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This paper provides a robust, scalable Bluetooth Low-Energy (BLE) basedindoor localization solution using commodity hardware. While WiFi-based indoorlocalization has been widely studied, BLE has emerged a key technology forcontact-tracing in the current pandemic. To accurately estimate distance usingBLE on commercial devices, systems today rely on Receiver Signal StrengthIndicator(RSSI) which suffers from sampling bias and multipath effects. Wepropose a new metric: Packet Reception Probability (PRP) that builds on acounter-intuitive idea that we can exploit packet loss to estimate distance. Welocalize using a Bayesian-PRP formulation that also incorporates an explicitmodel of the multipath. To make deployment easy, we do not require anyhardware, firmware, or driver-level changes to off-the-shelf devices, andrequire minimal training. PRP can achieve meter level accuracy with just 6devices with known locations and 12 training locations. We show that fusing PRPwith RSSI is beneficial at short distances < 2m. Beyond 2m, fusion is worsethan PRP, as RSSI becomes effectively de-correlated with distance. Robustlocation accuracy at all distances and ease of deployment with PRP can helpenable wide range indoor localization solutions using BLE.

Packet Reception Probability: Packets That You Can't Decode Can Help Keep You Safe