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Intersection of Machine Learning and Mobile Crowdsourcing : A Systematic Topic-driven Review
Chen, Weisi Hussain, Walayat Al-Qudah, Islam Al-Naymat, Ghazi Zhang, Xu
Chen, Weisi
Hussain, Walayat
Al-Qudah, Islam
Al-Naymat, Ghazi
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.
Keywords
Mobile crowdsourcing, Machine learning, Federated learning, Big data analytics, Security, Privacy
Date
2024
Type
Journal article
Journal
Book
Volume
Issue
Page Range
1-30
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled
Notes
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024