or objects in indoor environments no single technology could reproduce for indoors the same result achieved by GPS for outdoors environments. Due to this, it is necessary to consider use of information from multiple sources using different technologies. Thus, this work aims to build an Adaptable Platform for Indoor location. Based on this goal, the IndoLoR platform is proposed. This platform aims to allow information reception from different sources, data processing, data fusion, data storage and data retrieval for the indoor location context. The proposed platform is evaluated through a case study examining aspects related to easiness of adaptation and performance.