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Session D6b: Smartphone-Based Localization

Performance Evaluation of the Network Location Provider in Android Devices
Joohan Chun, Christopher Kong, and Dennis M. Akos, University of Colorado Boulder
Location: Grand Ballroom ABC
Date/Time: Thursday, May. 1, 2:58 p.m.

The Network Location Provider (NLP) in Android devices plays a critical role in location services, especially in GNSS-challenged environments such as indoors or areas affected by RFI (Radio Frequency Interference). Leveraging Wi-Fi fingerprinting and cell tower localization methods, NLP operates continuously and supports the Fused Location Provider (FLP). However, its internal mechanisms remain largely undisclosed. In this study, we evaluate NLP performance and accuracy using Google Pixel 6 and Pixel 9 smartphones across various environments—indoor, rural, suburban, and urban—using a u-blox F9P receiver as ground truth. Our results reveal that Wi-Fi availability is the dominant factor in NLP accuracy, with errors exceeding 1 km without Wi-Fi, but reduced to sub-100 meters with W-Fi enabled. Interestingly, the newer Pixel 9 offered no clear advantage over Pixel 6, indicating that NLP accuracy is more influenced by server-side algorithms and database quality than hardware. We also explored how dynamic or spoofed inputs could affect NLP. A simulated moving Wi-Fi hotspot had no immediate impact, and we raise the hypothesis that spoofed GNSS tags could poison the underlying database. These findings offer insights into NLP’s mechanisms and contribute to ongoing efforts to enhance smartphone-based Positioning, Navigation, and Timing (PNT) technologies, particularly in environments where GNSS is unreliable.
Keywords— Android PNT, NLP, FLP, Wi-Fi fingerprinting, cell tower localization, GNSS RFI

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