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

Pavel Pascacio, Joaquín Torres-Sospedra, Sven Casteleyn, Elena Simona Lohan

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

3 Lataukset (Pure)


In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, 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. Never-theless, 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 stand-alone lateration method in terms of positioning accuracy.

Otsikko2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings
ISBN (elektroninen)9781728186719
ISBN (painettu)9781665495264
DOI - pysyväislinkit
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Joint Conference on Neural Networks - Padua, Italia
Kesto: 18 heinäk. 202223 heinäk. 2022


NimiProceedings of the International Joint Conference on Neural Networks
ISSN (painettu)2161-4393
ISSN (elektroninen)2161-4407


ConferenceInternational Joint Conference on Neural Networks


  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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