Evidence map›Paper›PMID 36559982›Full record

ArticleSensors (Basel, Switzerland)2022

An Improved Method Based on Bluetooth Low-Energy Fingerprinting for the Implementation of PEPS System.

Francesco Bonavolontà, Annalisa Liccardo, Rosario Schiano Lo Moriello, Enzo Caputo, Giorgio de Alteriis, Angelo Palladino, Giuseppe Vitolo

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Francesco BonavolontàDipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, 80125 Naples, Italy.ORCID 0000-0003-0666-0942
Annalisa LiccardoDipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, 80125 Naples, Italy.ORCID 0000-0003-1270-4948
Rosario Schiano Lo MorielloDipartimento di Ingegneria Industriale, Università degli Studi di Napoli Federico II, 80125 Naples, Italy.ORCID 0000-0003-4875-2845
Enzo CaputoDipartimento di Ingegneria Industriale, Università degli Studi di Napoli Federico II, 80125 Naples, Italy.ORCID 0000-0003-0434-1585
Giorgio de AlteriisDipartimento di Ingegneria Industriale, Università degli Studi di Napoli Federico II, 80125 Naples, Italy.ORCID 0000-0002-4460-6640
Angelo PalladinoKineton, Via Emanuele Gianturco 23, 80142 Naples, Italy.
Giuseppe VitoloKineton, Via Emanuele Gianturco 23, 80142 Naples, Italy.ORCID 0000-0002-9280-7164

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the automotive field, the introduction of keyless access systems is revolutionizing car entry techniques currently dominated by a physical key. In this context, this paper investigates the possible use of smartphones to create a PEPS (Passive Entry Passive Start) system using the BLE (Bluetooth Low-Energy) Fingerprinting technique that allows, along with a connection to a low-cost BLE micro-controllers network, determining the driver's position, either inside or outside the vehicle. Several issues have been taken into account to assure the reliability of the proposal; in particular, (i) spatial orientation of each microcontroller-based BLE node which ensures the best performance at 180° and 90° referred to as the BLE scanner and the advertiser, respectively; (ii) data filtering techniques based on Kalman Filter; and (iii) definition of new network topology, resulting from the merger of two standard network topologies. Particular attention has been paid to the selection of the appropriate measurement method capable of assuring the most reliable positioning results by means of the adoption of only six embedded BLE devices. This way, the global accuracy of the system reaches 98.5%, while minimum and maximum accuracy values relative to the individual zones equal, respectively, to 97.3% and 99.4% have been observed, thus confirming the capability of the proposed method of recognizing whether the driver is inside or outside the vehicle.

Indexed as

BLEKalman Filterk-Nearest NeighborPEPS

Identifiers

PMID36559982
PMCPMC9783498

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.