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Urban Mobility-Driven Crowdsensing: Recent Advances in Machine Learning Designs and Ubiquitous Applications

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Mobile Crowdsourcing

Part of the book series: Wireless Networks ((WN))

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Abstract

Driven by explosively growing urban big data, increasing network connectivity, and enhanced mobility of human and transportation modalities, Urban Mobility-driven CrowdSensing (UMCS) has been widely used for various urban and ubiquitous applications, including urban event monitoring, city planning, and smart transportation system. This chapter examines and analyzes the recent advances and application of UMCS learning algorithm design and emerging use cases. In particular, we first overview the recent advances of machine learning (ML) techniques and algorithms for crowdsensing signal reconstruction and mobility learning to understand the importance of signal learning and mobility characterization for UMCS. We then review the emerging applications for UMCS, including indoor crowd detection and urban mobility system reconfiguration. For each category, we identify the strengths and weaknesses of the related studies and summarize the key research insights and representative studies, which can serve as a guideline for new researchers and practitioners in this emerging and important research field.

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Authors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Suining He

  2. The University of Michigan, Ann Arbor, MI, USA

    Kang G. Shin

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  1. Suining He
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  2. Kang G. Shin
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Correspondence to Suining He .

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  1. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA

    Jie Wu

  2. Jilin University, Changchun, China

    En Wang

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He, S., Shin, K.G. (2023). Urban Mobility-Driven Crowdsensing: Recent Advances in Machine Learning Designs and Ubiquitous Applications. In: Wu, J., Wang, E. (eds) Mobile Crowdsourcing. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-031-32397-3_2

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