With the development of the Internet of Vehicles (IoV), Parked Vehicle Edge Computing (PVEC) has gradually attracted people's attention. Parked vehicles(PVs) can be exploited as a supplementary computing resource to Mobile Edge Computing (MEC). Using the onboard resources of parked vehicles can effectively improve the security of the blockchainbased Internet of Vehicles system. However, there are multiple MEC nodes belonging to different service providers and multiple parked vehicles in an area. Due to individual rationality, parked vehicles and MEC nodes will not provide services for free. Therefore, in this paper, we study the interaction of MEC nodes and Parked vehicles in blockchain-based parking vehicle edge computing .and model it as a two-stage Stackelberg game to optimize the utility of MEC nodes and Parked vehicles. Specifically, we treat the parked vehicle as a leader, set the price of computing resources, treat the MEC node as a follower, and determine the demand for computing resources through the price of the leader. We use ADMM to optimize their utility function to obtain a computing offloading scheme that maximizes system utility. Simulation results show that our scheme can maximize the utility of MEC nodes and Parked vehicles and maximize social welfare.

Aiming at the problem of poor real-time monitoring performance of the Internet of Things (IoT) card, an online automatic monitoring method based on the optimized time series is designed. Based on the concept of time series, the automatic monitoring function of the life cycle of the Internet of Things card is deduced. On the basis of this function, the online process automatic monitoring model is constructed to shorten the time of data transmission in the monitoring process, to realize the online process real-time monitoring of the life cycle of the Internet of Things card. In this paper, the performance of the design method is analyzed by means of comparative experiments. The data transmission time of this automatic monitoring method is all below 0.40ms, and the transmission time of business statistical analysis is the highest, and reached 2.7ms. Therefore, the method effectively reduces the transmission time of monitoring process data and improves the monitoring efficiency.