We study the optimal design of mobile crowdsensing campaigns in terms of the aggregate quality of contributions attracted for a set of tasks. The interaction of the campaign with users is realized through a mobile app interface that recommends tasks to users and offers them incentives. The main contribution is a novel perspective on the payment distribution problem faced by the crowdsensing campaign organizer in light of originally unknown individual user preferences. Contrary to common practice, we acknowledge that users exhibit high diversity in decision making because they assess differently attributes related to a task such as their proximity to the place of interest (PoI), the payment made for contributing data, or the task context/theme. We draw on logistic-regression techniques from machine learning to learn users' individual preferences from past data rather than hypothesizing about them. We then formulate non-linear (sigmoid) optimization problems to determine the tasks and incentives (payments) that should be optimally offered to each user. Our mechanism is validated against synthetic but also real data about the way users choose tasks, collected through an online questionnaire. It achieves very good approximations of the optimal solutions and substantially outperforms alternative preference-agnostic policies that do not exercise behavioral user profiling to target the provision of incentives.
In REF , we modeled each user through a logistic-regression model that determines the probability that the user will respond to a task request or not based on two attributes, the distance traveled or effort exerted to execute the task, and the incentive payment.
First learn then earn: optimizing mobile crowdsensing campaigns through data-driven user profiling
MobiHoc '16
Computer Science