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An incentive mechanism for crowdsourcing markets with social welfare maximization in cloud‐edge computing
Author(s) -
Xu Xiaolong,
Cai Qing,
Zhang Guoming,
Zhang Jie,
Tian Wei,
Zhang Xiaorui,
Liu Alex X.
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4961
Subject(s) - crowdsourcing , cloud computing , incentive , computer science , popularity , social welfare , selfishness , profit maximization , outsourcing , computer security , microeconomics , business , economics , world wide web , marketing , profit (economics) , psychology , social psychology , political science , law , operating system
Summary Crowdsourcing is emerging as a powerful paradigm that utilizes the distributed devices to sense, collect, and upload data to satisfy the requirements of the users. Currently, with the popularity of edge computing, edge device users can act as recruiters or participants to publish or perform crowdsourcing tasks and share feedback. However, due to the individual selfishness, it is still a challenge to maximize the social welfare distribution of all the participants and the recruiters for the crowdsourcing market. In view of this challenge, an incentive mechanism for the crowdsourcing market with social welfare maximization in the cloud‐edge computing is designed in this paper. Technically, a double action model under the cloud‐edge computing framework is proposed first, which aims to maximize the social welfare maximization and meanwhile meet the demands of incentive compatibility, individual rationality, market clearing, and budget constraint. Secondly, a corresponding incentive mechanism is designed based on the double auction model to achieve the market fairness. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of the mechanism.