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Online RSSI selection strategy for indoor positioning in low-effort training scenarios

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Abstract

Indoor positioning has been extensively studied for at least the past twenty years. In the list of the most common solutions, those based on the Received Strength Signal Indicator (RSSI) have gained importance due to the simplicity of RSSI as well as the fact that it is available in several wireless sensor networks. In this work, we propose SeALS (Selection Strategy of Access Points with Least Squares Estimation), a new RSSI-based indoor positioning system using Bluetooth Low-Energy (BLE) access points, whose accuracy is improved by a new selection strategy of collected RSSI combined with the Ordinary Least Squares (OLS) estimation method. The main advantage of the proposed solution is the fact that it requires less time in the training phase allied with better system accuracy if compared to traditional methods. The proposed system is validated in a large-scale, real-world scenario, and the obtained results for the positioning error are reduced by up to 13% concerning the pure OLS method, and by up to 30% concerning the widely deployed K-Nearest Neighbors technique.

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Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES-PROEX)–Finance Code 001. This work was partially supported by Amazonas State Research Support Foundation-FAPEAM–through the POSGRAD 22-23 Project.

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  1. Braulio Pinto and Horacio Oliveira contributed equally to this work.

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  1. Institute of Computing, Federal University of Amazonas, Av. General Rodrigo Octavio Jordão Ramos, 1200, Manaus, Amazonas, 69067005, Brazil

    Braulio Pinto & Horacio Oliveira

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  1. Braulio Pinto
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  2. Horacio Oliveira
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Correspondence to Braulio Pinto.

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Pinto, B., Oliveira, H. Online RSSI selection strategy for indoor positioning in low-effort training scenarios. Computing (2024). https://doi.org/10.1007/s00607-024-01285-y

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