Q learning-based medium access control method for underwater acoustic network with variable number of nodes
A medium access control, underwater acoustic network technology, applied in data exchange networks, complex mathematical operations, climate sustainability, etc., to achieve the effect of maintaining throughput, energy saving, and fast learning
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[0048] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
[0049] 1) Consider a water network, including m = 20 sensor nodes (hereinafter referred to as "nodes") and 1 compliment (hereinafter referred to as "Social"), such as figure 1 Indicated. The node perceive information from the marine environment, and the Subsushi is responsible for collecting node perceived acoustic data.
[0050] The data collection process of the band is divided into n = 20 time slots. To ensure that each node has a time slot to send the data to the content, the number of time slots is equal to the number of water sound network nodes. In the Q learning algorithm, the Q matrix applied to the medium access control is 20 × 20 matrix, the Q matrix row m (M = 1, 2, ..., M) represents the node sequence number, the column N (n = 1) of the Q matrix. 2, ..., n) indicate the slot sequence number. Q (m, n) indicates the Q value corresponding...
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