Underwater acoustic network routing method based on information importance and Q learning algorithm

Abstract

The invention discloses an underwater acoustic network routing method based on information importance and a Q learning algorithm, and relates to an underwater acoustic network. The method comprises the following steps: introducing information importance into a multi-hop underwater acoustic sensor network, taking an information importance level as a first priority condition, taking relay node residual energy as a second priority condition, and selecting an optimal route by using a Q learning algorithm: selecting a shorter route for information with a high information importance level, and ensuring that important information is quickly and accurately transmitted to a water surface base station; for the information with low information importance level, selecting the relay node with sufficient residual energy, so the situation that due to the fact that some relay nodes are repeatedly utilized for multiple times, node death is too fast, and energy holes occur is avoided. The number of nodes selected for the Q learning algorithm is only 1 / 7 of the total number of survival nodes of the whole network, exploration of the survival nodes of the whole network is avoided, the candidate node set range of the Q learning iterative algorithm is shortened, the number of times of exploration needed for finding the optimal route is reduced, the algorithm operation time is saved, the underwater node power consumption is saved, and the life cycle of the underwater acoustic network is prolonged.

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...

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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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