Abstract
Mobile Crowd Sensing (MCS) is a new sensing paradigm exploiting the capabilities of smart devices (smartphones, wearables, etc.) to gather large volume of data. Gathering contextual information is a very expensive activity in terms of mobile device resource consumption, so limiting this consumption is essential for user satisfaction. The architectural style applied to the MCS platform largely affects the consumption of these resources. A server-centric MCS is more efficient when there are many entities interested on the gathered information, whilst a mobile-centric architecture has lower consumption when real-time information is required. In this paper, we propose a platform combining both architectural styles. This allows us to reduce the resource consumption of mobile devices, since it is easier to take advantage of the benefits of each style, and to better facilitate user aggregation, being able to group users both at the server and at the client-side depending on the freshness of the required information and the sensing task to be assigned. Finally, we have evaluated this platform for two different case studies, obtaining very promising results.
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Acknowledgements
This research was supported by the Sacher project (no. J32I16000120009) funded by the POR-FESR 2014-20 program through CIRI, by 4IE project (0045-4IE-4-P) funded by the POCTEP program, by the project TIN2015-69957-R (MINECO/FEDER), by the Department of Economy and Infrastructure of the Government of Extremadura (GR15098). The authors would also like to thank Leo Gioia for his help in capturing and processing the data during the realization of the case studies.
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Bellavista, P., Berrocal, J., Corradi, A., Foschini, L. (2017). Mobile Crowd Sensing as an Enabler for People as a Service Mobile Computing. In: Puliafito, A., Bruneo, D., Distefano, S., Longo, F. (eds) Ad-hoc, Mobile, and Wireless Networks. ADHOC-NOW 2017. Lecture Notes in Computer Science(), vol 10517. Springer, Cham. https://doi.org/10.1007/978-3-319-67910-5_12
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DOI: https://doi.org/10.1007/978-3-319-67910-5_12
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