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CrowdService: optimizing mobile crowdsourcing and service composition

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posted on 2018-05-18, 14:47 authored by Xin Peng, Jingxiao Gu, Tian Huat Tan, Yijun Yu, Bashar NuseibehBashar Nuseibeh, Wenyun Zhao
Some user needs can only be met by leveraging the capabilities of others to undertake particular tasks that require intelligence and labor. Crowdsourcing such capabilities is one way to achieve this. But providing a service that leverages crowd intelligence and labor is a challenge, since various factors need to be considered to enable reliable service provisioning. For example, the selection of an optimal set of workers from those who bid to perform a task needs to be made based on their reliability, expected reward, and distance to the target locations. Moreover, for an application involving multiple services, the overall cost and time constraints must be optimally allocated to each involved service. In this paper, we develop a framework, named CROWDSERVICE, which supplies crowd intelligence and labor as publicly accessible crowd services via mobile crowdsourcing. The paper extends our earlier work by providing an approach for constraints synthesis and worker selection. It employs a genetic algorithm to dynamically synthesize and update near-optimal cost and time constraints for each crowd service involved in a composite service, and selects a near-optimal set of workers for each crowd service to be executed. We implement the proposed framework on Android platforms, and evaluate its effectiveness, scalability and usability in both experimental and user studies.

Funding

Study on Aerodynamic Characteristics Control of Slender Body Using Active Flow Control Technique

Japan Society for the Promotion of Science

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History

Publication

ACM Transactions on Internet Technology (TOIT) - Special Issue on Internetware and Devops and Regular Papers;18 (2), article 19

Publisher

Association for Computing Machinery

Note

peer-reviewed

Other Funding information

National High Technology Development 863 Program of China, SFI, ERC

Rights

© ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published inACM Transactions on Internet Technology (TOIT) - Special Issue on Internetware and Devops and Regular Papers, 2018, 18 (2), article 2, http://dx.doi.org/10.1145/3108935

Language

English

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