Machine-learning-based positioning: A survey and future directions

Ziwei Li, Ke Xu, Xiaoliang Wang, Haiyang Wang, Yi Zhao, Meng Shen

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

Abstract

Widespread use of mobile intelligent terminals has greatly boosted the application of location-based services over the past decade. However, it is known that traditional location- based services have certain limitations such as high input of manpower/material resources, unsatisfactory positioning accuracy, and complex system usage. To mitigate these issues, machinelearning- based location services are currently receiving a substantial amount of attention from both academia and industry. In this article, we provide a retrospective view of the research results, with a focus on machine-learning-based positioning. In particular, we describe the basic taxonomy of location-based services and summarize the major issues associated with the design of the related systems. Moreover, we outline the key challenges as well as the open issues in this field. These observations then shed light on the possible avenues for future directions.

Original languageEnglish (US)
Article number8726079
Pages (from-to)96-101
Number of pages6
JournalIEEE Network
Volume33
Issue number3
DOIs
StatePublished - May 1 2019

Bibliographical note

Funding Information:
AcknowLedgment The work in this article was in part supported by the National Key R&D Program of China under Grant No. 2018YFB0803405, China National Funds for Distinguished Young Scientists under Grant No. 61825204, the Beijing Outstanding Young Scientist Project, the National Natural Science Foundation of China under Grant 61602039, and the Beijing Natural Science Foundation under Grant 4192050. Ke Xu is the corresponding author of this article.

Publisher Copyright:
© 1986-2012 IEEE.

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