Nowadays, IoT systems can better satisfy the service requirements of users with effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete information. The state-of-the-art solutions focus on the pricing game between a single service provider and users, which ignoring the competition among multiple edge service providers. To address this challenge, we design an edge-intelligent hierarchical dynamic pricing mechanism based on cloud-edge-client collaboration. We introduce an improved double-layer Stackelberg game model to describe the cloud-edge-client collaboration. Technically, we propose a novel pricing prediction algorithm based on double-label Radius K-nearest Neighbors, thereby reducing the number of invalid games to accelerate the game convergence. The experimental results show that our proposed mechanism effectively improves the quality of service for users and realizes the maximum benefit equilibrium for service providers, compared with the traditional pricing scheme. Our proposed mechanism is highly suitable for the IoT applications (e.g., intelligent agriculture or Internet of Vehicles), where there are multiple competing edge service providers for resource allocation.

The development of smart cities and deep learning technology is changing our physical world to a cyber world. As one of the main applications, the Internet of Vehicles has been developing rapidly. However, privacy leakage and delay problem for data collection remain as the key concerns behind the fast development of the cyber intelligence technologies. If the original data collected are directly uploaded to the cloud for processing, it will bring huge load pressure and delay to the network communication. Moreover, during this process, it will lead to the leakage of data privacy. To this end, in this article we design a data collection and preprocessing scheme based on deep learning, which adopts the semisupervised learning algorithm of data augmentation and label guessing. Data filtering is performed at the edge layer, and a large amount of similar data and irrelevant data are cleared. If the edge device cannot process some complex data independently, it will send the processed and reliable data to the cloud for further processing, which maximizes the protection of user privacy. Our method significantly reduces the amount of data uploaded to the cloud, and meanwhile protects the user's data privacy effectively.