Hierarchical partition security routing method and system for dense medium directional transceiving network

Abstract

The application provides a hierarchical partition security routing method and system of a dense medium directional transceiving network, and relates to the technical field of optical communication. The method comprises the following steps: constructing an optical node hierarchical deployment structure based on geographical layering according to water depth; initializing the optical node hierarchical deployment structure to obtain an initialized topological structure according to chaotic mapping optimization of the network; performing acoustic communication and acoustic print information identification on the communication nodes in the same layer and the AUV auxiliary nodes in adjacent layers to realize identity recognition and safe access of the communication nodes in the same layer, and obtaining an acoustic network, wherein the acoustic network comprises legal cross-layer relay nodes; determining the data relay of a single node in multi-beam transmission according to a plurality of links corresponding to the legal cross-layer relay nodes to obtain an optical network. In this way, the communication nodes are easy to be found and will not be interrupted; the data relay of multi-beam transmission can be easily performed, the deployment selection of relay nodes with high speed and high coverage rate can be realized in a complex deployment area, and the bandwidth utilization rate is high.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical communication, in particular to a layered division security routing method and system of a dense medium directional transceiving network. BACKGROUND

[0002] As a high-efficiency communication means, underwater optical networks have higher bandwidth and lower delay characteristics compared to acoustic communication, thus showing broad application prospects in underwater monitoring, ocean exploration, and national defense. However, due to the limited propagation angle of optical signals, their propagation in water is affected by complex environmental factors such as refraction, scattering, and attenuation. Therefore, how to reasonably deploy nodes and optimize communication protocols has become a research focus, and existing research is just getting started, with a lot of technical gaps. In this context, the use of highly directional light beams for multi-path propagation in the vertical and horizontal planes can greatly improve communication efficiency. Meanwhile, the deployment of underwater network nodes needs to consider uniform coverage, randomness, and dynamic adaptability. Through the combination of these technologies, the node layout and routing performance of underwater optical networks can be optimized, improving their stability and robustness in complex environments.

[0003] Currently, the existing technologies for underwater network research are regular grid deployment and random distribution optimization. However, the routing protocols of the above-mentioned existing underwater networks usually rely on acoustic channels or assume uniform node distribution, which does not fully utilize the multi-beam, multi-path, and large flux characteristics of optical communication, resulting in low bandwidth utilization. Node discovery is difficult, leading to frequent interruptions in node communication due to alignment problems. Considering single transceiving link quality makes it difficult to select relay nodes. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a layered division security routing method and system of a dense medium directional transceiving network, which solves the problems of low bandwidth utilization, difficulty in node discovery, frequent interruptions in node communication due to alignment problems, and difficulty in selecting relay nodes.

[0005] To solve the above technical problems, the embodiments of the present application provide the following technical solutions:

[0006] The first aspect of the present application provides a layered division security routing method of a dense medium directional transceiving network, comprising:

[0007] constructing an optical node layered deployment structure based on geographical layering according to water depth, the optical node layered deployment structure comprising a buoy node, a source node, a communication node, and an AUV auxiliary node;

[0008] optimizing the network according to chaotic mapping, initializing the optical node layered deployment structure to obtain a corresponding initialized topology structure, the initialized topology structure comprising communication nodes in the same layer and AUV auxiliary nodes in adjacent layers;

[0009] The communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer are subjected to acoustic communication and acoustic print information identification, so that the identity of the communication nodes in the same layer is identified and the communication nodes are safely accessed, and an acoustic network is obtained, wherein the acoustic network comprises legal cross-layer relay nodes.

[0010] According to the multiple links corresponding to the legal cross-layer relay nodes, the data relay of a single node in multi-beam transmission is determined, so as to obtain an optical network.

[0011] The second aspect of the present application provides a layered division security routing system of a dense medium directional transceiving network, comprising:

[0012] The construction module is configured to construct an optical node layered deployment structure based on geographical layering according to water depth, wherein the optical node layered deployment structure comprises a buoy node, a source node, a communication node and an AUV auxiliary node.

[0013] The initialization module is configured to initialize the optical node layered deployment structure according to chaotic mapping optimization network, so as to obtain a corresponding initialized topological structure, wherein the initialized topological structure comprises the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer.

[0014] The identification module is configured to subject the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer to acoustic communication and acoustic print information identification, so that the identity of the communication nodes in the same layer is identified and the communication nodes are safely accessed, and an acoustic network is obtained, wherein the acoustic network comprises legal cross-layer relay nodes.

[0015] The determination module is configured to determine the data relay of a single node in multi-beam transmission according to the multiple links corresponding to the legal cross-layer relay nodes, so as to obtain an optical network.

[0016] Compared with the prior art, the hierarchical division security routing method and system of the dense medium directional transceiving network provided by the application, according to water depth, constructs a light node hierarchical deployment structure based on geographical layering; according to chaotic mapping optimization network, the light node hierarchical deployment structure is initialized to obtain the corresponding initialized topological structure; the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layers are subjected to acoustic communication and acoustic print information identification to realize the identity recognition and safe access of the communication nodes in the same layer, and an acoustic network is obtained, the acoustic network including legal cross-layer relay nodes; according to the multiple links corresponding to the legal cross-layer relay nodes, the data relay of a single node in multi-beam transmission is determined to obtain an optical network. In this way, by utilizing the pseudo-random characteristics of chaotic mapping optimization network, high coverage rate distribution of communication nodes can be realized in the same layer, while certain random characteristics are retained to adapt to the dynamic changes of the underwater environment, so that the communication nodes are easier to find and will not be interrupted; considering acoustic communication and acoustic print information identification and the multiple links corresponding to the legal cross-layer relay nodes, data relay of multi-beam transmission can be more easily performed, and rapid and high-coverage relay node deployment selection can be realized in a complex deployment area; the multiple links corresponding to the legal cross-layer relay nodes can be fully utilized, so that the bandwidth utilization rate is higher. BRIEF DESCRIPTION OF DRAWINGS

[0017] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which a number of embodiments of the application are shown by way of example, and wherein like reference numerals refer to like elements throughout. In the drawings:

[0018] Figure 1 A flow chart of the hierarchical division security routing method of the dense medium directional transceiving network is schematically shown;

[0019] Figure 2 A schematic diagram of the light node hierarchical deployment structure based on geographical layering is schematically shown;

[0020] Figure 3 A structure diagram of the hierarchical division security routing system of the dense medium directional transceiving network is schematically shown. DETAILED DESCRIPTION

[0021] Exemplary embodiments of the present application will be described herein below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thoroughly and completely understood, and so that the scope of the present application will be completely conveyed to those skilled in the art.

[0022] It should be noted that: unless otherwise specified, the technical terms or scientific terms used in the present application should be the usual meaning understood by the skilled person in the field to which the present application belongs.

[0023] The method in the embodiment of the present application is described in detail below.

[0024] Figure 1 The flow chart of the hierarchical division security routing method for the dense medium directional transceiving network in the embodiment of the present application is schematically shown, referring to Figure 1 As shown in the figure, the hierarchical division security routing method for the dense medium directional transceiving network can include:

[0025] S101, constructing a light node hierarchical deployment structure based on geographical layering according to water depth.

[0026] The light node hierarchical deployment structure includes a buoy node, a source node, a communication node and an autonomous underwater vehicle (AUV) auxiliary node.

[0027] The light node hierarchical deployment structure of the present application is based on geographical layering. In the underwater environment, due to the poor effect of commonly used positioning technologies such as GPS, relative position positioning of communication nodes, etc., it is greatly affected by water flow. At present, the vertical direction recognition technology is relatively mature, and the most commonly used technology is the vertical direction recognition based on water pressure difference, which makes the communication node have a certain basis for vertical direction hierarchical deployment and light vertical pointing implementation. Therefore, in order to improve the applicability of actual deployment, the present application proposes a light node hierarchical deployment structure based on geographical layering.

[0028] Figure 2 The schematic diagram of the light node hierarchical deployment structure based on geographical layering is schematically shown, referring to Figure 2 As shown in the figure, the hierarchical division security routing system for the dense medium directional transceiving network includes a light node hierarchical deployment structure, and the light node hierarchical deployment structure includes a buoy node, a source node, a communication node and an AUV auxiliary node. The source node collects the probe signal and transmits it to the buoy node, and the buoy node transmits it to the target base station through electromagnetic communication or the like. At the same time, the buoy node transmits to the source node and the relay node through underwater light beams and sends command data. Except that the light beam direction of the communication node in the first layer points to the buoy node, the light beams of the remaining communication nodes are vertically upward and downward, which effectively improves the data transmission efficiency.

[0029] The communication nodes are layered according to water depth, Figure 2 In the middle, the communication nodes are divided into the first layer, i.e. layer one, the second layer, i.e. layer two, and the third layer, i.e. layer three, Figure 2The system also includes a water surface; given the difficulty of underwater positioning, water depth stratification effectively manages communication nodes, while enabling two-dimensional management of communication nodes on the same level, reducing complexity. It assists AUV auxiliary nodes in navigating between different levels. Communication nodes on the same level, as well as communication nodes and AUV auxiliary nodes, communicate with each other using an acoustic network. Data between communication nodes on the same level includes node location, node communication support commands, and node load. Non-relayed nodes on the same level serve as backup nodes, filling gaps in bandwidth or when the original relay node becomes inactive. The layered deployment structure of optical nodes utilizes the similarity of water density within the same level to reduce acoustic signal distortion, while also avoiding node voids and facilitating the achievement of the expected hop count in the vertical direction, effectively preventing node communication loops. Backup nodes and relay nodes on the same level record acoustic fingerprint information to achieve secure access and directional mobility assistance.

[0030] A hierarchical deployment structure for optical nodes with reduced pointing is proposed, which is suitable for underwater optical transmission and reception angle-limited scenarios. The aim is to reduce routing node holes and reduce the impact of alignment problems on routing performance.

[0031] S102. Optimize the network based on chaotic mapping, initialize the hierarchical deployment structure of optical nodes, and obtain the corresponding initialized topology.

[0032] The initial topology includes communication nodes on the same layer and AUV auxiliary nodes on adjacent layers.

[0033] Initializing the hierarchical deployment structure of optical nodes includes both hierarchical node deployment and intra-layer node initialization. The initialization of the hierarchical deployment structure primarily assists in node location initialization, aiming to achieve effective distribution of communication nodes after hierarchical arrangement, improve the effective coverage of communication nodes deployed on the same level, and reduce communication node overlap and collisions. A hierarchical network design based on chaotic mapping is also implemented.

[0034] The basic idea of ​​chaotic mapping optimization networks is to linearly map the optimization variables to chaotic variables through chaotic mapping, then perform optimization search based on the ergodicity and randomness of chaos, and finally linearly transform the obtained solution into the optimization variable space. An underwater layered network based on Tent chaotic mapping is adopted.

[0035] Specifically, based on the chaotic mapping optimization network, the hierarchical deployment structure of optical nodes is initialized to obtain the corresponding initialized topology, including:

[0036] Step A1: Based on the water depth, divide the communication nodes into layers and obtain the initial vector value corresponding to the first communication node in each layer.

[0037] The layering is equivalent to D dimensions, and step A1 can also be said that in the D-dimensional space underwater, the N communication nodes are composed of D-dimensional individuals, a D-dimensional vector initial value Coord0 is randomly generated, and Coord0 is taken as the chaotic variable of the first node, that is, the vector initial value corresponding to the first communication node of each layer is generated.

[0038] Step A2: generating target chaotic variables of each layer according to the vector initial value and the Tent chaotic mapping formula.

[0039] Among them, the target chaotic variables of each layer are the chaotic variables corresponding to the communication nodes other than the first communication node.

[0040] For each dimension of the vector initial value Coord0 in step A1, the Tent chaotic mapping formula is iterated and calculated in sequence to generate chaotic variables corresponding to the remaining N-1 communication nodes.

[0041] Step A3: mapping the target chaotic variables of each layer into the underwater flat layer transmission space to obtain target initialization position coordinates to obtain the initialized topology.

[0042] Among them, the target initialization position coordinates are the initialization position coordinates of all communication nodes in the same flat layer space.

[0043] Specifically, the chaotic variables corresponding to the remaining N-1 communication nodes are mapped into the underwater flat layer transmission space according to the following formula to obtain the initialization position coordinates COORDk=(Xk,Yk) of the N communication nodes in the same flat layer space, that is, the target initialization position coordinates, to obtain the initialized topology:

[0044]

[0045] Among them, Xk is the first coordinate corresponding to each index k calculated by the Tent mapping chaotic mapping formula, Yk is the second coordinate corresponding to each index k calculated by the Tent mapping chaotic mapping formula, xk is a scaling value between 0 and 1, Uk is the upper limit corresponding to each index k, Lk is the lower limit corresponding to each index k, and k is the index, that is, the serial number of the communication node.

[0046] After the Tent chaotic mapping optimization, the communication node distribution is relatively uniform, the communication node overlap coverage is relatively small, and the search efficiency of the communication node is improved.

[0047] S103, acoustic communication and voiceprint information identification are performed on the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer to realize identity recognition and safe access of the communication nodes in the same layer, and an acoustic network is obtained.

[0048] The acoustic network includes a legal cross-layer relay node.

[0049] After the initialization, the voiceprint-based identity recognition is proposed for secure access and layer assistance, so as to avoid the invalid communication nodes in adjacent layers as relay transmission in cross-layer transmission, resulting in link interruption or bandwidth limitation. Through acoustic communication and voiceprint information recognition, the position positioning and service support capability between communication nodes can be improved, and the system survivability can be improved.

[0050] Before the acoustic network is obtained by performing acoustic communication and voiceprint information recognition on the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer to realize identity recognition and secure access of the communication nodes in the same layer, the method comprises the following steps:

[0051] The acoustic signals corresponding to the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer are denoised and enhanced to obtain filtered signals.

[0052] The Wiener filter can be selected for denoising, and the expression is as follows:

[0053]

[0054] Wherein, X(τ) is the filtered signal, S XX (f) is the signal power spectrum, S NN (f) is the noise power spectrum, x(τ) is the acoustic signal corresponding to the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer, f is the frequency component of analysis, and τ is the integral variable.

[0055] Specifically, the acoustic communication and voiceprint information recognition are performed on the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer to realize identity recognition and secure access of the communication nodes in the same layer and obtain the acoustic network, comprising the following steps:

[0056] Step B1: The filtered signal is converted to the frequency domain by using short-time Fourier transform to obtain time-frequency information.

[0057] The expression of the short-time Fourier transform is as follows:

[0058]

[0059] Wherein, X(t,f) is the time-frequency information, t is the current analysis time point, f is the frequency component of analysis, X(τ) is the filtered signal, h(t-τ) is the window function, and τ is the integral variable.

[0060] Step B2: The time-frequency information is mapped to the mel frequency scale to obtain a mel spectrum.

[0061] The mel frequency scale is defined as:

[0062]

[0063] wherein f M el is the mel-spectrogram, and f' is the frequency value of the filtered signal.

[0064] Step B3: inputting the mel-spectrogram into the neural convolutional network to make the neural convolutional network output the local feature corresponding to the acoustic signal.

[0065] wherein the neural convolutional network comprises an input layer, a first convolutional layer, a first pooling layer, a second convolutional layer, a second pooling layer, a third convolutional layer, a third pooling layer, a flattening layer, a full connection layer, a random inactivation layer and an output layer connected in sequence.

[0066] Specifically, inputting the mel-spectrogram into the neural convolutional network to make the neural convolutional network output the local feature corresponding to the acoustic signal comprises:

[0067] Step B31: inputting the mel-spectrogram into the input layer and the first convolutional layer in sequence to make the first convolutional layer output the ground local feature in the mel-spectrogram.

[0068] The shape of the mel-spectrogram can be described as:

[0069] Input Size=(H, W, C);

[0070] wherein Input Size is the shape of the mel-spectrogram, H is the spectral height of the mel-spectrogram, indicating the frequency dimension, which can be 128 mel-spectrogram frequency dimensions, W is the frequency width of the mel-spectrogram, indicating the time dimension, and C is the number of channels, which should be 1 for a single mel-spectrogram.

[0071] The first convolutional layer has 64 convolutional kernels with a kernel size of 3*3 and a step size of 1, and the activation function is ReLU nonlinear activation. The size of the ground local feature in the mel-spectrogram output after convolution is 129*100*64.

[0072] Step B32: inputting the ground local feature into the first pooling layer, the second convolutional layer, the second pooling layer, the third convolutional layer and the third pooling layer in sequence to make the third pooling layer output the first intermediate feature.

[0073] The first pooling layer reduces the size of the convolutional feature map and reduces the amount of calculation. A 2*2 window is used, the pooling method is maximum pooling, the step size is 2, and the output is reduced to 64*50*64.

[0074] The second convolutional layer extracts high-level features of the voiceprint information. The convolutional kernel is 128, the convolutional kernel is 3*3, the step size is 1, the padding is the same (same), the activation function is ReLU, and the output size is 64*50*128.

[0075] The second pooling layer has the same parameters as the first pooling layer, and limits the output size to 32*25*128.

[0076] The third convolutional layer and the third pooling layer are used to output a first intermediate feature, which is 16*12*256 data.

[0077] In step B33, the first intermediate feature is sequentially input into a flattening layer and a fully connected layer, so that the fully connected layer outputs a second intermediate feature.

[0078] The first intermediate feature is input into the flattening layer, and the flattening layer is used as the input of the fully connected layer. After flattening, it is a 4912 one-dimensional vector. The data output by the flattening layer is input into the fully connected layer, so that the fully connected layer outputs the second intermediate feature.

[0079] In step B34, the second intermediate feature is mapped into a classification space to obtain a third intermediate feature.

[0080] The second intermediate feature is mapped into a classification space to obtain a third intermediate feature. The number of neurons is assumed to be 512, the activation function is ReLU, the input size is 4912, and the output size is 512. The expression for mapping into the classification space is:

[0081] y=Wx+b;

[0082] Where y is the third intermediate feature, W is the weight matrix, x is the input vector, i.e., the second intermediate feature, and b is the bias.

[0083] In step B35, the third intermediate feature is input into a random inactivation layer, so that the random inactivation layer outputs a fourth intermediate feature.

[0084] The random inactivation layer is used to prevent overfitting and improve the generalization ability of the model by randomly discarding part of the neurons. The dropout rate is set to 0.5.

[0085] In step B36, the fourth intermediate feature is input into an output layer to classify the fourth intermediate feature using the output layer, and output the local feature corresponding to the acoustic signal.

[0086] The output layer performs classification. The number of neurons is consistent with the sum of the number of communication nodes that need to be selected as candidates and the number of AUV auxiliary nodes. The activation function is Softmax, the input size is 512, and the output is the sum of the number of other communication nodes connected to the communication node and the number of AUV auxiliary nodes.

[0087] In step B4, the local feature is used to identify the identity of the adjacent communication node to obtain an acoustic network.

[0088] The acoustic network includes a legal relay node.

[0089] According to the local features, the identities of adjacent communication nodes can be identified, an acoustic network including legal relay nodes is obtained, the accuracy of geographic positioning is further improved, legal nodes are recorded, illegal access is prevented, and system security is improved.

[0090] In S104, data relaying of a single node in multi-beam transmission is determined according to the multiple links corresponding to the legal cross-layer relay nodes, so as to obtain an optical network.

[0091] The identity verification between communication nodes is realized through the initial deployment stage and the voiceprint recognition technology, the alignment difficulty is significantly reduced, and the reliability of geographic location identification and security identity verification is ensured. In addition, the application further proposes an optical multi-beam routing protocol of a hierarchical division security routing system suitable for a dense medium directional transceiving network, which fully utilizes the spatial multiplexing gain provided by a high-throughput multi-beam optical communication system. A plurality of routes are constructed by selecting appropriate auxiliary nodes near the main path to enhance the transmission efficiency and reliability of the network.

[0092] Since each communication node can transmit data through multi-beam transmission, it has more available links, and the selection of the communication node needs to consider the connection of multiple communication nodes in the upper and lower layers, so it is difficult to select the relay node by considering the link quality of single transceiving according to the tradition. Therefore, the application mainly realizes the selection of the relay node by abstracting the pipe quality of routing through multi-beam communication, and uses the pipe quality as the selection of the number of links to be expanded.

[0093] The B beams of each node will be organized into a pipe around the main transmission path, without considering the mobile support capability of the AUV auxiliary node, only considering that the communication node establishes the main transmission and the next hop candidate communication node connection by using the multi-beam characteristics. For the case that the lower layer communication node has no multiple candidate communication nodes for the upper layer communication node, the load of the link node is easy to be too high, which not only reduces the overall life of the network, but also easily causes the communication node to break and cause the interruption of multi-beam transmission of the adjacent layer. That is, the communication node needs to comprehensively consider the number of upper and lower layer communication nodes connected by the node, the remaining life of the communication node, the link signal-to-noise ratio, etc. Therefore, there is the following expression of the pipe quality in step C1.

[0094] Before determining the data relaying of a single node in multi-beam transmission according to the multiple links corresponding to the legal cross-layer relay nodes to obtain an optical network, the method further comprises:

[0095] The multiple links corresponding to the legal cross-layer relay nodes are obtained according to the beams.

[0096] Specifically, the data relaying of a single node in multi-beam transmission is determined according to the multiple links corresponding to the legal cross-layer relay nodes to obtain an optical network, comprising:

[0097] Step C1: determining multiple pipe qualities corresponding to multiple links according to a link signal-to-noise ratio, a bit error rate of data transmission of the communication node, a transmission bandwidth of the optical channel, and a remaining life of the communication node.

[0098] The multiple pipe qualities include a highest pipe quality.

[0099] When the communication node forms a fence-shaped pipe between different layers through multiple beams, the sum of the weights of the multiple beam links can be used as a performance indicator for overall description, which can take into account the number of available paths of the pipe and comprehensively consider the link quality and the remaining energy of the node. To reduce the complexity of the algorithm, the optical link quality of a single link node can be obtained by weighting, and the expression of the multiple pipe qualities corresponding to the multiple links is as follows:

[0100]

[0101] LQ = w1*SNR + w2* (SNR / SNRmax) + w3* (BER / BERmax) + w4* (C / Cmax) + w4*Er / Er,max wherein LQ is the multiple pipe qualities corresponding to the multiple links, w1 is a first weight, SNR is the link signal-to-noise ratio, SNRmax is the maximum link signal-to-noise ratio, w2 is a second weight, BER is the bit error rate of data transmission of the communication node, BERmax is the maximum bit error rate of data transmission of the communication node, w3 is a third weight, C is the transmission bandwidth of the optical channel, Cmax is the maximum transmission bandwidth of the optical channel, w4 is a fourth weight, and Er is the remaining life of the communication node, and Er,max is the maximum remaining life of the communication node.

[0102] The specific value of the link signal-to-noise ratio is related to the water body, turbidity, flow rate, and the like. The maximum link signal-to-noise ratio SNRmax, the maximum bit error rate of data transmission of the communication node BERmax, and the maximum transmission bandwidth of the optical channel Cmax can be used for normalization.

[0103] The pipe quality of a certain communication node transmitted through multiple beams can be expressed as the sum of the multiple pipe qualities corresponding to each link. This achieves single-node selection in multiple beam transmission.

[0104] Step C2: taking the highest pipe quality as data relay of a single node in multiple beam transmission to obtain an optical network.

[0105] The highest pipe quality is taken as data relay of a single node in multiple beam transmission to obtain an optical network. When the highest pipe quality is still low, other communication nodes are sequentially enabled to achieve multiple beam and multiple path transmission, thereby improving the system bandwidth and the invulnerability.

[0106] The application significantly reduces the access times of the directed optical network, improves the high-speed communication stability of optical communication and the routing recovery speed on the basis of the optical layering deployment structure and the optical orientation setting. The access type and stability of the algorithm of the prior art depend on probability, and no feasible deployment algorithm and adaptive design are proposed, and the actual landing ability is very poor. The design of the layered division security routing method of the dense medium directional transceiving network deployed and matched by the application fills the blank in the current underwater optical field. The voiceprint identity recognition algorithm not only improves the accuracy and speed of the AUV auxiliary node communication, but also significantly enhances the security of the communication, and effectively prevents illegal access. Further, the optical multi-beam routing algorithm is designed based on the proposed geographical layer-based optical node layered deployment structure, which effectively improves the utilization rate of communication resources and routing efficiency, and significantly enhances the throughput and anti-destroying performance.

[0107] Based on the above Figure 1 It can be seen from the implementation mode that the embodiment of the application constructs a geographical layer-based optical node layered deployment structure according to water depth; the optical node layered deployment structure is initialized according to chaotic mapping optimization network to obtain a corresponding initialized topological structure; the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer are subjected to acoustic communication and voiceprint information recognition to realize the identity recognition and safe access of the communication nodes in the same layer, and obtain an acoustic network, the acoustic network including a legal cross-layer relay node; the data relay of a single node in multi-beam transmission is determined according to a plurality of links corresponding to the legal cross-layer relay node to obtain an optical network. In this way, by utilizing the pseudo-random characteristics of chaotic mapping optimization network, high coverage rate distribution of communication nodes in the same layer can be realized, and at the same time, certain random characteristics are retained to adapt to the dynamic changes of the underwater environment, so that the communication nodes are easier to find and will not be interrupted; considering acoustic communication and voiceprint information recognition and a plurality of links corresponding to the legal cross-layer relay node, data relay of multi-beam transmission can be more easily performed, and rapid and high-coverage relay node deployment selection can be realized in a complex deployment area; the plurality of links corresponding to the legal cross-layer relay node can be fully utilized, so that the bandwidth utilization rate is higher.

[0108] Based on the same inventive concept, as an implementation of the above-mentioned layered division security routing method of the dense medium directional transceiving network, the embodiment of the application further provides a layered division security routing system of the dense medium directional transceiving network. Figure 3 The structure diagram of the system in the embodiment of the application is shown in Figure 3 The layered division security routing system of the dense medium directional transceiving network can include:

[0109] The construction module 301 is configured to construct a geographical layer-based optical node layered deployment structure according to water depth, and the optical node layered deployment structure includes a buoy node, a source node, a communication node and an AUV auxiliary node.

[0110] The initialization module 302 is configured to initialize the optical node hierarchical deployment structure according to the chaotic mapping optimization network, to obtain a corresponding initialized topological structure, and the initialized topological structure includes communication nodes in the same layer and AUV auxiliary nodes in adjacent layers.

[0111] The identification module 303 is configured to perform acoustic communication and voiceprint information identification on the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layers, to achieve identity recognition and safe access of the communication nodes in the same layer, and to obtain an acoustic network including legal cross-layer relay nodes.

[0112] The determination module 304 is configured to determine data relaying of a single node in multi-beam transmission according to a plurality of links corresponding to the legal cross-layer relay nodes, to obtain an optical network.

[0113] The initialization module 302 is specifically configured to layer the communication nodes according to water depth, to obtain a vector initial value corresponding to a first communication node in each layer; generate target chaotic variables in each layer according to the vector initial value and a Tent chaotic mapping formula, the target chaotic variables in each layer being chaotic variables corresponding to communication nodes other than the first communication node; and map the target chaotic variables in each layer into an underwater horizontal transmission space, to obtain target initialization position coordinates of all communication nodes in the same horizontal space, to obtain the initialized topological structure, the target initialization position coordinates being initialization position coordinates of all communication nodes in the same horizontal space.

[0114] The device can further include a filtering module configured to perform de-noising and enhancement processing on acoustic signals corresponding to the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layers, to obtain filtered signals, before performing acoustic communication and voiceprint information identification on the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layers, to achieve identity recognition and safe access of the communication nodes in the same layer, and to obtain an acoustic network.

[0115] The identification module 303 is specifically configured to convert the filtered signals to a frequency domain by using short-time Fourier transform, to obtain time-frequency information; map the time-frequency information to a mel frequency scale, to obtain a mel spectrum; input the mel spectrum into a neural convolutional network, to enable the neural convolutional network to output local features corresponding to the acoustic signals; and identify identities of adjacent communication nodes according to the local features, to obtain the acoustic network including legal relay nodes.

[0116] The device can further include an acquisition module configured to acquire a plurality of links corresponding to the legal cross-layer relay nodes according to optical beams, before determining data relaying of a single node in multi-beam transmission according to the plurality of links corresponding to the legal cross-layer relay nodes, to obtain an optical network.

[0117] The determining module 304 is specifically configured to determine a plurality of pipe qualities corresponding to the plurality of links according to the link signal-to-noise ratio, the bit error rate of the communication node transmitting data, the transmission bandwidth of the optical channel, and the remaining life of the communication node, the plurality of pipe qualities including a highest pipe quality; and take the highest pipe quality as data relay of a single node in multi-beam transmission to obtain the optical network.

[0118] It should be noted that the above description of the embodiment of the layered security routing system of the dense medium directional transceiving network is similar to the above description of the embodiment of the layered security routing method of the dense medium directional transceiving network, and has similar beneficial effects as the embodiment of the layered security routing method of the dense medium directional transceiving network. For technical details not disclosed in the embodiment of the layered security routing system of the dense medium directional transceiving network of the present embodiment, please refer to the description of the embodiment of the layered security routing method of the dense medium directional transceiving network of the present application for understanding.

[0119] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A hierarchical partitioning secure routing method for dense medium directional transceiver networks, characterized in that, include: A geographically layered deployment structure for optical nodes is constructed based on water depth. The optical node layered deployment structure includes buoy nodes, source nodes, communication nodes, and AUV auxiliary nodes. The network is optimized based on chaotic mapping. The hierarchical deployment structure of the optical nodes is initialized to obtain the corresponding initialized topology. The initialized topology includes communication nodes in the same layer and AUV auxiliary nodes in the adjacent layer. For the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer, sound communication and voiceprint information recognition are performed to realize the secure access of the communication nodes in the same layer, thereby obtaining an acoustic network, which includes legitimate cross-layer relay nodes; Based on the multiple links corresponding to the legitimate cross-layer relay nodes, the data relay of a single node in multi-beam transmission is determined to obtain the optical network; Before determining the data relay of a single node in multi-beam transmission based on the multiple links corresponding to the legitimate cross-layer relay node to obtain the optical network, the method further includes: Based on the beam, obtain multiple links corresponding to the legitimate cross-layer relay node; The step of determining the data relay of a single node in multi-beam transmission based on the multiple links corresponding to the legitimate cross-layer relay node to obtain the optical network includes: Based on the link signal-to-noise ratio, the bit error rate of data transmitted by the communication node, the transmission bandwidth of the optical channel, and the remaining lifetime of the communication node, the quality of multiple pipelines corresponding to the multiple links is determined, and the multiple pipeline quality includes the highest pipeline quality. The highest quality pipeline is used as the data relay for a single node in the multi-beam transmission to obtain the optical network.

2. The hierarchical partitioning secure routing method for dense medium directional transceiver networks according to claim 1, characterized in that, The process of optimizing the network based on chaotic mapping and initializing the hierarchical deployment structure of the optical nodes to obtain the corresponding initialized topology includes: Based on the water depth, the communication nodes are divided into layers to obtain the initial vector value corresponding to the first communication node in each layer; Based on the initial vector value and the Tent chaotic mapping formula, target chaotic variables for each layer are generated, and the target chaotic variables for each layer are the chaotic variables corresponding to other communication nodes except the first communication node. The chaotic variables of each target layer are mapped to the underwater horizontal transmission space to obtain the target initial position coordinates, so as to obtain the initial topology. The target initial position coordinates are the initial position coordinates of all communication nodes in each layer in the same horizontal space.

3. The hierarchical partitioning secure routing method for dense medium directional transceiver networks according to claim 1, characterized in that, Before performing acoustic communication and voiceprint information recognition on the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer to achieve secure access for identity recognition of the communication nodes in the same layer and obtain the acoustic network, the method includes: The acoustic signals corresponding to the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer are subjected to noise reduction and enhancement processing to obtain the filtered signals.

4. The hierarchical partitioning secure routing method for dense medium directional transceiver networks according to claim 3, characterized in that, The process of performing acoustic communication and voiceprint information recognition on the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer to achieve secure access for identity recognition of the communication nodes in the same layer, thereby obtaining an acoustic network, includes: The filtered signal is converted to the frequency domain using a short-time Fourier transform to obtain time-frequency information; The time-frequency information is mapped onto the Mel frequency scale to obtain the Mel spectrum; The Mel spectrum is input into a neural convolutional network so that the neural convolutional network outputs the local features corresponding to the acoustic signal; Based on the local features, the identities of neighboring communication nodes are identified to obtain the acoustic network, which includes the legitimate cross-layer relay nodes.

5. The hierarchical partitioning secure routing method for dense medium directional transceiver networks according to claim 4, characterized in that, The neural convolutional network comprises, in sequence, an input layer, a first convolutional layer, a first pooling layer, a second convolutional layer, a second pooling layer, a third convolutional layer, a third pooling layer, a flattened layer, a fully connected layer, a random deactivation layer, and an output layer.

6. The hierarchical partitioning secure routing method for dense medium directional transceiver networks according to claim 5, characterized in that, The step of inputting the Mel spectrum into a neural convolutional network so that the neural convolutional network outputs local features corresponding to the acoustic signal includes: The Mel spectrum is sequentially input into the input layer and the first convolutional layer, so that the first convolutional layer outputs the ground-based local features in the Mel spectrum; The local features of the foundation are sequentially input into the first pooling layer, the second convolutional layer, the third convolutional layer, and the third pooling layer, so that the third pooling layer outputs the first intermediate feature; The first intermediate feature is sequentially input into the flattening layer and the fully connected layer, so that the fully connected layer outputs the second intermediate feature; The second intermediate feature is mapped onto the classification space to obtain the third intermediate feature; The third intermediate feature is input into the random deactivation layer so that the random deactivation layer outputs the fourth intermediate feature. The fourth intermediate feature is input into the output layer so that the output layer can classify the fourth intermediate feature and output the local features corresponding to the acoustic signal.

7. The hierarchical partitioning secure routing method for dense medium directional transceiver networks according to claim 1, characterized in that, The expressions for the multiple pipeline qualities corresponding to the multiple links are: ； in, For the multiple pipeline qualities corresponding to the multiple links, As the first weight, The signal-to-noise ratio of the link. For the maximum link signal-to-noise ratio, As the second weight, The bit error rate for data transmission between communication nodes. The maximum bit error rate for data transmission between communication nodes. As the third weight, The transmission bandwidth of the optical channel. This is the maximum transmission bandwidth of the optical channel. The fourth weight, The remaining lifetime of the communication node. This represents the maximum remaining lifetime of the communication node.

8. A hierarchical partitioning secure routing system for dense medium directional transceiver networks, characterized in that, include: A construction module is used to build a geographically layered deployment structure for optical nodes based on water depth. The optical node layered deployment structure includes buoy nodes, source nodes, communication nodes, and AUV auxiliary nodes. An initialization module is used to optimize the network based on chaotic mapping and initialize the hierarchical deployment structure of the optical nodes to obtain the corresponding initialized topology, which includes communication nodes in the same layer and AUV auxiliary nodes in the adjacent layer. The identification module is used to perform acoustic communication and voiceprint information identification on the communication nodes in the same layer and the AUV auxiliary nodes in the adjacent layer, so as to realize the secure access of the communication nodes in the same layer and obtain the acoustic network, which includes legitimate cross-layer relay nodes. The determination module is used to determine the data relay of a single node in multi-beam transmission based on the multiple links corresponding to the legal cross-layer relay node, so as to obtain the optical network; The hierarchical secure routing system further includes: an acquisition module, used to acquire multiple links corresponding to the legitimate cross-layer relay node based on the multiple links corresponding to the legitimate cross-layer relay node before determining the data relay of the single node in the multi-beam transmission to obtain the optical network. The determining module is specifically used to determine multiple pipeline qualities corresponding to the multiple links based on the link signal-to-noise ratio, the bit error rate of data transmitted by the communication node, the transmission bandwidth of the optical channel, and the remaining lifetime of the communication node. The multiple pipeline qualities include the highest pipeline quality. The highest pipeline quality is used as the data relay of a single node in the multi-beam transmission to obtain the optical network.

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