Atmos: a hybrid crowdsourcing approach to weather estimation

Niforatos, Evangelos; Vourvopoulos, Athanasios; Langheinrich, Marc; Campos, Pedro; Doria, Andre

Publication

Atmos: a hybrid crowdsourcing approach to weather estimation

2014Conference object

dc.contributor.author	Niforatos, Evangelos
dc.contributor.author	Vourvopoulos, Athanasios
dc.contributor.author	Langheinrich, Marc
dc.contributor.author	Campos, Pedro
dc.contributor.author	Doria, Andre
dc.date.accessioned	2022-09-19T10:05:17Z
dc.date.available	2022-09-19T10:05:17Z
dc.date.issued	2014
dc.description.abstract	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.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	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).	pt_PT
dc.identifier.doi	10.1145/2638728.2638780	pt_PT
dc.identifier.uri	http://hdl.handle.net/10400.13/4623
dc.language.iso	eng	pt_PT
dc.publisher	ACM	pt_PT
dc.relation	RECALL: Enhanced Human Memory
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	pt_PT
dc.subject	Sensor networks	pt_PT
dc.subject	Smart cities	pt_PT
dc.subject	Crowd sensing	pt_PT
dc.subject	Mobile sensing	pt_PT
dc.subject	.	pt_PT
dc.subject	Faculdade de Ciências Exatas e da Engenharia	pt_PT
dc.title	Atmos: a hybrid crowdsourcing approach to weather estimation	pt_PT
dc.type	conference object
dspace.entity.type	Publication
oaire.awardTitle	RECALL: Enhanced Human Memory
oaire.awardURI	info:eu-repo/grantAgreement/EC/FP7/612933/EU
oaire.citation.endPage	138	pt_PT
oaire.citation.startPage	135	pt_PT
oaire.citation.title	Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication	pt_PT
oaire.fundingStream	FP7
person.familyName	Vourvopoulos
person.familyName	Pereira Campos
person.givenName	Athanasios
person.givenName	Pedro Filipe
person.identifier	279446
person.identifier.ciencia-id	5813-A481-A9D3
person.identifier.ciencia-id	7C19-B5E5-01CA
person.identifier.orcid	0000-0001-9676-8599
person.identifier.orcid	0000-0001-7706-5038
person.identifier.rid	F-3872-2017
person.identifier.scopus-author-id	48762198300
project.funder.identifier	http://doi.org/10.13039/501100008530
project.funder.name	European Commission
rcaap.rights	openAccess	pt_PT
rcaap.type	conferenceObject	pt_PT
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