A Context-Driven Worker Selection Framework for Crowd-Sensing

Jiangtao Wang, Yasha Wang, Sumi Helal, Daqing Zhang

Research output: Contribution to journalArticle

6 Citations (Scopus)
1 Downloads (Pure)

Abstract

Worker selection for many crowd-sensing tasks must consider various complex contexts to ensure high quality of data. Existing platforms and frameworks take only specific contexts into account to demonstrate motivating scenarios but do not provide general context models or frameworks in support of crowd-sensing at large. This paper proposes a novel worker selection framework, named WSelector, to more precisely select appropriate workers by taking various contexts into account. To achieve this goal, it first provides programming time support to help task creator define constraints. Then its runtime system adopts a two-phase process to select workers who are not only qualified but also more likely to undertake a crowd-sensing task. In the first phase, it selects
workers who satisfy predefined constraints. In the second phase, by leveraging the worker’s past participation history, it further selects those who are more likely to undertake a crowd-sensing task based on a case-based reasoning algorithm. We demonstrate the expressiveness of the framework by implementing multiple crowd-sensing tasks and evaluate the effectiveness of the case-based reasoning algorithm for willingness-based selection by using a questionnaire-generated dataset. Results show that our case-based
reasoning algorithm outperforms the currently practiced baseline method.
Original languageEnglish
Article number6958710
Number of pages16
JournalInternational Journal of Distributed Sensor Networks
Volume12
Issue number3
DOIs
Publication statusPublished - 1 Mar 2016
Externally publishedYes

Bibliographical note

Copyright © 2016 Jiangtao Wang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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