Method for establishing a routing path through Q learning on-board network based on fuzzy reasoning

Abstract

The invention relates to a method for establishing a routing path through a Q learning on-board network based on fuzzy reasoning. The method comprises the following specific steps: (1) performing network initialization; (2) sending a greetings data packet in a broadcasting manner; (3) starting to send a request message through a source network node; (4) calculating the channel grade of an intermediate network node; (5) updating a Q value in a routing request data packet; (6) judging whether a current network node s is a destination network node or not, executing the step (7) if yes and otherwise, executing the step (4); (7) establishing positive routing information; (8) judging whether a routing reply data packet reaches the source network node or not, executing the step (9) if yes and otherwise, executing the step (7); (9) sending the data packet. According to the method, the combination of a fuzzy reasoning technology and a routing technology is realized, and the discount rate in a Q learning method is calculated according to the fuzzy reasoning and can be dynamically adjusted according to the network environment condition of the on-board network, so that the speed of establishing on-board network routing is accelerated.

Description

technical field [0001] The invention belongs to the technical field of communication, and further relates to a method for establishing a routing path of a vehicle-mounted network based on fuzzy reasoning Q-learning in a wireless sensor network. The invention uses Q learning to evaluate the node link quality in the vehicle network environment, calculates the fuzzy judgment value of the discount rate in the node Q learning, and is a routing method that can dynamically adapt to the vehicle network. The invention can accelerate the speed of establishing the global optimal routing path. It can be applied to various fields such as vehicle network. Background technique [0002] At present, in the field of wireless sensor network technology represented by fuzzy reasoning, the combination of fuzzy reasoning technology and routing technology is widely used to establish vehicle network routing information and speed up the establishment of global optimal routing paths. [0003] The pa...

AI Technical Summary

Problems solved by technology

However, the shortcomings of this method are: each static node only collects the delay time and road status between the current network node and the neighbor network nodes, and the routing path is established on this basis, and the routing path cannot be established from the global road conditions, so it is difficult to quickly Obtain the global optimal routing path

However, the shortcomings of this method are: because the learning rate and discount rate of the routing model based on Q-learning adopt fixed empirical values, they cannot change with the dynamic changes of the specific network, resulting in slow convergence of the algorithm; The Bayesian network of the link state needs periodic training to adapt to the dynamic changes of the network. If the training period is too large, it cannot accurately reflect the current link status of the vehicle network. In a complex vehicle network environment, it will affect the overall performance of the network.

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