UWSN resource allocation method based on MI communication and AUV assistance in multilayer ocean current environment

Abstract

The invention provides a UWSN resource allocation method based on MI communication and AUV assistance in a multilayer ocean current environment. The method comprises the following steps: S1, modeling and expressing a UWSN network structure; s2, modeling to express the stress and position offset of the wireless sensor SNs under the action of ocean current; s3, modeling and expressing an SNs deployment optimization scheme; s4, modeling and expressing an equivalent circuit structure of the SNs for performing MI communication by using the TD antenna; s5, modeling and expressing an AUVs navigation trajectory optimization scheme; s6, modeling to express the sum of data transmission energy consumption, environment sensing energy consumption and mobile energy consumption of the sensor nodes; step S7, modeling and expressing the distance between the SNs and the AUVs, the data transmission time between the SNs and the AUVs, the transmitting power of the SNs and the battery energy preset constraint of the SNs; step S8, modeling an optimization model for expressing energy consumption minimization of the SNs; step S9, adopting GSWOA to solve an optimization model for minimizing the energy consumption of the SNs; according to the method, the SNs transmitting power and the transmission distance between the SNs and the AUVs can be jointly optimized, so that the energy consumption of the SNs is minimized.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment. Background Technology

[0002] Underwater wireless sensor networks (UWSNs) have been widely applied in various fields such as marine resource exploration, disaster prediction, environmental monitoring, and military applications. Underwater information transmission faces challenges including complex environments, long transmission distances, and high data rate requirements. AUVs (Aerial Vehicles) offer advantages in flexible deployment and high mobility, making them an important component of UWSNs. Therefore, researching resource allocation methods for AUV-assisted UWSNs is of great significance.

[0003] However, most existing research focuses primarily on static underwater environments, neglecting the influence of underwater currents. We will analyze the impact of stratified currents on SNs and AUVs, optimize node deployment locations through I-VFA to improve network coverage, and optimize AUV navigation trajectories using GA. Furthermore, underwater acoustic communication suffers from severe multipath fading and Doppler effects, resulting in low data transmission rates. Underwater MI communication, on the other hand, can achieve reliable, energy-efficient, and secure data transmission over short distances. Therefore, combining MI with AUVs in UWSNs can help improve system performance. Traditional unidirectional coil antennas in MI communication exhibit strong directivity. Due to the influence of underwater currents, the position and orientation of the MI coil frequently change, leading to severe angular misalignment between transceivers and a significant reduction in the received signal-to-noise ratio.

[0004] To address this issue, this invention employs a direction-insensitive TD antenna, consisting of three mutually perpendicular unidirectional coils. Regardless of the TD coil antenna's rotation in space, a reliable MI link is always maintained. Therefore, there is a strong motivation to investigate UWSN resource allocation methods based on MI communication and AUV assistance in complex ocean environments. Summary of the Invention

[0005] This invention proposes a UWSN resource allocation method based on MI communication and AUV assistance in a multi-layer ocean current environment to solve the problems of low network coverage and limited battery power of SNs under the influence of ocean currents. It can minimize the energy consumption of SNs by jointly optimizing the SNs transmission power and the transmission distance between SNs and AUVs.

[0006] The present invention adopts the following technical solution.

[0007] A UWSN resource allocation method based on MI communication and AUV assistance in a multi-layer ocean current environment, for use in underwater robots (AUVs) and underwater sensor networks (UWSNs), is characterized by the following steps;

[0008] Step S1: Model and describe the UWSN network structure based on MI magnetic induction communication and AUV assistance in a multi-layer ocean current environment;

[0009] Step S2: Model and describe the forces and positional shifts of wireless sensors (SNs) under the influence of ocean currents;

[0010] Step S3: Model and describe the SNs deployment optimization scheme;

[0011] Step S4: Model and describe the equivalent circuit structure of SNs using TD antennas for MI communication;

[0012] Step S5: Model and describe the AUV's flight trajectory optimization scheme;

[0013] Step S6: Model and describe the total energy consumption of data transmission, environmental perception, and mobility of the sensor node;

[0014] Step S7: Model and describe the distance between SNs and AUVs, the data transmission time between SNs and AUVs, the transmission power of SNs, and the preset constraints on the battery energy of SNs.

[0015] Step S8: Model and describe an optimization model that minimizes the energy consumption of SNs;

[0016] Step S9: Use GSWOA to solve the optimization model for minimizing the energy consumption of SNs.

[0017] Step S1 specifically includes the following steps:

[0018] Step S11: Construct a UWSN network structure based on MI communication and AUV assistance in a multi-layer ocean current environment, including one surface base station, M AUVs and N SNs;

[0019] SNs are randomly distributed in the underwater space for data sensing and temporary data storage;

[0020] AUVs depart from the surface base station, travel to a specific ocean current layer, collect data from SNs via MI communication, and then return to the surface base station;

[0021] Step S12: The set of SNs is defined as follows: The set of AUVs is defined as ;

[0022] To account for the impact of ocean currents on SNs and AUVs, a multi-layered ocean current is modeled here, which flows at a constant velocity and direction at a certain depth; a constant depth is assumed. The ocean (flat bottom) and Different ocean current layers, with the first Taking a layer as an example, the depth of this layer is And satisfy The time required for UWSN to complete one round of data acquisition is the longest time required for all AUVs to complete data acquisition. To prevent a large difference in acquisition time between nodes near and far from the water surface, the depth of each layer gradually decreases from top to bottom. In each layer, the ocean current speed is... , ;

[0023] To better reflect real-world conditions, the velocity-depth relationship of the Gulf Stream is incorporated into the current ocean current model. Compare At deeper, .

[0024] Step S2 specifically involves:

[0025] sensor nodes The coordinates are represented as ,speed and acceleration SNs are subject to gravity underwater. ,buoyancy Propulsion and resistance The impact; and respectively along The axis has positive and negative directions; Decomposed into along shaft and The component of the force on the axis, and Decomposed into along , and The component of the force on the axis; the gravitational force acting on SN is ,in Indicates the density of SN. Indicates the volume of SN. This represents the acceleration due to gravity; the buoyant force acting on SNs is... ,in The density of water is given; the propulsive force acting on the SNs is... ,in It is a constant related to the cross-sectional shape of SN. This represents the cross-sectional area of ​​the propulsive force acting on the SN. This represents the velocity difference between the water flow velocity and the nodal velocity across the cross section; the resistance acting on SN is... ,in This represents a constant related to the cross-sectional shape of SNs. The cross-sectional area representing the resistance acting on SNs; the component force acting on SNs. , and Expressed as a formula:

[0026]

[0027] in, It is along The advance angle of the shaft, , yes The drag angle between the shafts ; It is the drag angle relative to the XOY plane. ;

[0028] combined efforts Represented as ,in Indicates the acceleration of SNs;

[0029] Acceleration decomposed into , and Velocity decomposed into , and Then we obtain the kinematic equations of SN:

[0030]

[0031] in, and These are the water flow speeds. along axis and along The components of the axis;

[0032] use replace , , replace To simplify the expression, the above formula can be further simplified to:

[0033]

[0034] In time At that time, assume the coordinates of a certain SN are The speed is The second-order differential equation is derived from the above equation; assuming... In time At that time, the coordinates of SN are represented as follows:

[0035] .

[0036] Step S3 employs an improved Virtual Force Algorithm, specifically: the Virtual Force Algorithm is a widely used distributed self-adjusting deployment technique that can be used to optimize the deployment of SNs to improve network coverage. Based on the traditional Virtual Force Algorithm, an improved Virtual Force Algorithm (I-VFA) is proposed here.

[0037] Virtual forces between nodes Geometric distance between nodes The decision is made using the following formula:

[0038]

[0039] in, For sensor nodes and The distance between them; and These represent the communication radius and sensing radius of the SNs, respectively. and These are the repulsion coefficient and the attraction coefficient; the optimal threshold distance. Set as Appropriate It can be adjusted according to the density of underwater nodes;

[0040] Virtual forces between monitoring boundaries The formula is described as follows:

[0041]

[0042] in, It is a node Euclidean distance from the nearest boundary of the monitoring area; These are pre-designed optimal boundary values; It is the repulsion coefficient of the boundary;

[0043] In extreme cases, using traditional virtual forces may result in multiple partitioned networks; to address this issue, a balancing force is introduced, expressed by the formula:

[0044]

[0045] in, For nodes The average distance to its neighboring nodes; for any node ,if ,but Considered a node The neighboring nodes; It is the coefficient of the balanced force;

[0046] node It is subjected to a net force and moves in the direction of the net force; the net force is... The expression is as follows:

[0047]

[0048] in, Indicates a node The number of nodes to which force is applied;

[0049] This step also uses a mobility benefit model that considers node density and residual node energy; this model constrains the distance a node moves from a low-node-density region to a high-node-density region, and also constrains the distance a node with low residual energy moves; the mobility benefit model is given by the following formula:

[0050]

[0051] in, , ;

[0052] exist and In the expression, and These represent the number of adjacent nodes in the positive and negative directions of the node's movement, respectively. and These are the area and volume in the positive and opposite directions of node movement, respectively.

[0053] This represents the average remaining energy of a node and its neighbors. , It is the number of neighboring nodes; Represents the residual energy of a node; and These are the adjustment coefficients for node density and node residual energy, respectively.

[0054] To focus on the optimization effect of the virtual force algorithm on node distribution, it is assumed that during the node coverage optimization phase, nodes are driven only by virtual forces; at time t, the virtual force... Decomposed into along , and The coordinates of the axis SNs are updated based on the influence of ocean currents, as expressed by the formula:

[0055]

[0056] in, , , It is virtual force The amount, For each adaptive movement distance, This represents the maximum distance moved in each iteration.

[0057] As iterations proceed, the SNs tend towards an equilibrium state, and the virtual force... It also gets smaller and smaller; to prevent small fluctuations in SNs, a threshold is set here. ,like If so, the movement distance of the node is set to 0.

[0058] Step S4 specifically involves the following: The TD antenna consists of three mutually perpendicular sub-coils. It is assumed that the center of its transmitting coil is located at the origin of the Cartesian coordinate system, and the receiving coil is located at point... The distance between the transmitting and receiving coils is [location missing]. The radii of the transmitting coil and the receiving coil are respectively and The number of turns are respectively and Assume the normalized normal vector of a sub-coil in the receiving coil is... ,in , , They are respectively The angles between the three sub-coils and the yoz, xoz, xoy planes; the pairwise orthogonal normal vectors of the three sub-coils. , , Represented as the following orthogonal matrix:

[0059]

[0060] in, , From this, the normal vectors of the other two sub-coils can be obtained. , ;

[0061] The formula for calculating the mutual inductance between the three spatially distributed transceiver coils in the three sub-coils is as follows:

[0062]

[0063] in, Indicates the permeability in free space;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] If the system's operating angular frequency is Then the self-impedance of the transmitting coil and the self-impedance of the receiving coil They are respectively , ; This is the equivalent internal resistance of the power supply. For load resistance, and For the resistance of the transmitting and receiving coils, and For the self-inductance of the transmitting and receiving coils, and These are the transmitting capacitor and the receiving capacitor;

[0071] According to Kirchhoff's voltage law, the loop equation is as follows: , In the formula This refers to the voltage of the transmitter battery. and Let be the currents in the transmitting and receiving circuits, respectively; and solve for them. , ;

[0072] When the antenna system resonates, the resonant circuit of the antenna only exhibits resistive characteristics, and the combined effect of capacitive and inductive reactance is zero. At this time, the self-impedance of the transmitter and the self-impedance of the receiver are respectively... , Then the transmission power and received power Defined as , ; where Re{·} represents the real part of the imaginary number;

[0073] The total path loss for underwater MI communication is ; This refers to path loss in a lossless medium. For the medium loss in seawater; where , These are the electrical conductivity and magnetic permeability of seawater, respectively. The frequency of the transmitted signal;

[0074] According to Shannon's formula, the first The MI communication transmission rate of each SN is:

[0075]

[0076] in, Thermal noise power, For SNs MI communication bandwidth, Kelvin temperature, is the Boltzmann constant.

[0077] Specifically, step S5 involves assuming the speed of the AUVs during navigation. The current is constant in size and adjustable in direction, and it is a stratified ocean current model; here we take the first... Taking the first layer as an example, the first layer... The velocity of the ocean current is AUV In the The actual velocity of the stratospheric current is , ; and The included angle between them is , ; and The included angle between them is , ;

[0078] express From node sail to the next node The corresponding vector; from this, we can derive , ;

[0079] In order to complete data collection as quickly as possible and reduce AUV energy consumption, Optimize the navigation trajectory; Collecting data starting from the base station. After all the data from the SNs are returned to the base station, The total sailing distance can be expressed as ;in, Represents base station, Indicates AUV The number of sensor nodes to be collected. Indicates from node To the node distance, Used to indicate and Is there a path between them? This optimization problem is solved using a genetic algorithm (GA).

[0080] Step S6 specifically involves: assuming the AUVs collect data from each SN in the following manner: , The transmit power of each SNs is The remaining energy of SNs is ;

[0081] underwater nodes The time to transmit data to the AUV is ;

[0082] underwater nodes The energy consumption for transmitting data to an AUV is ;

[0083] underwater nodes The energy consumption for sensing environmental information is , Indicates energy consumption per unit of data;

[0084] go through After the next iteration The energy consumption generated by virtual force movement is , This represents the energy consumed per unit distance a node moves; as the iteration progresses, the SNs tend towards an equilibrium state after optimized deployment. Gradually shrinking to 0; underwater node The total energy consumption is .

[0085] Specifically, step S7 involves modeling the distance between SNs and AUVs, the data transmission time between SNs and AUVs, the transmission power of SNs, and the limitations of the battery energy of SNs.

[0086] The distance constraints between SNs and AUVs are: ;

[0087] The data transmission time constraints between SNs and AUV are as follows: ;

[0088] The limitations on the transmit power of SNs are: ;

[0089] The limitations on the battery energy of SNs are: ;

[0090] in, and These represent the minimum and maximum information transmission distances between AUVs and SNs, respectively. and These are the minimum and maximum information transmission times between the AUV and SNs, respectively; and These are the minimum and maximum transmit powers of the SNs, respectively;

[0091] The transmit power constraint for SNs is: ;in, For the first The transmit power of each SNs; The minimum transmit power is set to 1mW; The maximum transmit power is set to 5W; therefore, the range of transmit power is... This range has been verified through simulation to meet the requirements for reliable communication.

[0092] Specifically, step S8 involves: assuming the size of the data collected by SNs has been pre-calculated, then... The mobile energy consumption of SNs is a constant. The optimization model, determined when using I-VFA to optimize node deployment, aims to minimize SN energy consumption, given constraints on the distance between SNs and AUVs, data transmission time between SNs and AUVs, SNs transmit power, and SNs battery energy. The optimal resource allocation method is then determined. .

[0093] Step S9 specifically involves using GSWOA to solve the optimization model for minimizing the energy consumption of SNs. The algorithm flowchart is shown below. Figure 2 As shown, the specific steps are as follows:

[0094] Step S91: First, for the constrained optimization problem, the problem with inequality constraints is transformed into an unconstrained problem using the penalty function method. A fitness function consisting of an objective function and a penalty function is constructed, expressed as follows:

[0095]

[0096] in, Let be the objective function. As a penalty factor, The penalty function contains the following formula:

[0097]

[0098]

[0099]

[0100]

[0101] Step S92: The specific steps of the GSWOA algorithm include:

[0102] Step S921: Initialize whale population size Maximum number of iterations and initialize the whale's position. ;

[0103] Step S922: Calculate the fitness value of each whale, find the current optimal fitness value and its corresponding position;

[0104] Step S923: In the prey encirclement phase, whales move closer to the whale closest to the prey (candidate solution) in the current pod, gradually shrinking the whale pod's encirclement. The whale position update formula for this stage is:

[0105] in, This represents the current iteration number. This indicates the optimal position of the whale so far. This represents the current position of the whale. For solving the optimization problem in this paper, the whale position is initialized. ;coefficient and It is obtained from the following calculations:

[0106]

[0107] in, The value decreases linearly from 2 to 0, expressed as: , This represents the total number of iterations. yes Random values ​​in;

[0108] Step S924: During the bubble web attack phase, the humpback whale spirals upward and exhales bubbles to trap its prey. The position update formula is:

[0109]

[0110] in, This indicates the whale's movement towards its prey. It is a constant for the shape of the logarithmic spiral. yes A random number;

[0111] When whales spiral to search for prey, they also tighten their encirclement. Assuming these two mechanisms have equal probability of execution, the position update formula is:

[0112]

[0113] in, for Random numbers are evenly distributed between them; when This indicates that the whale is within a shrinking encirclement and has chosen a spiraling encirclement method.

[0114] Step S925: To improve the global search capability of the whale optimization algorithm and increase the search range of the whale pod; when When this occurs, it indicates that the whale is outside the shrinking encirclement and has chosen a random shrinking method. The position is updated as follows:

[0115]

[0116] in, Given a random vector of whale positions;

[0117] Step S926: To improve the solution accuracy of the whale optimization algorithm, an inertia weight that varies with the number of iterations is added to the whale's position update. ,

[0118] ;

[0119] Number the spiral shape constant Designed as a variable that changes with the number of iterations. The position update formula for GSWOA is:

[0120]

[0121] Step S927: Determine whether to update the position of the optimal solution according to the following formula:

[0122] ;

[0123] Step S928: Determine whether the termination condition has been met. If yes, proceed to the next step; otherwise, jump to step S923.

[0124] Step S929: The program ends and the optimal result is output.

[0125] The beneficial effects of this invention are as follows:

[0126] 1. This invention fully considers the impact of multi-layered ocean current environments on SNs and AUVs, and constructs a stratified ocean current model with unequal intervals. I-VFA is used to effectively optimize node deployment locations, significantly improving the network coverage of the underwater wireless sensor network, and velocity vector synthesis is used to correct the navigation trajectory of AUVs.

[0127] 2. This invention employs a direction-insensitive TD antenna model to construct the MI communication link. This effectively solves the problem of communication quality degradation or interruption caused by angular misalignment of traditional unidirectional coils under ocean current disturbances, ensuring the reliability of data transmission in dynamic water flow environments.

[0128] 3. This invention jointly optimizes the transmit power of SNs and the transmission distance between SNs and AUVs to minimize SN energy consumption and improve system performance. Furthermore, the GSWOA algorithm employed in this invention exhibits faster convergence speed and better global convergence capability compared to benchmark algorithms. Attached Figure Description

[0129] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0130] Appendix Figure 1 This is a schematic diagram of a UWSN network structure based on MI communication and AUV assistance in a multi-layer ocean current environment according to one embodiment of the present invention;

[0131] Appendix Figure 2 This is a schematic diagram of the resource allocation algorithm based on the GSWOA algorithm in one embodiment of the present invention;

[0132] Appendix Figure 3 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0133] This study addresses the challenges of low network coverage and limited node battery power consumption in UWSNs operating in ocean current environments by investigating a UWSN resource allocation method based on MI communication and AUV assistance. Specifically, the target monitoring area is divided into multiple ocean current layers at unequal intervals. First, satellite arrays (SNs) are deployed within these current layers, and the impact of ocean currents on the SNs is analyzed. An inter-vehicle-air communication (I-VFA) approach is introduced to improve UWSN network coverage. Then, AUVs are deployed to specific current layers to collect data from the SNs via MI communication, and GA (Global Algorithm) is used to optimize the AUVs' trajectories. To reduce SN energy consumption, the transmit power of the SNs and the distance between the SNs and AUVs are jointly optimized under constraints such as energy causality, transmit power, distance, and transmission time. This combined optimization problem is solved using a global search algorithm (GSWOA) to obtain a suboptimal solution. The GSWOA algorithm is then used iteratively to minimize SN energy consumption. Compared to other approaches, this method can more effectively reduce system energy consumption.

[0134] Specifically, it includes:

[0135] I. Network Model of UWSN Based on MI Communication and AUV Assistance

[0136] This embodiment proposes a UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment. The network structure diagram of the UWSN based on MI communication and AUV assistance is shown below. Figure 1As shown in the diagram, this system constructs a UWSN network structure based on MI communication and AUV assistance, including one surface base station, M AUVs, and N ASNs. SNs are randomly distributed in the underwater space for data sensing and temporary data storage. AUVs depart from the surface base station, travel to specific ocean current layers, collect data from the SNs via MI communication, and then return to the surface base station.

[0137] The system described in this example uses magnetic induction (MI) communication, which differs from traditional electromagnetic wave communication. MI communication utilizes the near-field coupling effect of low-frequency magnetic fields for signal transmission, rather than relying on electromagnetic wave radiation propagation, thus avoiding the problem of electromagnetic wave absorption in the marine environment.

[0138] MI communication is based on the principle of near-field magnetic coupling, where signals are transmitted between transmitting and receiving coils in the form of a magnetic field, carrying both energy and information. Considering the absorption characteristics of the marine environment, the loss in underwater MI communication in this model includes path loss in lossless media in the formula. and medium loss in seawater In dielectric loss In the modeling, the electrical conductivity of seawater has been included in this example. magnetic permeability and the operating frequency of MI communication Take it into consideration.

[0139] MI communication operates in the low-frequency (30 to 300 kHz) or very low-frequency (3 to 30 kHz) band. In this example, it is set to 10 kHz, which can effectively penetrate conductive seawater media, making it suitable for short-range, high-reliability, and low-power underwater communication scenarios.

[0140] In this example, the antenna structure modeling uses tri-directional (TD) antennas for both transmitting and receiving antennas.

[0141] Both the transmitter and receiver employ the same TD structure. The TD antenna consists of three mutually perpendicular coils. This structure is designed to address the deflection problem caused by frequent changes in coil position and orientation due to ocean current disturbances. Regardless of how the TD coil antenna rotates in space, a reliable MI link can always be maintained.

[0142] II. Establishing a UWSN resource allocation model based on MI communication and AUV assistance in an ocean current environment

[0143] like Figure 3As shown, this example provides a resource allocation method for an underwater wireless sensor network (UWSN) based on magnetic induction (MI) communication and autonomous underwater vehicle (AUV) assistance in a multi-layered ocean current environment, belonging to the field of wireless communication technology. This invention primarily addresses the problems of low network coverage and limited battery power of sensor nodes (SNs) under the influence of ocean currents.

[0144] This method, while simultaneously satisfying the constraints of the distance between AUV and SNs, the data transmission time between SNs and AUV, the transmission power of SNs, and the battery energy of SNs, jointly optimizes the transmission power of SNs and the transmission distance between SNs and AUVs to minimize the energy consumption of SNs. The method specifically includes the following steps: S1: Modeling the UWSN network structure based on MI communication and AUV assistance in a multi-layered ocean current environment; S2: Modeling the forces and positional displacement of SNs under the influence of ocean currents; S3: Modeling an optimized deployment scheme for SNs; S4: Modeling the equivalent circuit structure of SNs using tri-directional (TD) antennas for MI communication; S5: Modeling an optimized navigation trajectory scheme for AUVs; S6: Modeling the total energy consumption of sensor nodes, including data transmission energy consumption, environmental perception energy consumption, and mobility energy consumption; S7: Modeling the SNs-AUV distance, data transmission time between SNs and AUVs, SNs transmission power, and preset constraints on SNs battery energy; S8: Modeling an optimization model to minimize SNs energy consumption; S9: Solving the optimization model to minimize the total energy consumption of SNs using the Global Search Whale Optimization Algorithm (GSWOA) with a global search strategy. This invention can minimize SNs energy consumption by optimizing the resource allocation method of UWSNs based on MI communication and AUV assistance in an ocean current environment.

Claims

1. A UWSN resource allocation method based on MI communication and AUV assistance in a multi-layer ocean current environment, for use in underwater robots (AUVs) and underwater sensor networks (UWSNs), characterized by: Includes the following steps; Step S1: Model and describe the UWSN network structure based on MI magnetic induction communication and AUV assistance in a multi-layer ocean current environment; Step S2: Model and describe the forces and positional shifts of wireless sensors (SNs) under the influence of ocean currents; Step S3: Model and describe the SNs deployment optimization scheme; Step S4: Model and describe the equivalent circuit structure of SNs using TD antennas for MI communication; Step S5: Model and describe the AUV's flight trajectory optimization scheme; Step S6: Model and describe the total energy consumption of data transmission, environmental perception, and mobility of the sensor node; Step S7: Model and describe the distance between SNs and AUVs, the data transmission time between SNs and AUVs, the transmission power of SNs, and the preset constraints on the battery energy of SNs. Step S8: Model and describe an optimization model that minimizes the energy consumption of SNs; Step S9: Use GSWOA to solve the optimization model for minimizing the energy consumption of SNs.

2. The UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment according to claim 1, characterized in that: Step S1 specifically includes the following steps: Step S11: Construct a UWSN network structure based on MI communication and AUV assistance in a multi-layer ocean current environment, including one surface base station, M AUVs and N SNs; SNs are randomly distributed in the underwater space for data sensing and temporary data storage; AUVs depart from the surface base station, travel to a specific ocean current layer, collect data from SNs via MI communication, and then return to the surface base station; Step S12: The set of SNs is defined as follows: The set of AUVs is defined as ; Modeling multiple ocean currents, assuming a constant depth The ocean (flat bottom) and The first different ocean current layer, Layer depth is And satisfy The time required for UWSN to complete one round of data acquisition is the longest time required for all AUVs to complete data acquisition. To prevent a large difference in acquisition time between nodes near and far from the water surface, the depth of each layer gradually decreases from top to bottom. In each layer, the ocean current speed is... , ; Incorporating the velocity-depth relationship of the Gulf Stream into current ocean current models, when Compare At deeper, .

3. The UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment according to claim 1, characterized in that: Step S2 specifically involves: sensor nodes The coordinates are represented as ,speed and acceleration SNs are subject to gravity underwater. ,buoyancy Propulsion and resistance The impact; and respectively along The axis has positive and negative directions; Decomposed into along shaft and The component of the force on the axis, and Decomposed into along , and The component of the force on the axis; the gravitational force acting on SN is ,in Indicates the density of SN. Indicates the volume of SN. Represents gravitational acceleration; The buoyancy force acting on SNs is ,in The density of water is given; the propulsive force acting on the SNs is... ,in It is a constant related to the cross-sectional shape of SN. This represents the cross-sectional area of ​​the propulsive force acting on the SN. This represents the velocity difference between the water flow velocity and the node velocity across the cross section; The resistance acting on SN is ,in This represents a constant related to the cross-sectional shape of SNs. This represents the cross-sectional area of ​​the resistance acting on SNs; Component forces acting on SNs , and Expressed as a formula: in, It is along The advance angle of the shaft, , yes The drag angle between the shafts ; It is the drag angle relative to the XOY plane. ; combined efforts Represented as ,in Indicates the acceleration of SNs; Acceleration decomposed into , and Velocity decomposed into , and Then we obtain the kinematic equations of SN: in, and These are the water flow speeds. along axis and along The components of the axis; use replace , , replace To simplify the expression, the above formula can be further simplified to: In time At that time, assume the coordinates of a certain SN are The speed is The second-order differential equation is derived from the above equation; assuming... In time At that time, the coordinates of SN are represented as follows: 。 4. The UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment according to claim 1, characterized in that: Step S3 employs an improved virtual force algorithm, specifically as follows: Virtual forces between nodes Geometric distance between nodes The decision is made using the following formula: in, For sensor nodes and The distance between them; and These represent the communication radius and sensing radius of the SNs, respectively. and These are the repulsion coefficient and the attraction coefficient; the optimal threshold distance. Set as ; Virtual forces between monitoring boundaries The formula is described as follows: in, It is a node Euclidean distance from the nearest boundary of the monitoring area; These are pre-designed optimal boundary values; It is the repulsion coefficient of the boundary; Balanced forces are introduced, expressed by the formula: in, For nodes The average distance to its neighboring nodes; for any node ,if ,but Considered a node The neighboring nodes; It is the coefficient of the balanced force; node It is subjected to a net force and moves in the direction of the net force; the net force is... The expression is as follows: in, Indicates a node The number of nodes to which force is applied; This step also uses a mobility benefit model that considers node density and residual node energy; this model constrains the distance a node moves from a low-node-density region to a high-node-density region, and also constrains the distance a node with low residual energy moves; the mobility benefit model is given by the following formula: in, , ; exist and In the expression, and These represent the number of adjacent nodes in the positive and negative directions of the node's movement, respectively. and These are the area and volume in the positive and opposite directions of node movement, respectively. This represents the average remaining energy of a node and its neighbors. , It is the number of neighboring nodes; Represents the residual energy of a node; and These are the adjustment coefficients for node density and node residual energy, respectively. Assume that during the node coverage optimization phase, nodes are driven only by virtual forces; at time t, the virtual force... Decomposed into along , and The coordinates of the axis SNs are updated based on the influence of ocean currents, as expressed by the formula: in, , , It is virtual force The amount, For each adaptive movement distance, This represents the maximum distance moved in each iteration. As iterations proceed, the SNs tend towards an equilibrium state, and the virtual force... It also gets smaller and smaller; to prevent small fluctuations in SNs, a threshold is set here. ,like If so, the movement distance of the node is set to 0.

5. The UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment according to claim 1, characterized in that: Step S4 specifically involves the following: The TD antenna consists of three mutually perpendicular sub-coils. It is assumed that the center of its transmitting coil is located at the origin of the Cartesian coordinate system, and the receiving coil is located at point... The distance between the transmitting and receiving coils is [location missing]. The radii of the transmitting coil and the receiving coil are respectively and The number of turns are respectively and Assume the normalized normal vector of a sub-coil in the receiving coil is... ,in , , They are respectively The angles between the three sub-coils and the yoz, xoz, xoy planes; the pairwise orthogonal normal vectors of the three sub-coils. , , Represented as the following orthogonal matrix: in, , From this, the normal vectors of the other two sub-coils can be obtained. , ; The formula for calculating the mutual inductance between the three spatially distributed transceiver coils in the three sub-coils is as follows: in, Indicates the permeability in free space; ； ； ； ； ； ； If the system's operating angular frequency is Then the self-impedance of the transmitting coil and the self-impedance of the receiving coil They are respectively , ; This is the equivalent internal resistance of the power supply. For load resistance, and For the resistance of the transmitting and receiving coils, and For the self-inductance of the transmitting and receiving coils, and These are the transmitting capacitor and the receiving capacitor; List the loop equations as follows , In the formula This refers to the voltage of the transmitter battery. and Let be the currents in the transmitting and receiving circuits, respectively; and solve for them. , ; When the antenna system resonates, the resonant circuit of the antenna exhibits resistive characteristics, and the combined effect of capacitive and inductive reactance is zero. At this time, the self-impedance of the transmitter and the self-impedance of the receiver are respectively... , Then the transmission power and received power Defined as , ; where Re{·} represents the real part of the imaginary number; The total path loss for underwater MI communication is ; This refers to path loss in a lossless medium. For the medium loss in seawater; where , These are the electrical conductivity and magnetic permeability of seawater, respectively. The frequency of the transmitted signal; No. The MI communication transmission rate of each SN is: in, Thermal noise power, For SNs MI communication bandwidth, Kelvin temperature, is the Boltzmann constant.

6. The UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment according to claim 1, characterized in that: Specifically, step S5 involves assuming the speed of the AUVs during navigation. The current is constant in size and adjustable in direction, and it follows a stratified ocean current model; The velocity of the ocean current is AUV In the The actual velocity of the stratospheric current is , ; and The included angle between them is , ； and The included angle between them is , ； express From node sail to the next node The corresponding vector; from this, we can derive , ; right Optimize the navigation trajectory; Collecting data starting from the base station. After all the data from the SNs are returned to the base station, The total sailing distance can be expressed as ;in, Represents base station, Indicates AUV The number of sensor nodes to be collected. Indicates from node To the node distance, Used to indicate and Is there a path between them? This optimization problem is solved using a genetic algorithm.

7. The UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment according to claim 1, characterized in that: Step S6 specifically involves: assuming the AUVs collect data from each SN in the following manner: , The transmit power of each SNs is The remaining energy of SNs is ; underwater nodes The time to transmit data to the AUV is ; underwater nodes The energy consumption for transmitting data to an AUV is ; underwater nodes The energy consumption for sensing environmental information is , Indicates energy consumption per unit of data; go through After the next iteration The energy consumption generated by virtual force movement is , This represents the energy consumed per unit distance a node moves; as the iteration progresses, the SNs tend towards an equilibrium state after optimized deployment. Gradually shrinking to 0; underwater node The total energy consumption is .

8. The UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment according to claim 1, characterized in that: Specifically, step S7 involves modeling the distance between SNs and AUVs, the data transmission time between SNs and AUVs, the transmission power of SNs, and the limitations of the battery energy of SNs. The distance constraints between SNs and AUVs are: ; The data transmission time constraints between SNs and AUV are as follows: ; The limitations on the transmit power of SNs are: ; The limitations on the battery energy of SNs are: ; in, and These represent the minimum and maximum information transmission distances between AUVs and SNs, respectively. and These are the minimum and maximum information transmission times between the AUV and SNs, respectively; and These are the minimum and maximum transmit powers of the SNs, respectively; The transmit power constraint for SNs is: ;in, For the first The transmit power of each SNs; This is the minimum transmit power. This represents the maximum transmission power.

9. The UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment according to claim 1, characterized in that: Specifically, step S8 involves: assuming the size of the data collected by SNs has been pre-calculated, then... The mobile energy consumption of SNs is a constant. The optimization model, determined when using I-VFA to optimize node deployment, aims to minimize SN energy consumption, given constraints on the distance between SNs and AUVs, data transmission time between SNs and AUVs, SNs transmit power, and SNs battery energy. The optimal resource allocation method is then determined. .

10. The UWSN resource allocation method based on MI communication and AUV assistance in a multi-layered ocean current environment according to claim 1, characterized in that: Step S9 specifically involves: using GSWOA to solve the optimization model for minimizing the energy consumption of SNs. The specific steps are as follows: Step S91: First, for the constrained optimization problem, the problem with inequality constraints is transformed into an unconstrained problem using the penalty function method. A fitness function consisting of an objective function and a penalty function is constructed, expressed as follows: in, Let be the objective function. As a penalty factor, The penalty function contains the following formula: Step S92: The specific steps of the GSWOA algorithm include: Step S921: Initialize whale population size Maximum number of iterations and initialize the whale's position. ; Step S922: Calculate the fitness value of each whale, find the current optimal fitness value and its corresponding position; Step S923: During the prey encirclement phase, whales move towards the whale closest to the prey within the current pod, gradually tightening the encirclement. The whale position update formula for this stage is: in, This represents the current iteration number. This indicates the optimal position of the whale so far. This represents the current position of the whale. For solving the optimization problem in this paper, the whale position is initialized. ;coefficient and It is obtained from the following calculations: in, The value decreases linearly from 2 to 0, expressed as: , This represents the total number of iterations. yes Random values ​​in; Step S924: During the bubble web attack phase, the humpback whale spirals upward and exhales bubbles to trap its prey. The position update formula is: in, This indicates the whale's movement towards its prey. It is a constant for the shape of the logarithmic spiral. yes A random number; When whales spiral to search for prey, they also tighten their encirclement. Assuming these two mechanisms have equal probability of execution, the position update formula is: in, for Random numbers are evenly distributed between them; when This indicates that the whale is within a shrinking encirclement and has chosen a spiraling encirclement method. Step S925: To improve the global search capability of the whale optimization algorithm and increase the search range of the whale pod; when When this occurs, it indicates that the whale is outside the shrinking encirclement and has chosen a random shrinking method. The position is updated as follows: in, Given a random vector of whale positions; Step S926: To improve the solution accuracy of the whale optimization algorithm, an inertia weight that varies with the number of iterations is added to the whale's position update. , ； Number the spiral shape constant Designed as a variable that changes with the number of iterations. The position update formula for GSWOA is: Step S927: Determine whether to update the position of the optimal solution according to the following formula: ； Step S928: Determine whether the termination condition has been met. If yes, proceed to the next step; otherwise, jump to step S923. Step S929: Output the optimal result.

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