Intersection of Machine Learning and Mobile Crowdsourcing : A Systematic Topic-driven Review

Journal article

Chen, Weisi, Hussain, Walayat, Al-Qudah, Islam, Al-Naymat, Ghazi and Zhang, Xu. (2024). Intersection of Machine Learning and Mobile Crowdsourcing : A Systematic Topic-driven Review. Personal and Ubiquitous Computing. pp. 1-30. https://doi.org/10.1007/s00779-024-01820-w
AuthorsChen, Weisi, Hussain, Walayat, Al-Qudah, Islam, Al-Naymat, Ghazi and Zhang, Xu
Abstract

During the past decade of the big data era, mobile crowdsourcing has emerged as a popular research area, leveraging the collective intelligence and engagement of a vast number of individuals using their mobile devices. Another actively evolving area is machine learning, which has recently been augmented by the mobile crowdsourcing approach, especially for data collection and labeling. However, what happens when these two prevailing concepts meet? What topics have been discussed in recent literature? This paper adopts a systematic methodology, leveraging Latent Dirichlet allocation topic modeling for topic discovery from recent publications, to provide a comprehensive and insightful review of the intersection of machine learning and mobile crowdsourcing. Moreover, the paper highlights the emerging federated learning technology that integrates elements from both concepts. Key research questions are answered by examining discovered topics. The paper thoroughly discusses state-of-the-art developments and trends in combining these two concepts and explains the role of one concept in the other. The paper also addresses remaining challenges and outlines a future research agenda, including the potential incorporation of large language models into mobile crowdsourcing systems.

KeywordsMobile crowdsourcing; Machine learning ; Federated learning ; Big data analytics ; Security ; Privacy
Year01 Jan 2024
JournalPersonal and Ubiquitous Computing
Journal citationpp. 1-30
PublisherSpringer UK
ISSN1617-4917
Digital Object Identifier (DOI)https://doi.org/10.1007/s00779-024-01820-w
Web address (URL)https://link.springer.com/article/10.1007/s00779-024-01820-w
Open accessPublished as non-open access
Research or scholarlyResearch
Page range1-30
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Output statusPublished
Publication dates
Print10 Jun 2024
Publication process dates
Accepted28 May 2024
Deposited25 Jul 2024
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© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024

Place of publicationUnited Kingdom
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