research-article

The Fog Makes Sense: Enabling Social Sensing Services with Limited Internet Connectivity

Authors:

Enrique Saurez,

Umakishore RamachandranAuthors Info & Claims

SocialSens'17: Proceedings of the 2nd International Workshop on Social Sensing

Pages 61 - 66

https://doi.org/10.1145/3055601.3055614

Published: 18 April 2017 Publication History

Abstract

Social sensing services use humans as sensor carriers, sensor operators and sensors themselves in order to provide situation-awareness to applications. This promises to provide a multitude of benefits to the users, for example in the management of natural disasters or in community empowerment. However, current social sensing services depend on Internet connectivity since the services are deployed on central Cloud platforms. In many circumstances, Internet connectivity is constrained, for instance when a natural disaster causes Internet outages or when people do not have Internet access due to economical reasons. In this paper, we propose the emerging Fog Computing infrastructure to become a key-enabler of social sensing services in situations of constrained Internet connectivity. To this end, we develop a generic architecture and API of Fog-enabled social sensing services. We exemplify the usage of the proposed social sensing architecture on a number of concrete use cases from two different scenarios.

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Cited By

Xing XYu BKang CHuang BGong JLiu Y(2024)The Synergy Between Remote Sensing and Social Sensing in Urban Studies: Review and perspectivesIEEE Geoscience and Remote Sensing Magazine10.1109/MGRS.2023.334396812:1(108-137)Online publication date: Mar-2024
https://doi.org/10.1109/MGRS.2023.3343968
Modi AJani SChauhan KBhatia J(2020)Process Model for Fog Data Analytics for IoT ApplicationsFog Data Analytics for IoT Applications10.1007/978-981-15-6044-6_9(175-198)Online publication date: 26-Aug-2020
https://doi.org/10.1007/978-981-15-6044-6_9
Puliafito CMingozzi ELongo FPuliafito ARana O(2019)Fog Computing for the Internet of ThingsACM Transactions on Internet Technology10.1145/330144319:2(1-41)Online publication date: 2-Apr-2019
https://dl.acm.org/doi/10.1145/3301443
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The Fog Makes Sense: Enabling Social Sensing Services with Limited Internet Connectivity
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cover image ACM Conferences

SocialSens'17: Proceedings of the 2nd International Workshop on Social Sensing

April 2017

97 pages

ISBN:9781450349772

DOI:10.1145/3055601

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 18 April 2017

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CPS Week '17

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CPS Week '17: Cyber Physical Systems Week 2017

April 18 - 21, 2017

PA, Pittsburgh, USA

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Cited By

Xing XYu BKang CHuang BGong JLiu Y(2024)The Synergy Between Remote Sensing and Social Sensing in Urban Studies: Review and perspectivesIEEE Geoscience and Remote Sensing Magazine10.1109/MGRS.2023.334396812:1(108-137)Online publication date: Mar-2024
https://doi.org/10.1109/MGRS.2023.3343968
Modi AJani SChauhan KBhatia J(2020)Process Model for Fog Data Analytics for IoT ApplicationsFog Data Analytics for IoT Applications10.1007/978-981-15-6044-6_9(175-198)Online publication date: 26-Aug-2020
https://doi.org/10.1007/978-981-15-6044-6_9
Puliafito CMingozzi ELongo FPuliafito ARana O(2019)Fog Computing for the Internet of ThingsACM Transactions on Internet Technology10.1145/330144319:2(1-41)Online publication date: 2-Apr-2019
https://dl.acm.org/doi/10.1145/3301443
Franchi FMarotta ARinaldi CGraziosi FDerrico L(2019)IoT-based Disaster Management System on 5G uRLLC Network2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)10.1109/ICT-DM47966.2019.9032897(1-4)Online publication date: Dec-2019
https://doi.org/10.1109/ICT-DM47966.2019.9032897
Martin JKandasamy AChandrasekaran KJoseph C(2019)Elucidating the challenges for the praxis of fog computing: An aspect‐based studyInternational Journal of Communication Systems10.1002/dac.392632:7Online publication date: 27-Feb-2019
https://doi.org/10.1002/dac.3926
Tomlein MGrønbæk K(2018)Augmented Reality Supported Modeling of Industrial Systems to Infer Software ConfigurationProceedings of the ACM on Human-Computer Interaction10.1145/32290872:EICS(1-17)Online publication date: 19-Jun-2018
https://dl.acm.org/doi/10.1145/3229087
Mayer RGraser LGupta HSaurez ERamachandran U(2017)EmuFog: Extensible and scalable emulation of large-scale fog computing infrastructures2017 IEEE Fog World Congress (FWC)10.1109/FWC.2017.8368525(1-6)Online publication date: Oct-2017
https://doi.org/10.1109/FWC.2017.8368525
Mayer RGupta HSaurez ERamachandran U(2017)FogStore: Toward a distributed data store for Fog computing2017 IEEE Fog World Congress (FWC)10.1109/FWC.2017.8368524(1-6)Online publication date: Oct-2017
https://doi.org/10.1109/FWC.2017.8368524
Bach TTariq MMayer RRothermel K(2017)Knowledge Is at the Edge! How to Search in Distributed Machine Learning ModelsOn the Move to Meaningful Internet Systems. OTM 2017 Conferences10.1007/978-3-319-69462-7_27(410-428)Online publication date: 20-Oct-2017
https://doi.org/10.1007/978-3-319-69462-7_27

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