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When the Power of the Crowd Meets the Intelligence of the Middleware: The Mobile Phone Sensing Case

Authors:

Valérie Issarny,

Françoise SailhanAuthors Info & Claims

ACM SIGOPS Operating Systems Review, Volume 53, Issue 1

Pages 85 - 90

https://doi.org/10.1145/3352020.3352033

Published: 25 July 2019 Publication History

Abstract

The data gluttony of AI is well known: Data fuels the artificial intelligence. Technologies that help to gather the needed data are then essential, among which the IoT. However, the deployment of IoT solutions raises significant challenges, especially regarding the resource and financial costs at stake. It is our view that mobile crowdsensing, aka phone sensing, has a major role to play because it potentially contributes massive data at a relatively low cost. Still, crowdsensing is useless, and even harmful, if the contributed data are not properly analyzed. This paper surveys our work on the development of systems facing this challenge, which also illustrates the virtuous circles of AI. We specifically focus on how intelligent crowdsensing middleware leverages on-device machine learning to enhance the reported physical observations. Keywords: Crowdsensing, Middleware, Online learning.

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Cited By

Gazis AKatsiri E(2024)Streamline Intelligent Crowd Monitoring with IoT Cloud Computing MiddlewareSensors10.3390/s2411364324:11(3643)Online publication date: 4-Jun-2024
https://doi.org/10.3390/s24113643
Nasser RMizouni RSingh SOtrok H(2024)Systematic survey on artificial intelligence based mobile crowd sensing and sourcing solutions: Applications and security challengesAd Hoc Networks10.1016/j.adhoc.2024.103634164(103634)Online publication date: Nov-2024
https://doi.org/10.1016/j.adhoc.2024.103634
Li QLiu HZhao X(2023)IoT Networks-Aided Perception Vocal Music Singing Learning System and Piano Teaching with Edge ComputingMobile Information Systems10.1155/2023/20748902023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/2074890
Show More Cited By

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Information & Contributors

Information

Published In

cover image ACM SIGOPS Operating Systems Review

ACM SIGOPS Operating Systems Review Volume 53, Issue 1

July 2019

90 pages

ISSN:0163-5980

DOI:10.1145/3352020

Editors:
Robbert van Renesse
Cornell University, Ithaca, New York
,
Christopher J. Rossbach
Stop D9500, Austin, TX
,
Kishore Pusukuri
Santa Clara University
,
John Chandy
University of Connecticut
,
Antônio Fröhlich
Federal Univ. of Santa Catarina
,
Ashvin Goel
University of Toronto

Issue’s Table of Contents

Copyright © 2019 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2019

Published in SIGOPS Volume 53, Issue 1

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Cited By

Gazis AKatsiri E(2024)Streamline Intelligent Crowd Monitoring with IoT Cloud Computing MiddlewareSensors10.3390/s2411364324:11(3643)Online publication date: 4-Jun-2024
https://doi.org/10.3390/s24113643
Nasser RMizouni RSingh SOtrok H(2024)Systematic survey on artificial intelligence based mobile crowd sensing and sourcing solutions: Applications and security challengesAd Hoc Networks10.1016/j.adhoc.2024.103634164(103634)Online publication date: Nov-2024
https://doi.org/10.1016/j.adhoc.2024.103634
Li QLiu HZhao X(2023)IoT Networks-Aided Perception Vocal Music Singing Learning System and Piano Teaching with Edge ComputingMobile Information Systems10.1155/2023/20748902023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/2074890
Gazis AKatsiri E(2022)Crowd tracking and monitoring middleware via Map-ReduceInternational Journal of Parallel, Emergent and Distributed Systems10.1080/17445760.2022.203416337:3(333-343)Online publication date: 7-Feb-2022
https://doi.org/10.1080/17445760.2022.2034163
Du YIssarny VSailhan F(2020)In-network Collaborative Mobile Crowdsensing2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PerComWorkshops48775.2020.9156268(1-2)Online publication date: Mar-2020
https://doi.org/10.1109/PerComWorkshops48775.2020.9156268
Du YSailhan FIssarny V(2020)Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom45495.2020.9127391(1-10)Online publication date: Mar-2020
https://doi.org/10.1109/PerCom45495.2020.9127391

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