Less is More: Improving WiFi Localization Accuracy with Fewer Scans

WiFi-based indoor localization provides a practical alternative to vision and sensor-based tracking, but its adoption is limited by high energy costs from repeated signal scans. To address this, we propose LowFI, a layout-aware, low-energy WiFi localization framework that jointly optimizes the number of scans and access point (AP) selection. LowFI leverages fading in RF channels to adaptively budget scans and rank APs, reducing redundancy while maintaining accuracy. Experiments with various WiFi-enabled devices show that localization error in nLoS conditions decreases from ≈8 m with a single scan to ≈2 m with much limited number of scans, after which accuracy gains saturate. By selecting minimal scans and a modest set of APs, LowFI achieves sub-meter accuracy while cutting energy cost by up to 65%. These results demonstrate that careful scan and AP orchestration can deliver reliable localization without compromising device battery.