Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2018

Abstract

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.

Keywords

mobile crowdsourcing, collaboration, service composition, reliability

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ACM Transactions on Internet Technology

Volume

18

Issue

2

First Page

A1

Last Page

A23

ISSN

1533-5399

Identifier

10.1145/3108935

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3108935

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