Task Price Prediction Based on Clustering and DNN in Crowdsensing

With the popularization of mobile devices and the development of wireless networks, crowdsensing is devoted to providing universal Internet of Things services. A reasonable task pricing mechanism can not only motivate more users to participate in the sensing task but also help the benign development of crowdsensing platform, so it has gradually become a research hotspot in the field of crowdsensing. Aiming at the common problems of insufficient analysis of task pricing rules and large deviations of pricing prediction models, a task price prediction method based on clustering and DNN is proposed. Using the real historical trade price set as the data source, natural grouping and taxonomic description of task price are realized by exploring sensing task pricing law with complex constraint relation using two-step clustering analysis. On the basis of the above, the price interval prediction model based on DNN is implemented. The experimental results show that the predicting accuracy of the pricing mechanism is higher than 82.7%.