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WSelector: A multi-scenario and multi-view worker selection framework for crowd-sensing

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WSelector : A multi-scenario and multi-view worker selection framework for crowd-sensing. / Wang, J.; Helal, Sumi; Wang, Y.; Zhang, D.; J., Ma (Editor); A., Li (Editor); H., Ning (Editor); L.T., Yang (Editor).

Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015 IEEE 12th Intl Conf on. IEEE, 2015. p. 54-61.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Wang, J, Helal, S, Wang, Y, Zhang, D, J., M (ed.), A., L (ed.), H., N (ed.) & L.T., Y (ed.) 2015, WSelector: A multi-scenario and multi-view worker selection framework for crowd-sensing. in Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015 IEEE 12th Intl Conf on. IEEE, pp. 54-61. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.32

APA

Wang, J., Helal, S., Wang, Y., Zhang, D., J., M. (Ed.), A., L. (Ed.), H., N. (Ed.), & L.T., Y. (Ed.) (2015). WSelector: A multi-scenario and multi-view worker selection framework for crowd-sensing. In Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015 IEEE 12th Intl Conf on (pp. 54-61). IEEE. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.32

Vancouver

Wang J, Helal S, Wang Y, Zhang D, J. M, (ed.), A. L, (ed.) et al. WSelector: A multi-scenario and multi-view worker selection framework for crowd-sensing. In Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015 IEEE 12th Intl Conf on. IEEE. 2015. p. 54-61 https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.32

Author

Wang, J. ; Helal, Sumi ; Wang, Y. ; Zhang, D. ; J., Ma (Editor) ; A., Li (Editor) ; H., Ning (Editor) ; L.T., Yang (Editor). / WSelector : A multi-scenario and multi-view worker selection framework for crowd-sensing. Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015 IEEE 12th Intl Conf on. IEEE, 2015. pp. 54-61

Bibtex

@inproceedings{3c5a4d5f1e4f416fa5f7b7e2575b167b,
title = "WSelector: A multi-scenario and multi-view worker selection framework for crowd-sensing",
abstract = "Worker selection is very crucial for crowd-sensing to ensure high data quality. Existing approaches have two limitations. First, they only take specific factors into account for their motivating application scenarios, but do not provide general models in support of crowd-sensing at large. Second, they select workers only in terms of the requirements defined by the task creator without considering other worker-required factors. To overcome abovementioned limitations, this paper proposes a novel worker selection framework for crowd sensing. Compared to existing work, it mainly has following two characteristics. (1) Multi-scenario. Instead of defining specific factors, we propose a core ontology model to semantically express general factors, based on which task creators can build their own task-specific models efficiently. (2) Multi-view. We propose a two-phase process to select workers by considering factors both from the task creator and worker. We evaluate the effectiveness of the worker selection process by using a questionnaire-generated dataset. Results show that our approach outperforms the baseline method. {\textcopyright} 2015 IEEE.",
keywords = "Crowd-Sensing, Framework, Worker Selection, Big data, Internet, Trusted computing, Application scenario, Baseline methods, Selection framework, Task-specific models, Two-phase process, Ubiquitous computing",
author = "J. Wang and Sumi Helal and Y. Wang and D. Zhang and Ma J. and Li A. and Ning H. and Yang L.T.",
year = "2015",
month = aug,
day = "10",
doi = "10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.32",
language = "English",
isbn = "9781467372121",
pages = "54--61",
booktitle = "Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015 IEEE 12th Intl Conf on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - WSelector

T2 - A multi-scenario and multi-view worker selection framework for crowd-sensing

AU - Wang, J.

AU - Helal, Sumi

AU - Wang, Y.

AU - Zhang, D.

A2 - J., Ma

A2 - A., Li

A2 - H., Ning

A2 - L.T., Yang

PY - 2015/8/10

Y1 - 2015/8/10

N2 - Worker selection is very crucial for crowd-sensing to ensure high data quality. Existing approaches have two limitations. First, they only take specific factors into account for their motivating application scenarios, but do not provide general models in support of crowd-sensing at large. Second, they select workers only in terms of the requirements defined by the task creator without considering other worker-required factors. To overcome abovementioned limitations, this paper proposes a novel worker selection framework for crowd sensing. Compared to existing work, it mainly has following two characteristics. (1) Multi-scenario. Instead of defining specific factors, we propose a core ontology model to semantically express general factors, based on which task creators can build their own task-specific models efficiently. (2) Multi-view. We propose a two-phase process to select workers by considering factors both from the task creator and worker. We evaluate the effectiveness of the worker selection process by using a questionnaire-generated dataset. Results show that our approach outperforms the baseline method. © 2015 IEEE.

AB - Worker selection is very crucial for crowd-sensing to ensure high data quality. Existing approaches have two limitations. First, they only take specific factors into account for their motivating application scenarios, but do not provide general models in support of crowd-sensing at large. Second, they select workers only in terms of the requirements defined by the task creator without considering other worker-required factors. To overcome abovementioned limitations, this paper proposes a novel worker selection framework for crowd sensing. Compared to existing work, it mainly has following two characteristics. (1) Multi-scenario. Instead of defining specific factors, we propose a core ontology model to semantically express general factors, based on which task creators can build their own task-specific models efficiently. (2) Multi-view. We propose a two-phase process to select workers by considering factors both from the task creator and worker. We evaluate the effectiveness of the worker selection process by using a questionnaire-generated dataset. Results show that our approach outperforms the baseline method. © 2015 IEEE.

KW - Crowd-Sensing

KW - Framework

KW - Worker Selection

KW - Big data

KW - Internet

KW - Trusted computing

KW - Application scenario

KW - Baseline methods

KW - Selection framework

KW - Task-specific models

KW - Two-phase process

KW - Ubiquitous computing

U2 - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.32

DO - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.32

M3 - Conference contribution/Paper

SN - 9781467372121

SP - 54

EP - 61

BT - Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015 IEEE 12th Intl Conf on

PB - IEEE

ER -