Packet Delivery Ratio Fingerprinting: Toward Device-Invariant Passive Indoor Localization

Yaoxin Duan, Kam Yiu Lam, Victor C.S. Lee, Wendi Nie*, Hao Li, Joseph K Y Ng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

8 Citations (Scopus)


Passive indoor localization for mobile Wi-Fi devices, e.g., smartphones, has attracted increasing attention from research communities recently. Existing passive localization techniques leverage received signal strength (RSS) of packets transmitted by target Wi-Fi devices and do not require a dedicated software installed on the devices. However, RSS-based passive localization techniques: 1) are device dependent, which results in poor localization accuracy for a wide variety of mobile devices and 2) cannot perform real-time passive localization. In this article, we present a novel passive localization technique, namely, packet delivery ratio (PDR) fingerprinting, to address these problems. In PDR fingerprinting, the lowest-power and highest-modulation scheme (LPHMS) is proposed to generate device-invariant PDR, which replaces RSS to construct fingerprints, to achieve device-invariant localization accuracy. Moreover, instead of passively monitoring packets rarely sent by mobile devices, in PDR fingerprinting, access points (APs) actively transmit request-to-send (RTS) frames to trigger target devices to reply clear-to-send (CTS) frames to calculate PDR. The RTS/CTS mechanism enables PDR fingerprinting to perform real-time localization. We have conducted extensive experiments in a real-world testbed. The experimental results demonstrate that PDR fingerprinting presents a competitive localization accuracy compared to RSS-based passive fingerprinting methods but is device invariant.

Original languageEnglish
Article number8948034
Pages (from-to)2877-2889
Number of pages13
JournalIEEE Internet of Things Journal
Issue number4
Publication statusPublished - Apr 2020

Scopus Subject Areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

User-Defined Keywords

  • Device invariant
  • passive indoor localization
  • Wi-Fi fingerprinting


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