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New RSSI-fingerprinting-based smartphone localization system for indoor environments

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Abstract

Indoor device-free localization systems utilizing received signal strength indicator (RSSI) have become mainstream because of their low pricing, low complexity, low effort, low energy, and the framework can be reusable. However, it is reviewed from the existing literature that for paying extra attention to positioning errors mitigation, researchers fail to meet the optimum characteristics requirement for measurements. To overcome such limitations and focus on current challenges in measurements, this article proposes an RSSI fingerprinting positioning algorithm using the K-nearest neighborhood and regression analysis technique such that the localization precisions can be improved. Meanwhile, to get higher location accuracy, some special RSSI values are compared to obtain a suitable one. In the proposed positioning scheme, the indoor site is divided into two parts. Moreover, in each part, the positions of a smartphone at different reference points are localized with at most two access points (APs). This strategy not only reduces the neighboring noises significantly caused by the surrounding wireless signals coming from other electronic devices but also reduces the system management overhead due to the use of a minimum number of APs, which increases the system’s ability. The performance shows that the proposed scheme is highly capable of increasing the users’ experience and could be a research direction for future positioning systems.

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Acknowledgements

The first author would like to acknowledge the Council of Scientific and Industrial Research (CSIR) for providing the necessary support to carry out this work.

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Authors and Affiliations

  1. Department of Mathematics, Jadavpur University, Raja Subodh Chandra Mallick Road, Kolkata, West Bengal, 700032, India

    Debajyoti Biswas, Suvankar Barai & Buddhadeb Sau

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  1. Debajyoti Biswas
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  2. Suvankar Barai
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  3. Buddhadeb Sau
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Correspondence to Debajyoti Biswas.

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Biswas, D., Barai, S. & Sau, B. New RSSI-fingerprinting-based smartphone localization system for indoor environments. Wireless Netw 29, 1281–1297 (2023). https://doi.org/10.1007/s11276-022-03188-2

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