Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts

Aina Nadhirah Nor Hisham, Yin Hoe Ng (), Chee Keong Tan and David Chieng
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Aina Nadhirah Nor Hisham: Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia
Yin Hoe Ng: Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia
Chee Keong Tan: School of Information Technology, Monash University Malaysia, Subang Jaya 47500, Malaysia
David Chieng: Department of Electrical and Electronic Engineering, Faculty of Science and Engineering (FoSE), University of Nottingham Ningbo China, Ningbo 315100, China

Data, 2022, vol. 7, issue 11, 1-20

Abstract: Indoor positioning has garnered significant interest over the last decade due to the rapidly growing demand for location-based services. As a result, a multitude of techniques has been proposed to localize objects and devices in indoor environments. Wireless fingerprinting, which leverages machine learning, has emerged as one of the most popular positioning approaches due to its low implementation cost. The prevailing fingerprinting-based positioning mainly utilizes wireless fidelity (Wi-Fi) and Bluetooth low energy (BLE) signals. However, the RSS of Wi-Fi and BLE signals are very sensitive to the layout of the indoor environment. Thus, any change in the indoor layout could potentially lead to severe degradation in terms of localization performance. To foster the development of new positioning methods, several open-source location fingerprinting datasets have been made available to the research community. Unfortunately, none of these public datasets provides the received signal strength (RSS) measurements for indoor environments with different layouts. To fill this gap, this paper presents a new hybrid Wi-Fi and BLE fingerprinting dataset for multi-floor indoor environments with different layouts to facilitate the future development of new fingerprinting-based positioning systems that can provide adaptive positioning performance in dynamic indoor environments. Additionally, the effects of indoor layout change on the location fingerprint and localization performance are also investigated.

Keywords: indoor positioning; location fingerprint; received signal strength; Bluetooth low energy; wireless fidelity (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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