FCM: A fine-grained crowdsourcing model based on ontology in crowd-sensing

Jian An; Ruobiao Wu; Lele Xiang; Xiaolin Gui; Zhenlong Peng

doi:10.1007/978-3-319-47099-3_14

FCM: A fine-grained crowdsourcing model based on ontology in crowd-sensing

Jian An
, Ruobiao Wu
, Lele Xiang
, Xiaolin Gui
, Zhenlong Peng

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University
The Key Laboratory of Computer Network in Shaanxi Province
Quanzhou Normal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Crowd sensing between users with smart mobile devices is a new trend of development in Internet. In order to recommend the suitable service providers for crowd sensing requests, this paper presents a Fine-grained Crowdsourcing Model (FCM) based on Ontology theory that helps users to select appropriate service providers. First, the characteristic properties which extracted from the service request will be compared with the service provider based on ontology triple. Second, recommendation index of each service provider is calculated through similarity analysis and cluster analysis. Finally, the service decision tree is proposed to predict and recommend appropriate candidate users to participate in crowd sensing service. Experimental results show that this method provides more accurate recommendation than present recommendation systems and consumes less time to find the service provider through clustering algorithm.

Original language	English
Title of host publication	Network and Parallel Computing - 13th IFIP WG 10.3 International Conference, NPC 2016, Proceedings
Editors	Xinbo Gao, Barbara Chapman, Depei Qian, Wenguang Chen, Guang R. Gao
Publisher	Springer Verlag
Pages	172-179
Number of pages	8
ISBN (Print)	9783319470986
DOIs	https://doi.org/10.1007/978-3-319-47099-3_14
State	Published - 2016
Event	13th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2016 - Xi’an, China Duration: 28 Oct 2016 → 29 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9966 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2016
Country/Territory	China
City	Xi’an
Period	28/10/16 → 29/10/16

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10.1007/978-3-319-47099-3_14

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An, J., Wu, R., Xiang, L., Gui, X., & Peng, Z. (2016). FCM: A fine-grained crowdsourcing model based on ontology in crowd-sensing. In X. Gao, B. Chapman, D. Qian, W. Chen, & G. R. Gao (Eds.), Network and Parallel Computing - 13th IFIP WG 10.3 International Conference, NPC 2016, Proceedings (pp. 172-179). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9966 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-47099-3_14

An, Jian ; Wu, Ruobiao ; Xiang, Lele et al. / FCM : A fine-grained crowdsourcing model based on ontology in crowd-sensing. Network and Parallel Computing - 13th IFIP WG 10.3 International Conference, NPC 2016, Proceedings. editor / Xinbo Gao ; Barbara Chapman ; Depei Qian ; Wenguang Chen ; Guang R. Gao. Springer Verlag, 2016. pp. 172-179 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "FCM: A fine-grained crowdsourcing model based on ontology in crowd-sensing",

abstract = "Crowd sensing between users with smart mobile devices is a new trend of development in Internet. In order to recommend the suitable service providers for crowd sensing requests, this paper presents a Fine-grained Crowdsourcing Model (FCM) based on Ontology theory that helps users to select appropriate service providers. First, the characteristic properties which extracted from the service request will be compared with the service provider based on ontology triple. Second, recommendation index of each service provider is calculated through similarity analysis and cluster analysis. Finally, the service decision tree is proposed to predict and recommend appropriate candidate users to participate in crowd sensing service. Experimental results show that this method provides more accurate recommendation than present recommendation systems and consumes less time to find the service provider through clustering algorithm.",

author = "Jian An and Ruobiao Wu and Lele Xiang and Xiaolin Gui and Zhenlong Peng",

note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2016.; 13th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2016 ; Conference date: 28-10-2016 Through 29-10-2016",

year = "2016",

doi = "10.1007/978-3-319-47099-3\_14",

language = "英语",

isbn = "9783319470986",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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}

An, J, Wu, R, Xiang, L, Gui, X & Peng, Z 2016, FCM: A fine-grained crowdsourcing model based on ontology in crowd-sensing. in X Gao, B Chapman, D Qian, W Chen & GR Gao (eds), Network and Parallel Computing - 13th IFIP WG 10.3 International Conference, NPC 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9966 LNCS, Springer Verlag, pp. 172-179, 13th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2016, Xi’an, China, 28/10/16. https://doi.org/10.1007/978-3-319-47099-3_14

FCM: A fine-grained crowdsourcing model based on ontology in crowd-sensing. / An, Jian; Wu, Ruobiao; Xiang, Lele et al.
Network and Parallel Computing - 13th IFIP WG 10.3 International Conference, NPC 2016, Proceedings. ed. / Xinbo Gao; Barbara Chapman; Depei Qian; Wenguang Chen; Guang R. Gao. Springer Verlag, 2016. p. 172-179 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9966 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - FCM

T2 - 13th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2016

AU - An, Jian

AU - Wu, Ruobiao

AU - Xiang, Lele

AU - Gui, Xiaolin

AU - Peng, Zhenlong

N1 - Publisher Copyright: © IFIP International Federation for Information Processing 2016.

PY - 2016

Y1 - 2016

N2 - Crowd sensing between users with smart mobile devices is a new trend of development in Internet. In order to recommend the suitable service providers for crowd sensing requests, this paper presents a Fine-grained Crowdsourcing Model (FCM) based on Ontology theory that helps users to select appropriate service providers. First, the characteristic properties which extracted from the service request will be compared with the service provider based on ontology triple. Second, recommendation index of each service provider is calculated through similarity analysis and cluster analysis. Finally, the service decision tree is proposed to predict and recommend appropriate candidate users to participate in crowd sensing service. Experimental results show that this method provides more accurate recommendation than present recommendation systems and consumes less time to find the service provider through clustering algorithm.

AB - Crowd sensing between users with smart mobile devices is a new trend of development in Internet. In order to recommend the suitable service providers for crowd sensing requests, this paper presents a Fine-grained Crowdsourcing Model (FCM) based on Ontology theory that helps users to select appropriate service providers. First, the characteristic properties which extracted from the service request will be compared with the service provider based on ontology triple. Second, recommendation index of each service provider is calculated through similarity analysis and cluster analysis. Finally, the service decision tree is proposed to predict and recommend appropriate candidate users to participate in crowd sensing service. Experimental results show that this method provides more accurate recommendation than present recommendation systems and consumes less time to find the service provider through clustering algorithm.

UR - https://www.scopus.com/pages/publications/84994509815

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DO - 10.1007/978-3-319-47099-3_14

M3 - 会议稿件

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SN - 9783319470986

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 172

EP - 179

BT - Network and Parallel Computing - 13th IFIP WG 10.3 International Conference, NPC 2016, Proceedings

A2 - Gao, Xinbo

A2 - Chapman, Barbara

A2 - Qian, Depei

A2 - Chen, Wenguang

A2 - Gao, Guang R.

PB - Springer Verlag

Y2 - 28 October 2016 through 29 October 2016

ER -

An J, Wu R, Xiang L, Gui X, Peng Z. FCM: A fine-grained crowdsourcing model based on ontology in crowd-sensing. In Gao X, Chapman B, Qian D, Chen W, Gao GR, editors, Network and Parallel Computing - 13th IFIP WG 10.3 International Conference, NPC 2016, Proceedings. Springer Verlag. 2016. p. 172-179. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-47099-3_14

FCM: A fine-grained crowdsourcing model based on ontology in crowd-sensing

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