4.6 Article

Identifying the BLE Advertising Channel for Reliable Distance Estimation on Smartphones

Journal

IEEE ACCESS
Volume 10, Issue -, Pages 9563-9575

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3140803

Keywords

Smart phones; Estimation; Channel estimation; Advertising; Wireless communication; Wireless sensor networks; Bluetooth; Bluetooth; bluetooth low energy; BLE; channel detection; contact tracing; neighbor discovery; distance estimation; smartphone; android

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This paper presents a solution for detecting the wireless channel of received packets in smartphones, aiming to improve the accuracy and stability of distance estimation.
Estimating the distance between two smartphones plays an important role in a host of applications. For this purpose, smartphones emit and scan for Bluetooth Low Energy (BLE) signals. When a device is detected, the distance is estimated by evaluating the received strength of these signals. The main insight that is exploited for distance estimation is that the attenuation of a signal increases with the distance along which it has traveled. However, besides distance, there are multiple additional factors that impact the attenuation and hence disturb the distance estimation procedure. Among them, frequency-selective hardware and signal propagation belong to the most significant ones. For example, a BLE device transmits packets on three different frequencies (channels), while the transmit power and the receiver sensitivity depend on the frequency. As a result, the received signal strength varies for each channel, even when the distance remains constant. However, the information on which wireless channel a packet has been received is not made available to a smartphone. Hence, this error cannot be compensated, e.g. by calibration. In this paper, we for the first time provide a solution to detect the wireless channel on which a packet has been received by a smartphone application. We experimentally evaluate our proposed technique on multiple different smartphone models. Our results help to make distance estimation on smartphones more robust and accurate.

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