Utilizando a infraestrutura wi-fi disponível para prover a localização de smartphones em ambientes fechados

• Autor(a) principal: Silva, Bolívar Menezes da
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Manancial - Repositório Digital da UFSM
• Texto Completo: http://repositorio.ufsm.br/handle/1/16606
• topic: Localização em ambientes fechados; Sistema de posicionamento interno baseado em redes sem fio; Sistema oportunista; Indoor positioning system; Wireless-based indoor positioning system; Opportunistic system; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

The popularization of mobile devices with increasingly sensors and embedded resources has boosted several types of research in the area of context-awareness computing. Among the most relevant contextual information is the location. In outdoor environments, GPS technology is already widespread. However, in general, people tend to spend most of their time indoors, such as university buildings, hospitals, shopping malls, supermarkets, airports or even in their homes. Due to interference caused by various obstacles, the accuracy of GPS location can often be compromised. In order to overcome the problem of location indoors, several approaches, mainly using radiofrequency technologies, have been proposed. So far, there is no widely accepted solution that solves the problem of location indoors. In this sense, the present work uses an opportunistic approach, which takes advantage of the Wi-Fi infrastructure available in the environment, to provide the location of mobile stations. Based on this objective, the WALDO architecture was developed, linking some characteristics of different approaches developed in recent years, taking into account the techniques that present the best results at each stage, together with a zone-based approach and rankings. This approach seeks to make RSS reads that have noises ignored during the localization phase. After performing tests in different scenarios, using the WALDO architecture, the presented results were satisfactory. Although this is an opportunistic approach, the tests present most of the estimates with an average error between 2 and 4 meters, depending on the dataset used. In the first set of data, corresponding to an area of 66 m2 (computer lab), 80.24% of the tests presented location estimates between 0 and 4 meters from the true location (zero being the correct position of smartphone at the time of the test). On the other hand, in the tests performed with the second set of data, in an area of 560 m2 (composed of some rooms), the results between 0 and 5 meters corresponded to 75.5% of the tests.