Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation

You Li, Yuan Zhang, Xin Hu, Zhouzheng Gao, Jia Hu, Long Chen, Zhe He, Ling Pei, Kejie Chen, Maosong Wang, Xiaoji Niu, Ruizhi Chen, John Thompson, Fadhel Ghannouchi, Nasser El-Sheimy

Research output: Contribution to journalArticlepeer-review

Abstract

Localization techniques are becoming key to add location context to the Internet-of-Things (IoT) data without human perception and intervention. Meanwhile, the newly emerged low-power wide-area network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources have limited localization performance by using such IoT signals. This article reviews the IoT localization system through the following sequence: IoT localization system review, localization data sources, localization algorithms, localization error sources and mitigation, and localization performance evaluation. Compared to the related surveys, this article has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.
Original languageEnglish
Pages (from-to)4035 - 4062
Number of pages28
JournalIEEE Internet of Things Journal
Volume8
Issue number6
Early online date2 Sept 2020
DOIs
Publication statusPublished - 15 Mar 2021

Keywords / Materials (for Non-textual outputs)

  • Smoothing methods
  • Internet of Things
  • Robustness
  • Navigation
  • Zirconium
  • Mathematical model
  • Estimation

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