Bluetooth Beacon Based Accurate Indoor Positioning Using Machine Learning

Kotrotsios, Konstantinos; Orphanoudakis, Theofanis

doi:10.1109/seeda-cecnsm.2019.8908304

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…The position calculation is based on RSSI values [4]. The effectiveness of the indoor location system requires important things, including permanent and accurate identification of the location of the user, rapid identification of the site; and the ability to adapt to a changing and difficult environment [5]. The development of artificial intelligence algorithms has increased the handling of machine learning (ML) techniques.…”

Section: Introductionmentioning

confidence: 99%

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Maaloul¹,

Lejdel²,

Clementini

et al. 2023

Bulletin EEI

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The adoption of Bluetooth beacon technology demonstrates a broad interest in indoor positioning technology because of its low cost and ease of use. Bluetooth beacons usually have an accuracy of fewer than 4 meters. The use of machine learning (ML) leads to results with greater accuracy compared to using traditional filtering methods. In this paper, we provide indoor localization based on Bluetooth beacons using several different ML techniques. We used ML algorithms to locate customers' devices in shopping malls. The extra-trees classifier and k-neighbors classifier found the device with greater than 90% accuracy. Other algorithms were able to determine the location with less accuracy. The results also showed that Bluetooth technology is a valid solution to find the data used to analyze the spatial-temporal behavior of individuals.

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Section: Introductionmentioning

confidence: 99%

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Maaloul¹,

Lejdel²,

Clementini

et al. 2023

Bulletin EEI

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Accurate Gridless Indoor Localization Based on Multiple Bluetooth Beacons and Machine Learning

Kotrotsios

Orphanoudakis

2021

2021 7th International Conference on Automation, Robotics and Applications (ICARA)

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Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

Kotrotsios

Fanariotis

Leligou

et al. 2022

Sensors

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In this paper, we present the results of a performance evaluation and optimization process of an indoor positioning system (IPS) designed to operate on portable as well as miniaturized embedded systems. The proposed method uses the Received Signal Strength Indicator (RSSI) values from multiple Bluetooth Low-Energy (BLE) beacons scattered around interior spaces. The beacon signals were received from the user devices and processed through an RSSI filter and a group of machine learning (ML) models, in an arrangement of one model per detected node. Finally, a multilateration problem was solved using as an input the inferred distances from the advertising nodes and returning the final position approximation. In this work, we first presented the evaluation of different ML models for inferring the distance between the devices and the installed beacons by applying different optimization algorithms. Then, we presented model reduction methods to implement the optimized algorithm on the embedded system by appropriately adapting it to its constraint resources and compared the results, demonstrating the efficiency of the proposed method.

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Bluetooth Beacon Based Accurate Indoor Positioning Using Machine Learning

Cited by 3 publications

References 20 publications

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Accurate Gridless Indoor Localization Based on Multiple Bluetooth Beacons and Machine Learning

Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

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