University of Oulu

Wang, J., Wang, Y., Zhang, D., Goncalves, J., Ferreira, D., Visuri, A., Ma, S. (2019) Learning-Assisted Optimization in Mobile Crowd Sensing: A Survey. IEEE Transactions on Industrial Informatics, 15 (1), 15-22. doi:10.1109/TII.2018.2868703

Learning-assisted optimization in mobile crowd sensing : a survey

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Author: Wang, Jingtao1,2; Wang, Yasha3,2; Zhang, Daqing1,2;
Organizations: 1School of EECS
2Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University, Beijing 100871, China
3National Research & Engineering Center of Software Engineering
4School of Computing and Information Systems, University of Melbourne, VIC 100789, Australia
5University of Oulu, Oulu 109876, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019040110651
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2019-04-01
Description:

Abstract

Mobile crowd sensing (MCS) is a relatively new paradigm for collecting real-time and location-dependent urban sensing data. Given its applications, it is crucial to optimize the MCS process with the objective of maximizing the sensing quality and minimizing the sensing cost. While earlier studies mainly tackle this issue by designing different combinatorial optimization algorithms, there is a new trend to further optimize MCS by integrating learning techniques to extract knowledge, such as participants' behavioral patterns or sensing data correlation. In this paper, we perform an extensive literature review of learning-assisted optimization approaches in MCS. Specifically, from the perspective of the participant and the task, we organize the existing work into a conceptual framework, present different learning and optimization methods, and describe their evaluation. Furthermore, we discuss how different techniques can be combined to form a complete solution. In the end, we point out existing limitations, which can inform and guide future research directions.

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Series: IEEE transactions on industrial informatics
ISSN: 1551-3203
ISSN-E: 1941-0050
ISSN-L: 1551-3203
Volume: 15
Issue: 1
Pages: 15 - 22
DOI: 10.1109/TII.2018.2868703
OADOI: https://oadoi.org/10.1109/TII.2018.2868703
Type of Publication: A2 Review article in a scientific journal
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This work was supported by NSFC under Grant 61872010. Paper no. TII-18-2124.
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