Routing method for underwater acoustic network based on information importance and q-learning algorithm

Abstract

The invention discloses an underwater acoustic network routing selection method based on information importance and Q-learning algorithm, relating to underwater acoustic networks. Introduce the importance of information into the multi-diving underwater acoustic sensor network, take the level of information importance as the first priority condition, and take the remaining energy of relay nodes as the second priority condition, and use the Q learning algorithm to select the best route: the level of information importance is high Select a shorter route to ensure that important information is quickly and accurately transmitted to the surface base station; for information with a lower level of information importance, select a relay node with sufficient remaining energy to avoid repeated use of some relay nodes , causing the node to die too fast and energy voids to appear. The number of nodes selected for the Q-learning algorithm is only 1 / 7 of the total number of surviving nodes in the entire network, avoiding the exploration of surviving nodes in the entire network, shortening the range of candidate node sets for the Q-learning iterative algorithm, and reducing the number of explorations required to find the best route. Save algorithm running time, save power consumption of underwater nodes, and extend the life cycle of underwater acoustic networks.

Description

technical field [0001] The invention relates to an underwater acoustic network, in particular to an underwater acoustic network routing selection method based on information importance and Q learning algorithm. Background technique [0002] With the proposal and development of the concept of smart ocean, in order to alleviate the shortage of terrestrial resources, the exploration and development of marine resources using underwater acoustic sensor networks has gradually become an important research direction. [0003] In the harsh marine environment, due to the difficulty and cost of sensor node battery replacement, the energy efficiency of underwater sensor nodes has always been a challenging key issue in the design of underwater acoustic sensor networks. Studies have shown that the technical means of realizing long-distance transmission through multi-hop transmission can reduce the overall energy consumption of the underwater acoustic sensor network system (W. Zhang, et al...

AI Technical Summary

Problems solved by technology

[0004] With the development of machine learning, in view of the advantages of the Q-learning algorithm, Hu et al. (T.Hu, et al. QELAR: AMachine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks[J].IEEE Trans .on MobileComputing, 2010, 9(6): 796-809) used Q-learning algorithm for routing optimization of multi-diving underwater acoustic sensor network, which improved energy efficiency and extended network life, but the corresponding routing nodes would be due to It is frequently selected due to its optimality, which in turn causes the problem of energy holes in the network

Zhang Deqian et al. (Zhang Deqian, et al. A new algorithm for adaptive mobile Internet of Things routing based on Q-Learning strategy [J]. Electronic Journal, 2018, 46(10): 23-30) using Q-learning algorithm for mobile Internet of Things Routing Design Based on Data Importance Rating in Underwater Acoustic Sensor Networks [C].In Proc.of IEEE ICSPCC 2020,Taipa,Macau,China,Aug.21-23,2020) proposed to classify the information importance of underwater acoustic data, and then implement multi-diving underwater acoustic sensor network based on different importance levels route selection, but the selected multi-hop transmission path is longer and there are many dead relay nodes, the present invention will combine Q learning algorithm to effectively solve these problems

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