Bluetooth Low Energy Dataset Using Separate-Channel Fingerprinting Techniques and Frequency Scanned Antennas.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-02-12 DOI:10.1038/s41597-025-04581-0

José Antonio López-Pastor, Alejandro Gil-Martinez, Antonio Hernández-Mateos, Astrid Algaba-Brazález, José Luis Gómez Tornero

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引用次数: 0

Abstract

Location systems based on Bluetooth Low Energy (BLE) fingerprinting using RSSI (Received Signal Strength Indicator) have been widely used for the implementation of indoor real-time location systems (IRTLS). Numerous databases have BLE RSSI information collected in multiple scenarios with measurements at various time intervals. However, all these databases collect the RSSI of the three advertising channels of the BLE protocol without considering the channel over which they are transmitted, which is known as Unified Channel Fingerprinting (UCFP). This paper describes and makes available to the scientific community for the first time a dataset using Separate Channel Fingerprinting (SCFP) and Frequency Scanned Leaky Wave Antennas (FSLWA). The dataset is composed of calibration and test data collected by two different sub-systems: one using four monopole antennas and another one using two FSLWAs. Both systems employ four BLE dongles and cover an indoor area of 35m². The data is sequentially collected over a 94-day duration including obstacles in the environment to test the robustness of SCFP with FSLWA against traditional UCFP.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.