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.
Type de document :
Communication dans un congrès
ACM/IFIP/USENIX Middleware 2016, Dec 2016, Trento, Italy. Proceedings of the ACM/IFIP/USENIX Middleware'2016 Conference, 2016, Proceedings of the 2016 International Middleware Conference. 〈http://2016.middleware-conference.org/〉. 〈10.1145/2988336.2988353〉
Liste complète des métadonnées

Littérature citée [44 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01366610
Contributeur : Valerie Issarny <>
Soumis le : jeudi 15 septembre 2016 - 00:23:41
Dernière modification le : jeudi 4 janvier 2018 - 17:52:01
Document(s) archivé(s) le : vendredi 16 décembre 2016 - 12:41:29

Fichier

117-issarny.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

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. Proceedings of the ACM/IFIP/USENIX Middleware'2016 Conference, 2016, Proceedings of the 2016 International Middleware Conference. 〈http://2016.middleware-conference.org/〉. 〈10.1145/2988336.2988353〉. 〈hal-01366610〉

Partager

Métriques

Consultations de la notice

559

Téléchargements de fichiers

570