Smartphone system logs mall store visits and writes journals
AirLog: Store-Level Indoor Life Logging Made Easy
Networking and Internet ArchitectureArtificial Intelligence
Summary
People often want to remember where they shopped in big malls but manually tracking stores is annoying. The authors designed AirLog, which uses smartphones to automatically figure out which stores someone visited inside malls by reading Wi-Fi names and mall maps. It then uses smart reasoning to turn these visits into easy-to-read daily journals. This works on regular phones without extra hardware and helps with remembering and reflecting on shopping trips without any manual note-taking.
What this means in practice
- •For mobile app developers: Create apps that automatically track and summarize user mall visits into readable journals without installing extra hardware or complex infrastructure.$Commercial implications: Enables consumer-facing personal memory or diary apps that log indoor store visits just from smartphones.
- •For retail analytics teams: Gain precise store-level customer visit patterns in malls using customer phones and existing Wi-Fi cues without expensive localization setups.
Authors
Zihui Yun, Jiaying Du, Yue Yu, Zhewei Liu, Zhen Xiang, Longfei Shangguan, Zhenlin An
Abstract
This paper presents AirLog, a smartphone-based life journaling system that automatically reconstructs users' store visits in shopping malls and summarizes them into human-readable journals. Unlike conventional indoor localization systems, AirLog avoids labor-intensive radio-map construction and dedicated wireless localization infrastructure and algorithm calibrations. Instead, it repurposes two cues already available in commercial spaces: semantic information exposed by ambient Wi-Fi SSIDs and indoor directory images. AirLog converts directory images into spatial maps and fuses Wi-Fi semantic anchors with inertial dead reckoning to recover store-level trajectories, which are then summarized into journals by an LLM. Such store-level life logs can support applications such as personal memory recall, activity reflection, and automated diary generation without requiring users to manually record where they have been. We implement AirLog on commodity smartphones and evaluate it on both a large-scale public dataset and a self-collected dataset. The results demonstrate that AirLog substantially improves store-level region recovery, semantic matching, trajectory reconstruction, and journal quality over existing baselines. A human evaluation further shows that the generated journals are coherent and faithful to users' visits.