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