Q learning-based medium access control method for underwater acoustic network with variable number of nodes

Abstract

The invention discloses a node number variable underwater acoustic network medium access control method based on Q learning, and relates to an underwater acoustic network. The method comprises the following steps: dividing a transmission process of collecting data sensed by underwater acoustic sensor nodes by aggregation nodes into a plurality of time slots, applying a Q learning algorithm, combining feedback signals of the aggregation nodes and the quantity change condition of the sensor nodes, reasonably setting an award mechanism, and carrying out integral award sub-matrix design on a whole line (namely, a sub-matrix) of a Q matrix, not updating the Q matrix element by element, and reasonably distributing the time slot to each sensor node, so the data is not influenced by other sensor nodes in the transmission process, and the data collection conflict of the aggregation node is avoided. The method provided by the invention has the characteristics of high learning speed, high throughput, low energy consumption and strong anti-interference capability, can solve the problem of time slot redundancy when nodes are reduced or the problem of insufficient time slot when nodes are increased due to node death or position drift, and ensures the success rate of underwater acoustic data transmission and high throughput of an underwater acoustic network.

Description

technical field [0001] The invention relates to an underwater acoustic network, in particular to a Q-learning-based medium access control method for an underwater acoustic network with a variable number of nodes. Background technique [0002] In recent years, the ocean, which occupies 71% of the earth's surface area, has increasingly become the focus of world attention, both in terms of military and civilian use. As an important part of the marine Internet of Things, the underwater acoustic network has gradually become one of the important research hotspots. [0003] The underwater acoustic network is composed of a large number of battery-powered underwater acoustic sensor nodes. However, in the marine environment, it is difficult and costly to replace the batteries of the nodes, and the propagation time of the underwater acoustic channel is prolonged, the channel capacity is small, and the reliability is low. This requires that the data transmission of the underwater acous...

AI Technical Summary

Problems solved by technology

At present, in the research on the combination of Q-learning and medium access control protocols in underwater acoustic networks, how to optimize the design of Q-learning to ensure that the underwater acoustic network maintains high throughput and fast Method for learning speed and strong robustness

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