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Segmented backoff algorithm based on weighted reinforcement learning

A technology of reinforcement learning and backoff algorithm, applied in complex mathematical operations, advanced technology, climate sustainability, etc., can solve problems such as increased probability of collision, increased channel access failures, and unfair competition to access channels.

Active Publication Date: 2021-09-24
JIAXING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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.

Method used

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  • Segmented backoff algorithm based on weighted reinforcement learning
  • Segmented backoff algorithm based on weighted reinforcement learning
  • Segmented backoff algorithm based on weighted reinforcement learning

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Embodiment Construction

[0030] The following description serves to disclose the present invention to enable those skilled in the art to carry out the present invention. The preferred embodiments described below are only examples, and those skilled in the art can devise other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents and other technical solutions without departing from the spirit and scope of the present invention.

[0031] In the preferred embodiment of the present invention, those skilled in the art should note that the first algorithm involved in the present invention, the IEEE 802.15.4 CSMA / CA protocol, etc. can be regarded as prior art.

[0032] preferred embodiment.

[0033] The invention discloses a segmented back-off algorithm based on weighted reinforcement learning, which is used to improve the channel effective utilization rate of a wireless sensor network...

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Abstract

The invention discloses a segmented backoff algorithm based on weighted reinforcement learning, which comprises the following steps: S1, establishing a binary exponential backoff algorithm model, analyzing the condition that the channel effective utilization rate and the data packet loss rate of a wireless sensor network change along with the increase of data traffic in the network, establishing a segmented backoff window, and setting different numbers of nodes to change the data flow condition in the network so as to obtain the channel effective utilization rate of each segment of backoff window under the condition of different numbers of nodes. According to the weighted reinforcement learning-based segmented backoff algorithm disclosed by the invention, the channel access mode of the network access control layer is adjusted through the weighted reinforcement learning model; and therefore, the effective utilization rate of the channel of the wireless sensor network is improved and the packet loss rate is reduced while the fairness of competitive access of the node to the channel is ensured.

Description

technical field [0001] The invention belongs to the technical field of media access control layers of wireless sensor networks, and in particular relates to a segmented back-off 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 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 s...

Claims

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Application Information

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IPC IPC(8): H04W74/08H04W84/18G06F17/18
CPCH04W74/0816H04W84/18G06F17/18Y02D30/70
Inventor 陈丽朱锌成杨俊邓琨尚涛赵竞远陈洁王君壬陈雨豪孙泽成盖博源
Owner JIAXING UNIV
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