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A multi-auv collaborative data collection method based on q-learning in uasns

A data collection and node technology, which is applied to services, network topology, electrical components, etc. based on specific environments, can solve problems such as big data collection delays, and achieve the effects of reducing energy consumption, reducing cruise paths, and assigning tasks reasonably and efficiently.

Active Publication Date: 2022-07-15
HOHAI UNIV CHANGZHOU
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  • Claims
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AI Technical Summary

Problems solved by technology

[0009] Using a single AUV for data collection is suitable for small-scale networks. When the network scale increases, it is easy to generate large data collection delays.

Method used

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  • A multi-auv collaborative data collection method based on q-learning in uasns
  • A multi-auv collaborative data collection method based on q-learning in uasns
  • A multi-auv collaborative data collection method based on q-learning in uasns

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Embodiment Construction

[0048] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0049] A Q-learning-based multi-AUV collaborative data collection method in UASNs, including the following steps:

[0050] Step 1: Node clustering

[0051] like figure 1 As shown in the figure, in the underwater wireless sensor network, the nodes are randomly deployed, and the cluster head node is selected from these nodes according to the selection rules, which is responsible for collecting and integrating the data of the nodes in the cluster; after the selection of the cluster head, it does not become the cluster head The nodes that receive the message will receive declaration messages from different cluster heads, and the nodes that receive the messages will send a join message to the nearest cluster head to join the nearest cluster head to form different node clusters;

[0052] Step 2: AUV task assignment

[0053] like figure 2 As shown in the fi...

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Abstract

The invention discloses a Q-learning-based multi-AUV collaborative data collection method in UASNs, comprising the following steps: selecting a cluster head according to certain conditions, other nodes adaptively join the cluster head nearby to form a node cluster; AUV task assignment; path planning is performed based on the Q-learning algorithm, and AUV completes data collection according to the planned path. The invention improves the task completion efficiency of the AUVs and reduces the data collection delay by reasonably assigning tasks to multiple AUVs; the information level of the data packets is considered during data collection, and the emergency data is preferentially collected to realize the urgent data collection. Fast and efficient processing; by using Q‑learning for AUV path planning, the AUV’s sailing distance and energy consumption are reduced.

Description

technical field [0001] The invention belongs to the field of underwater acoustic sensor networks, in particular to a Q-learning-based multi-AUV collaborative data collection method in UASNs. Background technique [0002] Underwater acoustic sensor network is an emerging and promising network technology, which can be widely used in underwater applications, such as underwater environment observation, coastline monitoring and protection, disaster prevention, aided navigation and mine detection. In recent years, the underwater wireless sensor network has attracted more and more attention of marine researchers because of its wide application and many advantages. It can help humans perceive and monitor the vast undetected marine environment, and monitor and warn marine disasters. The occurrence of marine resources provides important information and support for the exploration, utilization and protection of marine resources. [0003] As one of the foundations of UASNs, data collec...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W4/38H04W84/18H04W40/32H04L45/00H04L47/2425H04B13/02
CPCH04W4/38H04W84/18H04W40/32H04L45/46H04L47/2433H04B13/02
Inventor 韩光洁宫爱妮王皓何宇
Owner HOHAI UNIV CHANGZHOU
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