Adaptive joint computing unloading and resource allocation method in Internet of Vehicles

Abstract

The invention relates to an adaptive joint computing unloading and resource allocation method in Internet of Vehicles, and belongs to the field of Internet of Vehicles edge computing. The method comprises the step of considering the concurrent unloading condition of a plurality of vehicles under the combination of a local computing platform, an MEC server, a far-end cloud server and an idle vehicle multi-computing platform; comprehensively considering the size of a vehicle task, the maximum tolerance time delay, a computing resource under a RSU and a network bandwidth factor, and unloading thecomputing task of the vehicle to a plurality of computing platforms; according to the task number under the RSU, automatically adjusting an unloading platform and an unloading proportion of the vehicle computing task, and distributing computing resources of the MEC while the vehicle obtains the optimal unloading proportion; modeling the calculation unloading decision and the resource allocation into a multi-constraint optimization problem; utilizing a compressed particle swarm optimization algorithm, and proposing a particle matrix coding mode to jointly optimize unloading decision and resource allocation. Experimental results show that the method can minimize the total cost of the system while meeting the maximum tolerance time delay.

Description

technical field [0001] The invention belongs to the field of Internet of Vehicles computing, and relates to an adaptive joint computing unloading and resource allocation method in the Internet of Vehicles. Background technique [0002] As the number of autonomous vehicles in C-V2X increases, various computation-intensive and latency-sensitive applications emerge, such as image-assisted navigation and augmented reality driving, which require massive computing resources for real-time processing and analysis of large amounts of transmitted data. sensory data, which poses a huge challenge for vehicles with limited computing resources. [0003] By offloading computing tasks to other computing nodes, it can effectively solve the problem of limited computing resources in vehicles. The remote cloud server has rich computing resources, but it is far away from the vehicle, which will cause huge transmission delay and energy consumption. The MEC server sinks the calculation to the ro...

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Problems solved by technology

In fact, most vehicles are unloaded concurrently. For MEC servers with limited computing resources, it is difficult to meet the maximum tolerance delay. Therefore, it is necessary to c

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