The introduction of edge computing (EC) in intelligent driving allows the vehicle to offload tasks to the EC server closer to the vehicle side, creating a new paradigm for task offloading and resource allocation. The movement of the vehicle, the time sensitivity of the processing data, and the resource allocation of the EC server have become bottlenecks of the rapid development of intelligent driving. In this paper, we jointly considered the problems of the network economy and resource allocation. In order to eliminate dependence on third parties, we propose a resource transaction architecture based on the blockchain. Moreover, we propose the Dynamic Allocation algorithm of Edge Resources (DAER) based on the double auction mechanism to maximize the satisfaction of users and Service Providers of edge computing (SPs), where the DAER algorithm is implemented in the form of smart contracts in the blockchain architecture. In particular, we propose the state search algorithm that can improve the prediction accuracy of the staged destination of the vehicle to help to allocate resources reasonably. Through simulation experiments, we verify the superior performance of the DAER algorithm in terms of resource utilization rate and the satisfaction of both parties participating in the auction.

In the Industrial Internet of Things (IIoT), model and computing power sharing among devices can improve resource utilization and work efficiency. However, data privacy and security issues hinder the sharing process. Besides, in the process of model sharing, due to the customization of industrial equipment functions and the high separation of model and task types between devices, it is difficult to share model and optimize models among devices with different task requirements. In this article, we propose an adaptively federated multitask learning (AFL) for IIoT devices efficiently model sharing. Inspired by the parameter sharing mechanism, AFL builds a sparse sharing structure by designing an iterative pruning network and generating subnets for each task. Moreover, for better share relevant information, we further propose tailored task mask layers for effectively training specialized subnets, and an adaptive loss function to dynamically adjust the priority between tasks. Extensive experiments show that AFL can successfully fit hundreds of tasks from different devices into one model, which preserves both high accuracy and system scalability, and outperforms other related approaches that naively combine federated learning with multitask learning.