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On Data Persistence Models for Mobile Crowdsensing Applications

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 706))

Abstract

In this paper, we discuss various models and solutions for saving data in crowdsensing applications. A mobile crowdsensing is a relatively new sensing paradigm based on the power of the crowd with the sensing capabilities of mobile devices, such as smartphones, wearable devices, cars with mobile equipment, etc. This conception (paradigm) becomes quite popular due to huge penetration of mobile devices equipped with multiple sensors. The conception enables to collect local information from individuals (they could be human persons or things) surrounding environment with the help of sensing features of the mobile devices. In our paper, we provide a review of the data persistence solutions (back-end systems, data stores, etc.) for mobile crowdsensing applications. The main goal of our research is to propose a software architecture for mobile crowdsensing in Smart City services. The deployment for such kind of applications in Russia has got some limitations due to legal restrictions also discussed in our paper.

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Author information

Authors and Affiliations

  1. Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia

    Dmitry Namiot

  2. Ventspils International Radioastronomy Centre, Ventspils University College, Ventspils, Latvia

    Manfred Sneps-Sneppe

Authors
  1. Dmitry Namiot
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  2. Manfred Sneps-Sneppe
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Corresponding author

Correspondence to Dmitry Namiot .

Editor information

Editors and Affiliations

  1. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Leonid Kalinichenko

  2. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  3. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

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Namiot, D., Sneps-Sneppe, M. (2017). On Data Persistence Models for Mobile Crowdsensing Applications. In: Kalinichenko, L., Kuznetsov, S., Manolopoulos, Y. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2016. Communications in Computer and Information Science, vol 706. Springer, Cham. https://doi.org/10.1007/978-3-319-57135-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-57135-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57134-8

  • Online ISBN: 978-3-319-57135-5

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