Research output: Contribution to Journal/Magazine › Review article › peer-review
Research output: Contribution to Journal/Magazine › Review article › peer-review
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TY - JOUR
T1 - Task Allocation in Mobile Crowd Sensing
T2 - State-of-the-Art and Future Opportunities
AU - Wang, Jiangtao
AU - Wang, Leye
AU - Wang, Yasha
AU - Zhang, Daqing
AU - Kong, Linghe
PY - 2018/10/1
Y1 - 2018/10/1
N2 - Mobile crowd sensing (MCS) is the special case of crowdsourcing, which leverages the smartphones with various embedded sensors and user's mobility to sense diverse phenomenon in a city. Task allocation is a fundamental research issue in MCS, which is crucial for the efficiency and effectiveness of MCS applications. In this paper, we specifically focus on the task allocation in MCS systems. We first present the unique features of MCS allocation compared to generic crowdsourcing, and then provide a comprehensive review for diversifying problem formulation and allocation algorithms together with future research opportunities.
AB - Mobile crowd sensing (MCS) is the special case of crowdsourcing, which leverages the smartphones with various embedded sensors and user's mobility to sense diverse phenomenon in a city. Task allocation is a fundamental research issue in MCS, which is crucial for the efficiency and effectiveness of MCS applications. In this paper, we specifically focus on the task allocation in MCS systems. We first present the unique features of MCS allocation compared to generic crowdsourcing, and then provide a comprehensive review for diversifying problem formulation and allocation algorithms together with future research opportunities.
KW - Crowdsourcing
KW - mobile crowd sensing (MCS)
KW - task allocation
KW - PARTICIPANT SELECTION
KW - INTERNET
KW - ASSIGNMENT
KW - FRAMEWORK
KW - PRIVACY
KW - BUDGET
U2 - 10.1109/JIOT.2018.2864341
DO - 10.1109/JIOT.2018.2864341
M3 - Review article
VL - 5
SP - 3747
EP - 3757
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
SN - 2327-4662
IS - 5
ER -