Abstract
Nowadays, it is clear that location systems are increasingly present in people’s lives. In general people often spend 80–90 % of their time in indoor environments, which include shopping malls, libraries, airports, universities, schools, offices, factories, hospitals, among others. In these environments, GPS does not work properly, causing inaccurate positioning. Currently, when performing the location of people or objects in indoor environments, no single technology can reproduce the same results achieved by the GPS for outdoor environments. One of the main reasons for this is the high complexity of indoor environments where, unlike outdoor spaces, there is a series of obstacles such as walls, equipment and even people. Thus, it is necessary that the solutions proposed to solve the problem of location in indoor environments take into account the complexity of these environments. In this paper, we propose an adaptable platform for indoor location, which allows the use and combination of different technologies, techniques and methods in this context.
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Acknowledgment
This work was partially supported by the National Institute of Science and Technology for Software Engineering (INES) funded by CNPq under grant 573964/2008–4.
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Melo, M., Aquino, G., Morais, I. (2016). Indoor Location: An Adaptable Platform. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9787. Springer, Cham. https://doi.org/10.1007/978-3-319-42108-7_15
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DOI: https://doi.org/10.1007/978-3-319-42108-7_15
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