Open AccessCrowdsensing big data: sensing, data selection, and understanding
Author(s) -
Shuying Zhai,
Ru Li,
Yuange Yang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012045
Subject(s) - crowdsensing , computer science , big data , participatory sensing , wearable computer , data science , key (lock) , cloud computing , data extraction , wearable technology , mobile device , world wide web , computer security , data mining , embedded system , medline , political science , law , operating system
Mobile Crowdsensing (MCS) has become an emerging paradigm for large-scale sensing. It empowers ordinary citizens to contribute data sensed or generated from their mobile devices (e.g., smartphones, wearable devices), aggregates and fuses the data in the cloud for crowd intelligence extraction and human-centric service delivery. The data contributed by the crowd in MCS systems presents the features such as multi-modal, rich-content, spatio-temporal, and human-centric. The key challenges and techniques about crowdsensing big data were discussed. The recent progress of our group in this promising research area was described.