Vis enkel innførsel

dc.contributor.authorBibri, Simon Elias
dc.date.accessioned2019-04-04T10:42:07Z
dc.date.available2019-04-04T10:42:07Z
dc.date.created2017-12-26T18:04:23Z
dc.date.issued2018
dc.identifier.citationSustainable cities and society. 2018, 38 230-253.nb_NO
dc.identifier.issn2210-6707
dc.identifier.urihttp://hdl.handle.net/11250/2593283
dc.description.abstractThe Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart sustainable cities as an emerging urban development approach due to its great potential to advance environmental sustainability. As one of the prevalent ICT visions or computing paradigms, the IoT is associated with big data analytics, which is clearly on a penetrative path across many urban domains for optimizing energy efficiency and mitigating environmental effects. This pertains mainly to the effective utilization of natural resources, the intelligent management of infrastructures and facilities, and the enhanced delivery of services in support of the environment. As such, the IoT and related big data applications can play a key role in catalyzing and improving the process of environmentally sustainable development. However, topical studies tend to deal largely with the IoT and related big data applications in connection with economic growth and the quality of life in the realm of smart cities, and largely ignore their role in improving environmental sustainability in the context of smart sustainable cities of the future. In addition, several advanced technologies are being used in smart cities without making any contribution to environmental sustainability, and the strategies through which sustainable cities can be achieved fall short in considering advanced technologies. Therefore, the aim of this paper is to review and synthesize the relevant literature with the objective of identifying and discussing the state-of-the-art sensor-based big data applications enabled by the IoT for environmental sustainability and related data processing platforms and computing models in the context of smart sustainable cities of the future. Also, this paper identifies the key challenges pertaining to the IoT and big data analytics, as well as discusses some of the associated open issues. Furthermore, it explores the opportunity of augmenting the informational landscape of smart sustainable cities with big data applications to achieve the required level of environmental sustainability. In doing so, it proposes a framework which brings together a large number of previous studies on smart cities and sustainable cities, including research directed at a more conceptual, analytical, and overarching level, as well as research on specific technologies and their novel applications. The goal of this study suits a mix of two research approaches: topical literature review and thematic analysis. In terms of originality, no study has been conducted on the IoT and related big data applications in the context of smart sustainable cities, and this paper provides a basis for urban researchers to draw on this analytical framework in future research. The proposed framework, which can be replicated, tested, and evaluated in empirical research, will add additional depth to studies in the field of smart sustainable cities. This paper serves to inform urban planners, scholars, ICT experts, and other city stakeholders about the environmental benefits that can be gained from implementing smart sustainable city initiatives and projects on the basis of the IoT and related big data applications.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.titleThe IoT for Smart Sustainable Cities of the Future: An Analytical Framework for Sensor–Based Big Data Applications for Environmental Sustainabilitynb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber230-253nb_NO
dc.source.volume38nb_NO
dc.source.journalSustainable cities and societynb_NO
dc.identifier.doi10.1016/j.scs.2017.12.034
dc.identifier.cristin1532016
dc.description.localcodeThis article will not be available due to copyright restrictions (c) 2018 by Elseviernb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel