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1. Lima, Max Willian Soares. Efficient indoor localization using graphs.

Degree: 2019, Universidade Federal do Amazonas

The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this, the primary source of information is the signal strength of packets received by a set of routers. The fingerprint technique is one of the most used techniques for IPSs. By using supervised machine learning techniques, it trains a model with the received signal intensity information so it can be used to estimate the positions of the devices later in an online phase. Although the k-Nearest Neighbors (kNN) is one of the most widely used classification methods due to its accuracy, it has no scalability since a sample we need to classify must be compared to all other samples in the training database. In this work, we use a novel hierarchical navigable small world graph technique to build a search structure so the location of a sample can be efficiently found, allowing the IPSs to be used in large scale scenarios or run on devices with limited resources. To carry out our performance evaluation, we proposed a synthetic IPS dataset generator as well as implemented a complete real-world, high scale IPS testbed. We compared the performance of our graph-based solution with other known kNN variants, such as Kd-Tree and Ball-Tree. Our results clearly show the performance gains of the proposed solution at 96% when compared to the classic kNN and at least 77% when compared to the tree-based approaches.

The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this, the primary source of information is the signal strength of packets received by a set of routers. The fingerprint technique is one of the most used techniques for IPSs. By using supervised machine learning techniques, it trains a model with the received signal intensity information so it can be used to estimate the positions of the devices later in an online phase. Although the k-Nearest Neighbors (kNN) is one of the most widely used classification methods due to its accuracy, it has no scalability since a sample we need to classify must be compared to all other samples in the training database. In this work, we use a novel hierarchical navigable small world graph technique to build a search structure so the location of a sample can be efficiently found, allowing the IPSs to be used in large scale scenarios or run on devices with limited resources. To carry out our performance evaluation, we proposed a synthetic IPS dataset generator as well as implemented a complete real-world, high scale IPS testbed. We compared the performance of our graph-based solution with other known kNN variants, such as Kd-Tree and Ball-Tree. Our results clearly show the performance gains of the proposed solution at 96% when compared to the classic kNN and at least 77% when compared to the tree-based approaches.

CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Advisors/Committee Members: Moura, Edleno Silva de, 407487582-91, http://lattes.cnpq.br/4737852130924504, Oliveira, Horácio Antonio Braga Fernandes de, http://lattes.cnpq.br/9314744999783676, Balico, Leandro Nelinho, http://lattes.cnpq.br/7704628402527376, [email protected].

Subjects/Keywords: Sistemas de posicionamento indoor (localização sem fio); CIÊNCIAS EXATAS E DA TERRA: CIÊNCIA DA COMPUTAÇÃO; Small world graphs; Indoor positioning systems; Nearest neighborsc

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APA (6th Edition):

Lima, M. W. S. (2019). Efficient indoor localization using graphs. (Masters Thesis). Universidade Federal do Amazonas. Retrieved from https://tede.ufam.edu.br/handle/tede/7308

Chicago Manual of Style (16th Edition):

Lima, Max Willian Soares. “Efficient indoor localization using graphs.” 2019. Masters Thesis, Universidade Federal do Amazonas. Accessed March 07, 2021. https://tede.ufam.edu.br/handle/tede/7308.

MLA Handbook (7th Edition):

Lima, Max Willian Soares. “Efficient indoor localization using graphs.” 2019. Web. 07 Mar 2021.

Vancouver:

Lima MWS. Efficient indoor localization using graphs. [Internet] [Masters thesis]. Universidade Federal do Amazonas; 2019. [cited 2021 Mar 07]. Available from: https://tede.ufam.edu.br/handle/tede/7308.

Council of Science Editors:

Lima MWS. Efficient indoor localization using graphs. [Masters Thesis]. Universidade Federal do Amazonas; 2019. Available from: https://tede.ufam.edu.br/handle/tede/7308

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