A Framework of Quality-Aware Personalized Task Matching For Mobile Crowdsensing

The proliferation of smartphone devices coupled with the rich development of mobile sensing technology has emerged a new form of sensing paradigm called Mobile Crowdsensing (MCS). It transforms smartphone users from passive consumers of information to producers by enabling them to contribute to various location-based sensing tasks (i.e. traffic monitoring). In MCS, task assignment is a significant issue where finding the best match between tasks and workers is crucial to ensure both the quality and effectiveness of a MCS platform. The previous studies on task assignment primarily focus on maximising the number of allocated tasks or minimising the travelling distances of the workers. However, they rarely pay attention to the credibility of the platform’s workers, which primarily depends on the careful investigation of users’ domain knowledge, trustworthiness and willingness level. To address the problem, a novel quality-aware personalised task-matching framework has been proposed in this study. The framework aims to match the right tasks to the right workers in a personalised way while workers’ credibility is taken into consideration. We conduct extensive experiments on real and synthetic datasets to evaluate the performance of our proposed model. Experimental results demonstrate the effectiveness of our proposed model in personalised task matching.