A Context-Driven Worker Selection Framework for Crowd-Sensing

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.