SmartLoc: Smart Wireless Indoor Localization Empowered by Machine Learning

Lin Li, Xiansheng Guo, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Recently, machine learning (ML) has been widely adopted for fingerprint-based indoor localization because of its potency in delineating relationships between received signal strength (RSS) information and labels accurately. Existing ML-based indoor localization systems are less robust because they only adopt the output with the highest probability. This affects the final location estimate, hence compromising accuracy due to the severity of RSS fluctuations. Since different ML algorithms (MLAs) yield different performances, it is therefore intuitive to fuse predictions from multiple MLAs to improve the positioning performance in the presence of signal fluctuation. In this article, we propose SmartLoc, a smart wireless indoor localization framework to enhance indoor localization. In the offline phase, multiple MLAs are trained by utilizing an offline database. We further apply probability alignment to guarantee the predicted probabilities of each MLA at the same confidence level. In the online phase, given a testing RSS sample of a user at an unknown location, we extract the labels with probabilities greater than a certain threshold from each MLA to construct the space of candidate labels (SCL). The size of SCL can be adaptively determined by using our proposed dynamic size determination algorithm. Based on the SCL, we propose a probabilistic model to intelligently estimate the user's location by evaluating the label credibility simultaneously. A high label credibility indicates that the frequently occurred label is more likely to be true. Experimental results in a real changing environment verify the superiority of SmartLoc, outperforming the best among comparative methods by 10.8% in 75th percentile accuracy.

Original languageEnglish (US)
Article number8820139
Pages (from-to)6883-6893
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Volume67
Issue number8
DOIs
StatePublished - Aug 2020

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Indoor localization
  • machine learning (ML)
  • received signal strength (RSS)
  • smart localization
  • wireless fingerprinting

Fingerprint Dive into the research topics of 'SmartLoc: Smart Wireless Indoor Localization Empowered by Machine Learning'. Together they form a unique fingerprint.

Cite this