Beyond RSS: A PRR and SNR Aided Localization System for Transceiver-free Target in Sparse Wireless Networks

Dian ZHANG, Wen XIE, Zexiong LIAO, Wenzhan ZHU, Landu JIANG*, Yongpan ZOU*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Nowadays transceiver-free (also referred to as device-free) localization using Received Signal Strength (RSS) is a hot topic for researchers due to its widespread applicability. However, RSS is easily affected by the indoor environment, resulting in a dense deployment of reference nodes. Some hybrid systems have already been proposed to help RSS localization, but most of them require additional hardware support. In order to solve this problem, in this paper, we propose two algorithms, which leverage the Packet Received Rate (PRR) to help RSS localization without additional hardware support. Moreover, we take the environment noise information into consideration by utilizing the Signal-to-Noise Ratio (SNR) which is based on the RSS and Noise Floor (NF) information instead of pure RSS. Thus, we can alleviate the noise effect in the environment and make our system more sensitive to the target. Specifically, when reference nodes are sparsely deployed and RSS is very weak, PRR and SNR can help in performing localization more accurately. Our BEYOND RSS system is based on sparse wireless sensor networks, wherein the experimental results show that the average localization error of our approach outperforms the pure RSS based approach by about 15.19%.
Original languageEnglish
Number of pages16
JournalIEEE Transactions on Mobile Computing
DOIs
Publication statusE-pub ahead of print - 3 Mar 2021

Bibliographical note

Natural Science Funds of Guangdong; 10.13039/501100001809; National Natural Science Foundation of China; Shenzhen Peacock Talent.

Keywords

  • NF
  • None-Line-of-Sight
  • PRR
  • RSS
  • SNR
  • Transceiver-free localization

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