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Dos and Don'ts in Mobile Phone Sensing Middleware: Learning from a Large-Scale Experiment

Abstract : Mobile phone sensing contributes to changing the way we approach science: massive amount of data is being contributed across places and time, and paves the way for advanced analyses of numerous phenomena at an unprecedented scale. Still, despite the extensive research work on enabling resource-efficient mobile phone sensing with a very-large crowd, key challenges remain. One challenge is facing the introduction of a new heterogeneity dimension in the traditional middleware research landscape. The middleware must deal with the heterogeneity of the contributing crowd in addition to the system's technical heterogeneities. In order to tackle these two heterogeneity dimensions together, we have been conducting a large-scale empirical study in cooperation with the city of Paris. Our experiment revolves around the public release of a mobile app for urban pollution monitoring that builds upon a dedicated mobile crowd-sensing middleware. In this paper, we report on the empirical analysis of the resulting mobile phone sensing efficiency from both technical and social perspectives, in face of a large and highly heterogeneous population of participants. We concentrate on the data originating from the 20 most popular phone models of our user base, which represent contributions from over 2,000 users with 23 million observations collected over 10 months. Following our analysis, we introduce a few recommendations to overcome-technical and crowd-heterogeneities in the implementation of mobile phone sensing applications and supporting middleware.
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Contributor : Valerie Issarny Connect in order to contact the contributor
Submitted on : Thursday, September 15, 2016 - 12:23:41 AM
Last modification on : Wednesday, November 30, 2022 - 11:22:07 AM
Long-term archiving on: : Friday, December 16, 2016 - 12:41:29 PM


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Valerie Issarny, Vivien Mallet, Kinh Nguyen, Pierre-Guillaume Raverdy, Fadwa Rebhi, et al.. Dos and Don'ts in Mobile Phone Sensing Middleware: Learning from a Large-Scale Experiment. ACM/IFIP/USENIX Middleware 2016, Dec 2016, Trento, Italy. ⟨10.1145/2988336.2988353⟩. ⟨hal-01366610⟩



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