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Atmos: a hybrid crowdsourcing approach to weather estimation
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Orientador(es)
Resumo(s)
Motivated by the novel paradigm of participatory
sensing in collecting in situ automated data and human
input we introduce the Atmos platform. Atmos
leverages a crowd-sourcing network of mobile devices
for the collection of in situ weather related sensory
data, provided by available on-board sensors, along
with human input, to generate highly localized
information about current and future weather
conditions. In this paper, we share our first insights of
an 8-month long deployment of Atmos mobile app on
Google Play that gathered data from a total of 9
countries across 3 continents. Furthermore, we
describe the underlying system infrastructure and
showcase how a hybrid people-centric and
environment-centric approach to weather estimation
could benefit forecasting. Finally, we present our
preliminary results originating from questionnaires
inquiring into how people perceive the weather, how
they use technology to know about the weather and
how it affects their habits.
Descrição
Palavras-chave
Sensor networks Smart cities Crowd sensing Mobile sensing . Faculdade de Ciências Exatas e da Engenharia
Contexto Educativo
Citação
Niforatos, E., Vourvopoulos, A., Langheinrich, M., Campos, P., & Doria, A. (2014, September). Atmos: a hybrid crowdsourcing approach to weather estimation. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (pp. 135-138).
Editora
ACM