5G Internet of Vehicles partial calculation unloading method

Abstract

The invention discloses a 5G Internet of Vehicles partial calculation unloading method. According to the method, the interests of the operators, the incentive compatibility of the users and the personal rationality are cooperatively considered, the transmission overhead and the calculation overhead of calculation unloading are modeled, and the optimization problem is described by taking the maximization of the overall utility of the operators and the users as the target. The optimization problem is divided into three sub-problems. In order to minimize the transmission delay overhead, an optimal channel allocation strategy is solved by using a bilateral matching algorithm. And the optimal unloading ratio is obtained through convex optimization. And finally, a non-cooperative game is constructed to determine edge computing service pricing, the pricing mechanism ensures personal interests of the users while maximizing the overall utility, and balance between the users and operators is realized. The invention provides a new method for partial calculation unloading under the 5G Internet of Vehicles.

Description

technical field [0001] The present invention relates to partial computing offloading of 5G Internet of Vehicles, in particular to channel allocation, task scheduling and edge computing service pricing mechanism under computing offloading. Background technique [0002] The 5G Internet of Vehicles is expected to connect billions of cars in the future, and based on this, various novel applications can be developed, such as interactive games and augmented reality, to provide passengers with satisfactory network services. In order to attract users' interest, new mobile applications are rich and vivid, but also consume a lot of computing resources and power, which poses a great challenge to resource-constrained vehicles. The emergence of Mobile Edge Computing (MEC) enables users to greatly reduce task execution delay and energy consumption by offloading computing tasks to edge servers, thereby enjoying extremely low-latency network services. However, limited MEC resources are dif...

AI Technical Summary

Problems solved by technology

However, limited MEC resources are difficult to fully satisfy the excessive number of users, so efficient resource allocation and task scheduling strategies, as well as the corresponding MEC service pricing mechanism need to be further explored by researchers.

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