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Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Authors:

Francesco Restuccia,

Shameek Bhattacharjee,

Tommaso MelodiaAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 13, Issue 4

Article No.: 34, Pages 1 - 43

https://doi.org/10.1145/3139256

Published: 30 November 2017 Publication History

Abstract

Smartphones have become the most pervasive devices in people’s lives and are clearly transforming the way we live and perceive technology. Today’s smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as an accelerometer, a gyroscope, a microphone, and a camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this article, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing and analyze in depth the current state of the art on the topic. We also outline novel research challenges, along with possible directions of future work.

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cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 13, Issue 4

November 2017

290 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3139355

Editor:
Chenyang Lu
Department of Computer Science and Engineering/School of Engineering and Applied Sciences/Washington University/1 Brookings Drive/St. Louis, MO

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Publication History

Published: 30 November 2017

Accepted: 01 August 2017

Revised: 01 August 2017

Received: 01 June 2017

Published in TOSN Volume 13, Issue 4

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Liu KPeng SGong WZhang BLi C(2024)Hybrid User-Based Task Assignment for Mobile Crowdsensing: Problem and AlgorithmIEEE Internet of Things Journal10.1109/JIOT.2024.336795811:11(19589-19601)Online publication date: 1-Jun-2024
https://doi.org/10.1109/JIOT.2024.3367958
Xue JXu YWu WZhang TShen QZhou HZhuang W(2024)Sparse Mobile Crowdsensing for Cost-Effective Traffic State Estimation With Spatio–Temporal Transformer Graph Neural NetworkIEEE Internet of Things Journal10.1109/JIOT.2024.335655411:9(16227-16242)Online publication date: 1-May-2024
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https://doi.org/10.1109/CCNC51664.2024.10454892
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https://doi.org/10.1109/CCNC51664.2024.10454822
Yao XXing WZheng KQi CLi XSong Q(2024)GTDIM: Grid-based Two-stage Dynamic Incentive Mechanism for Mobile Crowd SensingPervasive and Mobile Computing10.1016/j.pmcj.2024.101964103(101964)Online publication date: Oct-2024
https://doi.org/10.1016/j.pmcj.2024.101964
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