Cramér–Rao Lower Bound Analysis of Differential Signal Strength Fingerprinting for Crowdsourced IoT Localization

Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced indoor positioning system (IPS) has to handle diverse devices and the inherent heterogeneity in received signal strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting methods have been explored, which mitigate the device characteristics that cause RSS from different commercial devices to report differently. In this article, we focus on mean differential fingerprinting (MDF) that produces the differential fingerprints by subtracting the mean RSS value of all access points from the original RSS fingerprints. We study the localization performance of the MDF method by means of the Cramér–Rao lower bound (CRLB) and show analytically that it outperforms another method that addresses device diversity. Furthermore, we evaluate the localization accuracy of existing solutions using real-life Wi-Fi RSS data sets collected by multiple consumer devices. The experimental results confirm our analytical findings and demonstrate the effectiveness of the MDF method to mitigate device diversity, as well as other factors that affect the RSS readings, including the device carrying mode and power control schemes of the Wi-Fi infrastructure, thus contributing to the wider adoption of crowdsourced IPS.