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Intersection of machine learning and mobile crowdsourcing: a systematic topic-driven review

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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.

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Data availability

The articles reviewed in this paper can be found in mainstream databases such as EI Compendex and Scopus by Elsevier. The list of reviewed articles and relevant analysis data can be made available by the authors upon request.

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Funding

This research was supported by the Fujian Provincial Natural Science Foundation of China (Grant No. 2022J05291). The second author received partial support from ACU to complete this research, under grant number CAWGS – 905737–111.

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Authors and Affiliations

  1. School of Software Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian, China

    Weisi Chen & Xu Zhang

  2. Peter Faber Business School, Australian Catholic University, North Sydney, NSW, 2060, Australia

    Walayat Hussain

  3. Higher Colleges of Technology, Abu Dhabi, United Arab Emirates

    Islam Al-Qudah

  4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Ghazi Al-Naymat

Authors
  1. Weisi Chen
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  2. Walayat Hussain
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  3. Islam Al-Qudah
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  4. Ghazi Al-Naymat
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  5. Xu Zhang
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Contributions

All authors contributed to this review paper. Weisi Chen had the idea for the article. The literature search was performed by Weisi Chen, Walayat Hussain, Islam Al-Qudah, and Ghazi Al-Naymat. Relevant data analysis was conducted by Weisi Chen, Walayat Hussain, Islam Al-Qudah, Ghazi Al-Naymat, and Xu Zhang. The first draft of the manuscript was written by Weisi Chen, Walayat Hussain, Islam Al-Qudah, and Ghazi Al-Naymat, and Weisi Chen and Walayat Hussain prepared all revised versions. All authors read and approved the final manuscript. Walayat Hussain, Islam Al-Qudah, and Ghazi Al-Naymat contributed equally to this work.

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Correspondence to Weisi Chen.

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Chen, W., Hussain, W., Al-Qudah, I. et al. Intersection of machine learning and mobile crowdsourcing: a systematic topic-driven review. Pers Ubiquit Comput 29, 77–101 (2025). https://doi.org/10.1007/s00779-024-01820-w

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