Task Allocation in Mobile Crowd Sensing: State-of-The-Art and Future Opportunities

Jiangtao Wang; Leye Wang; Yasha Wang; Daqing Zhang; Linghe Kong

doi:10.1109/JIOT.2018.2864341

Task Allocation in Mobile Crowd Sensing: State-of-The-Art and Future Opportunities

Jiangtao Wang
, Leye Wang
, Yasha Wang
, Daqing Zhang
, Linghe Kong

Ministry of Education of the People's Republic of China
The Hong Kong University of Science and Technology
Shanghai Jiao Tong University

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

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.

langue originale	Anglais
Numéro d'article	8429062
Pages (de - à)	3747-3757
Nombre de pages	11
journal	IEEE Internet of Things Journal
Volume	5
Numéro de publication	5
Les DOIs	https://doi.org/10.1109/JIOT.2018.2864341
état	Publié - 1 oct. 2018
Modification externe	Oui

Accès au document

10.1109/JIOT.2018.2864341

Contient cette citation

@article{153ef1b000814ea2ba8b56e6333955c8,

title = "Task Allocation in Mobile Crowd Sensing: State-of-The-Art and Future Opportunities",

abstract = "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.",

keywords = "Crowdsourcing, mobile crowd sensing (MCS), task allocation",

author = "Jiangtao Wang and Leye Wang and Yasha Wang and Daqing Zhang and Linghe Kong",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.",

year = "2018",

month = oct,

day = "1",

doi = "10.1109/JIOT.2018.2864341",

language = "English",

volume = "5",

pages = "3747--3757",

journal = "IEEE Internet of Things Journal",

issn = "2327-4662",

number = "5",

}

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

U2 - 10.1109/JIOT.2018.2864341

DO - 10.1109/JIOT.2018.2864341

M3 - Article

AN - SCOPUS:85051371752

SN - 2327-4662

VL - 5

SP - 3747

EP - 3757

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

IS - 5

M1 - 8429062

ER -

Task Allocation in Mobile Crowd Sensing: State-of-The-Art and Future Opportunities

Résumé

Accès au document

Empreinte digitale

Contient cette citation