Wi-Fi fine time measurement–Principles, applications, and future trends: A survey

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion Pub Date : 2025-02-03 DOI:10.1016/j.inffus.2025.102992

Yuan Wu , Mengyi He , Wei Li , Izzy Yi Jian , Yue Yu , Liang Chen , Ruizhi Chen

{"title":"Wi-Fi fine time measurement–Principles, applications, and future trends: A survey","authors":"Yuan Wu , Mengyi He , Wei Li , Izzy Yi Jian , Yue Yu , Liang Chen , Ruizhi Chen","doi":"10.1016/j.inffus.2025.102992","DOIUrl":null,"url":null,"abstract":"<div><div>IEEE 802.11–2016 proposed the Wi-Fi Fine Time Measurement (FTM) protocol, aiming at providing meter or sub-meter level ranging function between smart terminals and Wi-Fi access points (APs). Compared with other indoor positioning technologies for instance, Bluetooth, acoustic, visible light, Ultra-wideband, etc., Wi-Fi has been characterized by low cost, no deployment, and potentially high positioning precision, especially with the enhancement of FTM, which enables Wi-Fi to be a competitive technology for Internet of Things, indoor location-based services (iLBSs), smart city, and many other fields. In this article, we first present a comprehensive survey that focuses on the Wi-Fi FTM technology, which contains the working principle, measurement for positioning, and methods comparison. We highlight the current FTM-related localization methods especially learning and multi-source fusion-based approaches. Then, we review the real-world applications and existing commercial solutions, revealing the possible directions for the industrialization of Wi-Fi FTM localization. Finally, this paper analyzes existing open issues of Wi-Fi FTM positioning (e.g., capability, scalability, multipath, NLOS, device heterogeneity, and privacy) and discusses the potential development trends.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"118 ","pages":"Article 102992"},"PeriodicalIF":14.7000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Fusion","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S156625352500065X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

IEEE 802.11–2016 proposed the Wi-Fi Fine Time Measurement (FTM) protocol, aiming at providing meter or sub-meter level ranging function between smart terminals and Wi-Fi access points (APs). Compared with other indoor positioning technologies for instance, Bluetooth, acoustic, visible light, Ultra-wideband, etc., Wi-Fi has been characterized by low cost, no deployment, and potentially high positioning precision, especially with the enhancement of FTM, which enables Wi-Fi to be a competitive technology for Internet of Things, indoor location-based services (iLBSs), smart city, and many other fields. In this article, we first present a comprehensive survey that focuses on the Wi-Fi FTM technology, which contains the working principle, measurement for positioning, and methods comparison. We highlight the current FTM-related localization methods especially learning and multi-source fusion-based approaches. Then, we review the real-world applications and existing commercial solutions, revealing the possible directions for the industrialization of Wi-Fi FTM localization. Finally, this paper analyzes existing open issues of Wi-Fi FTM positioning (e.g., capability, scalability, multipath, NLOS, device heterogeneity, and privacy) and discusses the potential development trends.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Information Fusion 工程技术-计算机：理论方法

CiteScore

33.20

自引率

4.30%

发文量

161

审稿时长

7.9 months

期刊介绍： Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.