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Chapter 32 - Crowdsensing architectures for smart cities

dc.contributor.authorFiandrino, Claudio 
dc.contributor.authorCapponi, Andrea
dc.contributor.authorKliazovich, Dzmitry
dc.contributor.authorBouvry, Pascal
dc.date.accessioned2021-07-13T09:41:26Z
dc.date.available2021-07-13T09:41:26Z
dc.date.issued2020-02
dc.identifier.urihttp://hdl.handle.net/20.500.12761/793
dc.description.abstractSmart cities take advantage of information and communication technology developments to provide added value to existing public services and improve citizens' quality of life. Mobile crowdsensing (MCS) has become, in the last few years, one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, that is, the mobile devices consume energy for sensing and reporting operations. This chapter emphasizes the role of energy management in MCS by assessing the performance of multiple data-collection frameworks and presents the applicability of MCS in a smart public street lighting scenario.
dc.publisherElsevier
dc.relation.ispartofseriesMicro and Nano Technologies
dc.titleChapter 32 - Crowdsensing architectures for smart cities
dc.typebook part
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/B978012819870400030X
dc.book.titleNanosensors for Smart Cities
dc.page.final542
dc.page.initial527
dc.subject.keywordMobile crowdsensing
dc.subject.keywordurban sensing
dc.subject.keywordsmart cities
dc.subject.keywordenergy efficiency
dc.subject.keyworddata collection
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/2109


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