A Tale of Three Location Trackers: AirTag, SmartTag, and Tile

TL;DR

The paper investigates AirTag, SmartTag, and Tile to quantify real-world location-tracking performance beyond marketing claims. Using improved crawlers, high-resolution ground truth, and expansive in-the-wild data (29 countries, 22 volunteers), it shows that companion-device density largely drives update rate and accuracy, with AirTag benefiting from firmware updates and cross-platform Tile lagging due to adoption. Spatial errors follow a log-normal distribution, while individual updates approximate a Rayleigh margin of error for some tags, indicating the need for probabilistic location handling. The findings stress the practical importance of ecosystem density for crowd-sourced tracking systems and suggest design and policy considerations to improve reliability and privacy in item tracking.

Abstract

Bluetooth Low Energy (BLE) location trackers, or "tags", are popular consumer devices for monitoring personal items. These tags rely on their respective network of companion devices that are capable of detecting their BLE signals and relay location information back to the owner. While manufacturers claim that such crowd-sourced approach yields accurate location tracking, the tags' real-world performance characteristics remain insufficiently understood. To this end, this study presents a comprehensive analysis of three major players in the market: Apple's AirTag, Samsung's SmartTag, and Tile. Our methodology combines controlled experiments -- with a known large distribution of location-reporting devices -- as well as in-the-wild experiments -- with no control on the number and kind of reporting devices encountered, thus emulating real-life use-cases. Leveraging data collection techniques improved from prior research, we recruit 22 volunteers traveling across 29 countries, examining the tags' performance under various environments and conditions. Our findings highlight crucial updates in device behavior since previous studies, with AirTag showing marked improvements in location report frequency. Companion device density emerged as the primary determinant of tag performance, overshadowing technological differences between products. Additionally, we find that post-COVID-19 mobility trends could have contributed to enhanced performance for AirTag and SmartTag. Tile, despite its cross-platform compatibility, exhibited notably lower accuracy, particularly in Asia and Africa, due to limited global adoption. Statistical modeling of spatial errors -- measured as the distance between reported and actual tag locations -- shows log-normal distributions across all tags, highlighting the need for improved location estimation methods to reduce occasional significant inaccuracies.

Figures (16)

• Figure 1: On the left, two data collection servers (MacOS and Ubuntu) run the FindMy, SmartThings, and pytile crawlers. On the right, a sample 3D mockup of our vantage point, a Galaxy A34 equipped with three tags.
• Figure 2: 3D-printed cases for the Redmi Go (left) and Samsung Galaxy A34 (right), used as vantage points.
• Figure 3: On the left, an Android GPS location reported at a specific timestamp, showing latitude, longitude, and $\sigma$ at 68% confidence interval. On the right, precision of reported location coordinates, measured in decimal degrees, collected by our crawlers for different tag types.
• Figure 4: Distribution of mobility levels between vantage points in the original vs. extended campaign.
• Figure 5: RSSI strength for each tag type at varying distances. The red triangles mark the statistical average.