A Lateration Method based on Effective Combinatorial Beacon Selection for Bluetooth Low Energy Indoor Positioning

Pascacio, Pavel; Torres-Sospedra, Joaquín; Casteleyn, Sven

doi:10.1109/wimob52687.2021.9606419

Cited by 3 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be noted that the positioning accuracy of the lateration baseline was significantly degraded, compared to BLE-RSS approaches reported in the literature (2 m to 3 m error [27], [30]), due to the adverse test scenario conditions, namely poor quantity and inappropriate distribution of anchors, unstable signal strength and hardware heterogeneity.…”

Section: Results Of the Collaborative Modelmentioning

confidence: 84%

“…The input data to fit the model are the distances estimated in the 5 th step, the weights and the GT of the BLE anchors. The weight value for every BLE anchor is computed as the inverse of its distance square with respect to the device/user; We set the threshold value to −83 dBm and the pathloss attenuation factor to η = 2.1, as we previously used in [27] and which are in phase to values in literature [16]. Each device involved in the collaborative positioning model will have its own value for RSS 1m RX (BX) calculated from the data collected in the device registration phase (see Table II).…”

Section: B Stand-alone Phasementioning

confidence: 99%

See 1 more Smart Citation

A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning

Pascacio¹,

Torres-Sospedra²,

Casteleyn³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor environments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Nevertheless, such BLE-and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE-RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the standalone lateration method in terms of positioning accuracy.

show abstract

Section: Results Of the Collaborative Modelmentioning

confidence: 84%

Section: B Stand-alone Phasementioning

confidence: 99%

A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning

Pascacio¹,

Torres-Sospedra²,

Casteleyn³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The threshold and the attenuation factor (η) values are −83 dBm and 2.1 respectively, as we used before in [16] and which are aligned with values proposed in the literature [20]. The devices used in the collaborative positioning approach have their own RSS 1m RX (BX) value according to the data gathered during the device calibration phase (see Table I).…”

Section: B Second Phase: Non-collaborative Phasementioning

confidence: 82%

“…It is worth mentioning that lateration positioning accuracy was significantly worse than RSS in the literature [16], [23]. Our real-world test scenario conditions include hardware heterogeneity (smartphones), strong NLOS and poor/inappropriate distribution of the BLE anchors.…”

Section: Discussionmentioning

confidence: 99%

“…The results showed that the proposed method improves the traditional lateration by ≈ 60%. Pascacio et al [16] proposed a lateration method based on a combinatorial BLE anchor selection to enhance the positioning accuracy of lateration methods, with an error reduction of ≈ 14% with respect to the traditional lateration in an office scenario.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

L/F-CIPS: Collaborative Indoor Positioning for Smartphones With Lateration and Fingerprinting

Pascacio,

Torres-Sospedra,

Casteleyn

et al. 2023

IEEE Sensors J.

Self Cite

View full text Add to dashboard Cite

The demand for indoor location-based services and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help to alleviate these drawbacks. In this paper, we propose a smartphone-based collaborative architecture using neural networks and received signal strength, which exploits the built-in wireless communication technologies in smartphones and the collaboration between devices to improve traditional positioning systems without additional deployment. Experiments are carried out in two real-world scenarios, demonstrating that our proposed architecture enhances the position accuracy of traditional indoor positioning systems.

show abstract

A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning

Pascacio

Torres-Sospedra

Casteleyn

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

A Lateration Method based on Effective Combinatorial Beacon Selection for Bluetooth Low Energy Indoor Positioning

Cited by 3 publications

References 19 publications

A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning

A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning

L/F-CIPS: Collaborative Indoor Positioning for Smartphones With Lateration and Fingerprinting

A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning

Contact Info

Product

Resources

About