A Segmented Backoff Algorithm Based on Weighted Reinforcement Learning

Abstract

The invention discloses a segmented backoff algorithm based on weighted reinforcement learning, comprising step S1: establishing a binary exponential backoff algorithm model, and analyzing the channel effective utilization rate and data packet loss rate of a wireless sensor network with the increase of data traffic in the network If the situation changes, a segmented backoff window is established, and different node numbers are set to change the data flow in the network, so as to obtain the channel effective utilization of each segment backoff window under different node numbers. The invention discloses a segmented backoff algorithm based on weighted reinforcement learning, which adjusts the channel access mode of the network access control control layer through the weighted reinforcement learning model, so as to ensure the fairness of nodes competing for access channels At the same time, it can improve the effective utilization of the wireless sensor network channel and reduce the packet loss rate.

Description

technical field [0001] The invention belongs to the technical field of a wireless sensor network media access control layer, and in particular relates to a segmented backoff algorithm based on weighted reinforcement learning. Background technique [0002] In a wireless sensor network, each sensor node transmits its own data by competing for access to a shared channel. With the expansion of the network and the increase in data traffic, the number of nodes competing for the channel increases, and the failure of channel access due to conflicts increases. ; In addition, under the framework of binary exponential backoff algorithm, with the increase of data traffic, the number of collisions generated by nodes in the network due to competing for access channels increases, and after each collision, the collided nodes will be in [0, 2^(minBE+X)-1] (minBE is the minimum back-off index size, x is the number of collisions), a back-off time is randomly generated, but the back-off time is...

AI Technical Summary

Problems solved by technology

[0002] In a wireless sensor network, each sensor node transmits its own data by competing for access to a shared channel. With the expansion of the network and the increase in data traffic, the number of nodes competing for the channel increases, and the number of channel access failures due to conflicts increases. ; In addition, under the framework of binary exponential backoff algorithm, as the data flow increases, the number of collisions of nodes in the network due to competition for access channels increases, and after each collision, the collided nodes will be in [0, 2^(minBE+X)-1] (minBE is the smallest backoff index size, x is the number of collisions) randomly generates a backoff time, but if the backoff time is too small, it will increase the probability of collision and increase the node access channel When collision occurs, the possibility of data transmission failure will affect the effective utilization of the channel

[0003] And the existing backoff algorithm in IEEE 802.15.4 often has a smaller backoff window for nodes that have just transmitted data when nodes compete for access to the channel, so there is a greater probability of continuing to seize the channel. For other nodes Competition for access to channels can be unfair

However, the previous reinforcement learning algorithms did not provide convergence and action bias for the algorithm in solving this problem.

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