Enhanced Localization for Indoor Construction

Ibrahim, Magdy; Moselhi, Osama

doi:10.1016/j.proeng.2015.10.085

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…The first BLE (Bluetooth Low Energy) transmitters appeared in 2010 and used the Bluetooth 4.0 version. In the new standard, the transmission speed has been reduced from 40 Mb / s (in version 3.1) to 1 Mb / s, but thus the energy consumption has been minimized, and the range has been increased to 100m [10]. Thanks to this technology, it was possible to create, inter alia, beacons that were used in this work.…”

Section: Bluetooth Low Energymentioning

confidence: 99%

Optimizing the placement of BLE transmitters for Indoor Positioning Systems

Pilski,

Aleksandrowicz

2023

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The article presents the results of experimental research related to the optimization of the placement of BLE transmitters (beacons) inside rooms in order to locate the user. The experiments were carried out to check how far BLE transmitters can be moved away from each other so that the average localization error was at a level acceptable to the user. The value of the localization error was assumed based on the literature (less than 1 m). As a result of the conducted experiments, a mathematical formula was defined to calculate the required number of BLE transmitters for the exact location inside the rooms. The experiments used BLE transmitters, rooms inside the building of Siedlce University of Natural Sciences and Humanities and a mobile application with a database. In the study of the optimization of the placement of beacons, the fingerprint technique was used.

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Section: Bluetooth Low Energymentioning

confidence: 99%

Optimizing the placement of BLE transmitters for Indoor Positioning Systems

Pilski,

Aleksandrowicz

2023

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“…Several works use distance-based positioning with BLE beacons in various domestic or public environments. In [16], the authors use trilateration in a hall of 7 m × 5 m where the maximum distance from the beacons to the target is less than 9 m. Despite the small size of the environment, their experiments using three beacons produce an average error of 2.766 m. In [17], the authors optimize the trilateration algorithm applying a Kalman filter to reduce noise in the RSSI. Park et al [18] calculate the distance using the RSSI and then they refine the result applying two Kalman filters to reduce the error.…”

Section: Background and Related Workmentioning

confidence: 99%

“…As shown in the table, many works [4,5,9,12,17,19,[25][26][27][28][29] do not perform any feature extraction and work directly on raw RSSI data. Another common solution [10,11,14,18,21,23] is to use as feature the average of RSSI measures over a short interval of time (from a few seconds to some minutes) for noise rejection.…”

Section: Background and Related Workmentioning

confidence: 99%

A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

et al. 2019

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Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters.

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