Real-time task allocation method based on vehicle fog computing

Abstract

The invention provides a real-time task allocation method based on VFC (Vehicle Fog Computing), and the method comprises the steps: dividing a VFC system into a plurality of subsystems, selecting onesubsystem, enabling a vehicle needing resources to serve as a client vehicle, taking vehicles using a dedicated short-range communication (DSRC) protocol and Long Term Evolution (LTE) technology, particularly low-speed traveling and parked vehicles, as vehicle networking fog nodes, and taking LTE base stations as regional heads to manage and coordinate all fog nodes; and enabling the regional header to calculate a linear strategy quotation function for achieving maximization of expected utility of two transaction parties by utilizing a two-way auction incentive mechanism under an incomplete information static game, match buyers and sellers meeting conditions to perform task allocation, and calculating transaction prices. Therefore, the requirement for low time delay of the Internet of Vehicles is met, and the mobile fog nodes are stimulated and stimulated through the mechanism to complete tasks more actively, continuously and stably.

Description

technical field [0001] The present invention relates to the technical field of mobile communication, in particular to a real-time task assignment method based on fog computing of connected vehicles Background technique [0002] With the development of intelligent transportation, new vehicle applications such as autonomous driving, intelligent vehicle interaction, and safety warnings continue to emerge. On the one hand, vehicle terminals have low data processing efficiency due to their limited resources, including computing resources, storage resources, and bandwidth resources. , it is difficult to adapt to the needs of in-vehicle multimedia applications, in-vehicle social networks, and location-based services; on the other hand, intelligent transportation systems contain a large amount of data, and the amount of data processed by in-vehicle terminals is large and slow. Therefore, the industry needs a strong support platform to analyze process these traffic data. Although cl...

AI Technical Summary

Problems solved by technology

[0002] With the development of intelligent transportation, new vehicle applications such as autonomous driving, intelligent vehicle interaction, and safety warnings continue to emerge. On the one hand, vehicle terminals have low data processing efficiency due to their limited resources, including computing resources, storage resources, and bandwidth resources. , it is difficult to adapt to the needs of in-vehicle multimedia applications, in-vehicle social networks, and location-based services; on the other hand, intelligent transportation systems contain a large amount of data, and the amount of data processed by in-vehicle terminals is large and slow. Therefore, the industry needs a strong support platform to analyze process the traffic data

Although cloud computing has powerful computing capabilities, it needs to face problems such as network congestion, large network delay, and high operating costs when cloud computing centrally processes a large amount of data, and when vehicles move at high speed, the network topology changes dynamically. , showing an unstable state of interruption and connection, which affects the continuity of data transmission and increases the delay of message delivery, which limits the large-scale application of networks in mobile scenarios such as Internet of Vehicles and wireless access networks.

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