Incentive Mechanism for Spatial Crowdsourcing Cooperation: A Fair Revenue Allocation Method

Xiaowei Wang; An Liu; Shushu Liu; Junhua Fang; Jiajie Xu

doi:10.1007/978-3-030-91431-8_49

Incentive Mechanism for Spatial Crowdsourcing Cooperation: A Fair Revenue Allocation Method

Xiaowei Wang, An Liu^*, Shushu Liu, Junhua Fang, Jiajie Xu

^*Corresponding author for this work

Soochow University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

1 Citation (Scopus)

Abstract

With the development of intelligent mobile devices, spatial crowdsourcing (SC) has become popular recently, and SC platforms cooperation has attracted people’s attention, which can enlarge the whole social-economic benefits. The way to encourage platforms to take part in cooperation is essential. A viable method is to make each platform in cooperation get higher revenue via allocating the revenue generated by cooperation. However, the current work lack studies on the revenue allocation in SC platform cooperation. In this paper, based on cooperative game theory, we propose some ideal properties that the revenue allocation method should satisfy. Then, based on Shapley value, we propose a fair revenue allocation method named SRA, measured by the marginal contribution of each platform. Given the exponential complexity of the method, we propose an efficient approximation method, Coalition-based Shapley value Revenue Allocation (CSRA). Extensive experimental results verify the effectiveness and efficiency of our algorithms.

Original language	English
Title of host publication	Service-Oriented Computing
Subtitle of host publication	19th International Conference, ICSOC 2021, Proceedings
Editors	Hakim Hacid, Odej Kao, Massimo Mecella, Naouel Moha, Hye-young Paik
Publisher	Springer
Pages	728-735
Number of pages	8
Edition	1
ISBN (Electronic)	978-3-030-91431-8
ISBN (Print)	978-3-030-91430-1
DOIs	https://doi.org/10.1007/978-3-030-91431-8_49
Publication status	Published - 2021
MoE publication type	A4 Conference publication
Event	International Conference on Service Oriented Computing - Virtual, Online Duration: 22 Nov 2021 → 25 Nov 2021 Conference number: 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13121 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Service Oriented Computing
Abbreviated title	ICSOC
City	Virtual, Online
Period	22/11/2021 → 25/11/2021

Keywords

Incentive mechanism
Revenue allocation
Shapley value
Spatial crowdsourcing

Access to Document

10.1007/978-3-030-91431-8_49

Cite this

Wang, X., Liu, A., Liu, S., Fang, J., & Xu, J. (2021). Incentive Mechanism for Spatial Crowdsourcing Cooperation: A Fair Revenue Allocation Method. In H. Hacid, O. Kao, M. Mecella, N. Moha, & H. Paik (Eds.), Service-Oriented Computing: 19th International Conference, ICSOC 2021, Proceedings (1 ed., pp. 728-735). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13121 LNCS). Springer. https://doi.org/10.1007/978-3-030-91431-8_49

Wang, Xiaowei ; Liu, An ; Liu, Shushu et al. / Incentive Mechanism for Spatial Crowdsourcing Cooperation : A Fair Revenue Allocation Method. Service-Oriented Computing: 19th International Conference, ICSOC 2021, Proceedings. editor / Hakim Hacid ; Odej Kao ; Massimo Mecella ; Naouel Moha ; Hye-young Paik. 1. ed. Springer, 2021. pp. 728-735 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c8ceaae47ba040358b19b86f84db7705,

title = "Incentive Mechanism for Spatial Crowdsourcing Cooperation: A Fair Revenue Allocation Method",

abstract = "With the development of intelligent mobile devices, spatial crowdsourcing (SC) has become popular recently, and SC platforms cooperation has attracted people{\textquoteright}s attention, which can enlarge the whole social-economic benefits. The way to encourage platforms to take part in cooperation is essential. A viable method is to make each platform in cooperation get higher revenue via allocating the revenue generated by cooperation. However, the current work lack studies on the revenue allocation in SC platform cooperation. In this paper, based on cooperative game theory, we propose some ideal properties that the revenue allocation method should satisfy. Then, based on Shapley value, we propose a fair revenue allocation method named SRA, measured by the marginal contribution of each platform. Given the exponential complexity of the method, we propose an efficient approximation method, Coalition-based Shapley value Revenue Allocation (CSRA). Extensive experimental results verify the effectiveness and efficiency of our algorithms.",

keywords = "Incentive mechanism, Revenue allocation, Shapley value, Spatial crowdsourcing",

author = "Xiaowei Wang and An Liu and Shushu Liu and Junhua Fang and Jiajie Xu",

note = "Funding Information: This work was supported by the National Natural Science Foundation of China (Grant Nos. 61572336, 61632016, 62072323), the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20211307, BK20191420), the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China (Grant Nos. 18KJA520010, 19KJA610002), and the Collaborative Innovation Center of Novel Software Technology and Industrialization. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; International Conference on Service Oriented Computing, ICSOC ; Conference date: 22-11-2021 Through 25-11-2021",

year = "2021",

doi = "10.1007/978-3-030-91431-8_49",

language = "English",

isbn = "978-3-030-91430-1",

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

publisher = "Springer",

pages = "728--735",

editor = "Hakim Hacid and Odej Kao and Massimo Mecella and Naouel Moha and Hye-young Paik",

booktitle = "Service-Oriented Computing",

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}

Wang, X, Liu, A, Liu, S, Fang, J & Xu, J 2021, Incentive Mechanism for Spatial Crowdsourcing Cooperation: A Fair Revenue Allocation Method. in H Hacid, O Kao, M Mecella, N Moha & H Paik (eds), Service-Oriented Computing: 19th International Conference, ICSOC 2021, Proceedings. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13121 LNCS, Springer, pp. 728-735, International Conference on Service Oriented Computing, Virtual, Online, 22/11/2021. https://doi.org/10.1007/978-3-030-91431-8_49

Incentive Mechanism for Spatial Crowdsourcing Cooperation: A Fair Revenue Allocation Method. / Wang, Xiaowei; Liu, An; Liu, Shushu et al.
Service-Oriented Computing: 19th International Conference, ICSOC 2021, Proceedings. ed. / Hakim Hacid; Odej Kao; Massimo Mecella; Naouel Moha; Hye-young Paik. 1. ed. Springer, 2021. p. 728-735 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13121 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Incentive Mechanism for Spatial Crowdsourcing Cooperation

T2 - International Conference on Service Oriented Computing

AU - Wang, Xiaowei

AU - Liu, An

AU - Liu, Shushu

AU - Fang, Junhua

AU - Xu, Jiajie

N1 - Conference code: 19

PY - 2021

Y1 - 2021

N2 - With the development of intelligent mobile devices, spatial crowdsourcing (SC) has become popular recently, and SC platforms cooperation has attracted people’s attention, which can enlarge the whole social-economic benefits. The way to encourage platforms to take part in cooperation is essential. A viable method is to make each platform in cooperation get higher revenue via allocating the revenue generated by cooperation. However, the current work lack studies on the revenue allocation in SC platform cooperation. In this paper, based on cooperative game theory, we propose some ideal properties that the revenue allocation method should satisfy. Then, based on Shapley value, we propose a fair revenue allocation method named SRA, measured by the marginal contribution of each platform. Given the exponential complexity of the method, we propose an efficient approximation method, Coalition-based Shapley value Revenue Allocation (CSRA). Extensive experimental results verify the effectiveness and efficiency of our algorithms.

AB - With the development of intelligent mobile devices, spatial crowdsourcing (SC) has become popular recently, and SC platforms cooperation has attracted people’s attention, which can enlarge the whole social-economic benefits. The way to encourage platforms to take part in cooperation is essential. A viable method is to make each platform in cooperation get higher revenue via allocating the revenue generated by cooperation. However, the current work lack studies on the revenue allocation in SC platform cooperation. In this paper, based on cooperative game theory, we propose some ideal properties that the revenue allocation method should satisfy. Then, based on Shapley value, we propose a fair revenue allocation method named SRA, measured by the marginal contribution of each platform. Given the exponential complexity of the method, we propose an efficient approximation method, Coalition-based Shapley value Revenue Allocation (CSRA). Extensive experimental results verify the effectiveness and efficiency of our algorithms.

KW - Incentive mechanism

KW - Revenue allocation

KW - Shapley value

KW - Spatial crowdsourcing

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U2 - 10.1007/978-3-030-91431-8_49

DO - 10.1007/978-3-030-91431-8_49

M3 - Conference article in proceedings

AN - SCOPUS:85120520556

SN - 978-3-030-91430-1

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

SP - 728

EP - 735

BT - Service-Oriented Computing

A2 - Hacid, Hakim

A2 - Kao, Odej

A2 - Mecella, Massimo

A2 - Moha, Naouel

A2 - Paik, Hye-young

PB - Springer

Y2 - 22 November 2021 through 25 November 2021

ER -

Wang X, Liu A, Liu S, Fang J, Xu J. Incentive Mechanism for Spatial Crowdsourcing Cooperation: A Fair Revenue Allocation Method. In Hacid H, Kao O, Mecella M, Moha N, Paik H, editors, Service-Oriented Computing: 19th International Conference, ICSOC 2021, Proceedings. 1 ed. Springer. 2021. p. 728-735. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-91431-8_49

Incentive Mechanism for Spatial Crowdsourcing Cooperation: A Fair Revenue Allocation Method

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