Ellipsoid-Pricing-Based Context-Feature Mechanisms for Noisy Sensing Tasks

Recently, online pricing receives extensive attentions for addressing more realistic mobile crowdsensing (MCS) applications. However, existing these works mainly focus on finding an optimal price by repeated price experiments of user’s acceptance decision, which incurs a desirable regret. In practice, there are also many other factors that will affect these regrets, such contextual features as discrete features and continuous features. In this article, we investigate the impact of users’ context features and corresponding weights on selecting an optimal price. To apply for multidimensional contextual features and learned corresponding weights, three ellipsoid pricing-based mechanisms are proposed for homogenous and heterogeneous noisy sensing tasks, respectively. What is more, performance analysis are given for satisfying basic properties of crowdsensing. Finally, extensive simulations show that our mechanisms outweigh than existing mechanisms.