Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
With an increasing number of rich embedded sensors, like accelerometer and GPS, smartphone becomes a pervasive people-centric sensing platform for inferring user’s daily activities and social contexts. Alternatively, wireless sensor network offers a comprehensive platform for capturing the surrounding environmental information using mobile sensing nodes, e.g., the OpenSense project [2] in Switzerland deploying air quality sensors like CO on public transports like buses and trams. The two sensing platforms are typically isolated from each other. In this paper, we build ExposureSense, a rich mobile participatory sensing infrastructure that integrates the two independent sensing paradigms. ExposureSense is able to monitor people’s daily activities as well to compute a reasonable estimation of pollution exposure in their daily life. Besides using external sensor networks, ExposureSense also supports pluggable sensors (e.g., O3) to further enrich air quality data using mobile participatory sensing with smartphones.
Andreas Peter Burg, Andreas Toftegaard Kristensen
Florian Frédéric Vincent Breider, Thibault Béranger Masset