UWSN resource allocation method based on MI communication in ocean current scene

Abstract

The invention relates to a UWSN resource allocation method based on MI communication in an ocean current scene, and belongs to the technical field of wireless communication. The method comprises the following steps: S1, modeling a UWSN network structure based on MI communication; s2, modeling an offset position of the ASNs; s3, modeling a magnetic coupling resonance wireless energy equivalent circuit structure; s4, modeling energy consumed by the AUV for charging the ASNs; s5, modeling energy consumed by the ASNs for sending data to the AUV; s6, modeling energy consumed by navigation and hovering of the AUV; s7, modeling the sum of the navigation energy consumption, the hovering energy consumption and the charging loss of the AUV and the energy consumption of the ASNs communication link; s8, modeling a preset constraint of the AUV transmitting power and the ASNs transmitting power; s9, modeling an optimization model for minimizing the total energy consumption of the system; and S10, an improved sparrow search algorithm fusing Cauchy variation and reverse learning is adopted to solve the optimization model for minimizing the total energy consumption of the system. According to the method, the total energy consumption of the system can be minimized by optimizing the resource allocation strategy based on the MI communication UWSN in the ocean current scene.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and particularly relates to a resource allocation method for UWSN based on MI communication in an ocean current scenario. Background Art

[0002] Underwater wireless sensor networks have been widely applied in the fields of marine resource exploration, disaster prediction, environmental monitoring, and military. In a system with limited resources, AUV has the advantages of flexible deployment and high mobility, making it an important part of UWSN. In addition, a reasonable resource allocation strategy can improve the system performance. Therefore, it is of great significance to study the resource allocation strategy of AUV-assisted UWSN.

[0003] However, most of the existing studies have considered the sensor nodes (SNs) on the seabed plane, without considering the SNs at different depths in seawater. We consider and analyze the influence of multi-layer ocean currents on AUV and SNs. In addition, the underwater acoustic communication channel has severe multipath fading and Doppler effects, resulting in a low data transmission rate. MI communication has the advantages of stable channel and high short-distance data rate. Combining MI and AUV in UWSN will help improve the system performance. At the same time, the energy of SNs is limited. To avoid the situation that SNs die due to insufficient energy, we consider using MCR-WPT technology. AUV can charge the battery of each SN. SNs can obtain sufficient energy. Therefore, there is a strong motivation to study the resource allocation strategy of UWSN based on MI communication in complex oceans. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems of low data rate and limited battery power in UWSN based on traditional acoustic communication, and provide a resource allocation method for UWSN based on MI communication in an ocean current scenario. This method models the joint allocation problem of transmission power as an optimization problem under the constraint conditions of the transmission power of the autonomous underwater vehicle (AUV), the transmission power of the anchored sensor nodes (ASNs), and the battery energy of ASNs, so as to achieve the goal of minimizing the system energy consumption. The present invention can minimize the total energy consumption of the system by optimizing the resource allocation strategy of UWSN based on MI communication in an ocean current scenario.

[0005] To achieve the above purpose, the technical solution of the present invention is: a resource allocation method for UWSN based on MI communication in an ocean current scenario, including:

[0006] S1. Model the network structure of UWSN based on magnetic induction MI communication;

[0007] S2. Model the offset positions of the modeling anchor sensor nodes ASNs;

[0008] S3. Model the equivalent circuit structure of magnetic coupled resonant wireless energy;

[0009] S4. Model the energy consumed by the autonomous underwater vehicle AUV to charge the ASNs;

[0010] S5. Model the energy consumed by the ASNs to send data to the AUV;

[0011] S6. Model the energy consumed by the AUV during navigation and hovering;

[0012] S7. Model the sum of the energy consumption of the AUV during navigation, the energy consumption during hovering, the charging loss, and the energy consumption of the ASNs communication link;

[0013] S8. Model the limiting conditions of the transmission power of the AUV and the ASNs;

[0014] S9. Model the optimization model for minimizing the total energy consumption of the system;

[0015] S10. Use ISSA to solve the optimization model for minimizing the total energy consumption of the system.

[0016] In an embodiment of the present invention, the step S1 is specifically as follows:

[0017] Step S11. Construct the network structure of the UWSN based on MI communication, including a surface base station, an AUV, and N ASNs; the AUV is used to charge the N ASNs and receive information transmitted from the ASNs; each ASN includes a rechargeable battery, and the AUV charges each ASN through magnetic coupled resonance wireless energy transfer MCR-WPT;

[0018] Step S12. The set of all ASNs is defined as S = {S 1 , S 2 , …, S N}, S n represents the nth ASN in the set, n ∈ [1, N]; assume that the size of the data collected by the AUV from each ASNs is represented by the set I = {I 1 , I 2 , …, I N}; each ASNs knows its anchor coordinates as (x n , y n , 0) and the anchor chain length l i , L = {l 1 , l 2 , …, l N}; An ocean with a constant depth H and K different ocean current layers, where the height of each ocean current layer is ΔH = H / K; In the K-th layer, the velocity and direction of the ocean current are modeled as piecewise constant functions, which are v w (k) and k = 1, 2, …, K; Additionally, for a more realistic scenario, the velocity of the ocean current depends on the depth, assuming v w (k - 1) > v w (k).

[0019] In an embodiment of the present invention, the step S2 is specifically:

[0020] Calculate the offset position of the n-th ASNs according to the following formula:

[0021]

[0022] where, (x n,0 , y n,0 , z n,0 ) and (x n , y n , z n ) respectively represent the coordinates of the ASNs at the static position and the coordinates of the ASNs at the offset position; is the horizontal offset angle, which is equivalent to the angle between the ocean current layer where the current ASNs is located and the X-axis direction, α n represents the vertical offset angle, α n ∈(0, θ 2 ; θ 1 and θ 2 are the maximum horizontal and vertical offset angles respectively;

[0023] Calculate α n as:

[0024]

[0025] where, F g = ρV S g is the gravity of the ASNs, ρ is the density of the ASNs, V s is the volume of the ASNs, and g is the acceleration due to gravity of the earth; F b = ρ w V S g is the buoyancy of the seawater on the ASNs, ρ w is the density of the seawater; is the resistance of the ASNs to the ocean current, which is affected by the cross-sectional area A R of the ASNs, A R is the cross-sectional area of the ASNs, c is the resistance parameter of the object shape, and v w is the velocity of the ocean current.

[0026] In an embodiment of the present invention, step S3 is specifically as follows:

[0027] When the transmitting coil and the receiving coil are both solenoid structures and are coaxially placed, the mutual inductance calculation formula between the coupling coils is In the formula, μ represents the magnetic permeability of seawater, N t and N r respectively represent the number of turns of the transmitting coil and the receiving coil, a t and a r respectively represent the radii of the transmitting coil and the receiving coil, and d represents the charging distance between the AUV and the ASNs;

[0028] If the system operating angular frequency is ω, then the self-impedance Z t of the transmitting coil and the self-impedance Z r of the receiving coil are respectively R S is the equivalent internal resistance of the power supply, R L is the load resistance, R t and R r are the resistances of the transmitting and receiving coils, L t and L r are the self-inductances of the transmitting and receiving coils, C t and C r are the transmitting capacitance and the receiving capacitance;

[0029] According to Kirchhoff's voltage law, the loop equation is listed as U S = Z t i 1 + jωM′i 2 , 0 = Z r i 2 + jωM′i 1 , where M′ is the mutual inductance between the coupling coils, U S is the voltage of the transmitter battery, i 1 and i 2 are the currents of the transmitting loop and the receiving loop respectively; and the solution is

[0030] When the system resonates, the resonant circuit only exhibits a resistance characteristic, and the combined effect of the capacitive reactance and the inductive reactance is 0. At this time, the self-impedances of the transmitter and the receiver are Z t = R s + R t , Z r = R L + R r ; then the input power P in and the output power P out are defined as Where Re{·} represents the real part of an imaginary number.

[0031] In an embodiment of the present invention, step S4 is specifically as follows:

[0032] According to the following formula, calculate the energy consumed by the AUV to charge the nth ASNs:

[0033] E n,loss = E n,in - E n,out

[0034] Where, E n,in = P n,in T n,out is the energy transmitted by the AUV to the nth ASNs, and P n,in in the formula is the input power transmitted by the AUV to the nth ASNs; is the energy received by the nth ASNs from the AUV; PL UMI = PL MI + PL EC is the total path loss; is the path loss in a lossless medium; is the medium loss in seawater; in the formula, σ and μ are the conductivity and permeability of seawater respectively; f is the operating frequency of the MCR-WPT system;

[0035] According to the following formula, calculate the charging time for the AUV to charge the nth ASNs:

[0036]

[0037] Where, the maximum energy of the ASNs battery is E max , and the remaining energy of the nth ASNs battery is E n,left .

[0038] In an embodiment of the present invention, step S5 is specifically as follows:

[0039] According to the following formula, calculate the energy consumption of the nth ASNs for transmitting data to the AUV:

[0040] E n,t = P n,t T n,MI

[0041] Where, is the transmission power of the nth ASNs; is the time for the nth ASNs to transmit data to the AUV, and I n in the formula is the amount of data transmitted by the nth ASNs to the AUV; in the formula is the MI communication transmission rate of the nth ASNs, N0 = kBT is the thermal noise power, B is the communication bandwidth of all ASNs MI communications, T is the Kelvin temperature, and k ≈ 1.38×10 -23 J / K is the Boltzmann constant;

[0042] According to the following formula, calculate the energy consumption of the AUV when receiving the nth ASNs data:

[0043]

[0044] In an embodiment of the present invention, the step S6 is specifically:

[0045] According to the following formula, calculate the energy consumption of the AUV hovering at the nth ASNs:

[0046]

[0047] Among them, S AUV is the surface area of the AUV, η is the efficiency of the AUV propulsion system, ρ w is the density of water, c d is the drag coefficient of the AUV, is the magnitude of the velocity of the kth layer of ocean current where the AUV hovers, ||·|| 2 represents the two-norm of the vector, that is, the magnitude of the vector;

[0048] Adopt the particle swarm optimization algorithm based on the genetic algorithm, namely the GA-PSO algorithm, to optimize the trajectory of the AUV, that is, find a shortest trajectory that traverses N ASNs, and search and sort the set S = {S 1 , S 2 , …, S N} to minimize the navigation energy consumption of the AUV;

[0049] According to the following formula, calculate the energy consumption of the AUV traversing all ASNs:

[0050]

[0051] Among them, is the magnitude of the navigation speed of the AUV in still water, T n,n+1 is the navigation time of the AUV from the nth ASNs to the n + 1th ASNs;

[0052] According to the following formula, calculate the navigation time of the AUV from the nth ASNs to the n + 1th ASNs:

[0053]

[0054] Among them, d n,n+1 (k) is the distance traveled by the AUV in the kth layer of ocean current, is the actual speed of the AUV starting from the nth ASN in the kth layer of ocean current, where i and j represent the ocean current layers where the nth and (n + 1)th ASNs are located respectively;

[0055] According to the following formula, calculate the distance traveled by the AUV in the kth layer of ocean current:

[0056]

[0057] where h n,n+1 (k) is the vertical height from the upper bound of the ocean current layer where the AUV is located to the nth ASN, and h n,n+1 (j) is the vertical height from the nth ASN to the (n + 1)th ASN, and d n,n+1 is the distance between the nth ASN and the (n + 1)th ASN;

[0058] According to the following formula, calculate the actual speed of the AUV starting from the nth ASN in the kth layer of ocean current:

[0059]

[0060] where β is and the included angle between, ω is and the included angle between, S n and S n+1 represent the position coordinates of the nth ASN and the (n + 1)th ASN respectively, is the vector from the nth ASN to the (n + 1)th ASN.

[0061] In an embodiment of the present invention, the step S7 is specifically:

[0062] According to the following formula, calculate the total energy consumption of the system:

[0063]

[0064] The step S8 is specifically:

[0065] The AUV transmission power limit condition is: P in,min ≤P n,in ≤P in,max , n ∈ {1, 2,..., N}, P in,max =P in ;

[0066] The ASN transmission power limit condition is: P t,min ≤P n,t ≤P t,max , n ∈ {1, 2,..., N},

[0067] The limiting conditions for ASNs energy harvesting are: E n,out +E n,left ≥E n,t ;

[0068] wherein, P in,min and P t,min are respectively the minimum transmission powers of the AUV and the ASNs, and P in,max and P t,max are respectively the maximum transmission powers of the AUV and the ASNs.

[0069] In an embodiment of the present invention, the step S9 is specifically: after optimizing the AUV trajectory by the GA-PSO algorithm, if the order in which the AUV traverses the ASNs is known, then E voyage is a fixed value. Therefore, under the constraint conditions of the AUV transmission power, the ASNs transmission power, and the ASNs energy harvesting, the optimization model determines the optimized resource allocation strategy with the goal of minimizing the system energy consumption, that is wherein, P in =(P 1,in , P 2,in , …, P N,in ) is the transmission power of the AUV for charging N ASNs, and P t =(P 1,t , P 2,t , …, P N,t ) is the transmission power of N ASNs for MI communication.

[0070] In an embodiment of the present invention, the step S10 is specifically:

[0071] S101. For the optimization problem with constraint conditions, the inequality constraint problem is transformed into an unconstrained problem by the penalty function method, and a fitness function composed of an objective function and a penalty function is constructed; it is given by the following formula:

[0072] f i (P in , P t ) = f obj (P in , P t ) + λ P f P (P in , P t )

[0073] wherein, f obj (P in , P t ) is the objective function, λ p is the penalty factor, and f P (P in , Pt ) is a penalty function, which contains the following three expressions:

[0074]

[0075]

[0076] S102. Use ISSA to solve the optimization model for minimizing the total energy consumption of the system. The specific steps are as follows:

[0077] S1021. Initialize the sparrow population size M, the maximum number of iterations m max , the problem dimension D, the discoverer ratio r f , the scout ratio r s , the warning threshold T A , the safety threshold T S , and initialize the sparrow position X using the following Sin chaotic mapping i =(P in ,P t ):

[0078]

[0079] S1022. Calculate the fitness value of each sparrow, find the current optimal fitness value and the worst fitness value, and the corresponding positions;

[0080] S1023. Select some sparrows with better fitness values as discoverers and update the discoverer positions according to the following formula:

[0081]

[0082] where, represents the position of the i-th sparrow in the j-th dimension at the m-th iteration, represents the optimal position of the sparrow in the j-th dimension at the m-th iteration, δ is a random number between (0,1), T A ∈[0,1], T S ∈[0,0.5] represent the warning value and the safety value respectively, Q is a random number obeying the normal distribution of [0,1], and w is the adaptive weight coefficient;

[0083] Calculate the adaptive weight coefficient w according to the following formula:

[0084]

[0085] S1024. The remaining sparrows are used as followers and their positions are updated according to the following formula:

[0086]

[0087] where, is the optimal position occupied by the discoverer in the (m + 1)-th iteration, is the position of the worst sparrow in the m-th iteration of the sparrow population; A represents a 1×D matrix, where each element is randomly assigned 1 or -1, and A + = A T (AA T ) -1 . L represents a 1×D matrix, where each element in the matrix is all 1;

[0088] S1025. Select a part of the sparrows as scouts according to the ratio r s and update their positions according to the following formula:

[0089]

[0090] where, is the globally optimal position in the m-th iteration, k is a random number in [-1, 1], f i is the fitness value of the current sparrow individual, f g and f w are the current globally best and worst fitness values respectively, and ε is the minimum constant to prevent the denominator from being 0;

[0091] S1026. Opposition-based learning and Cauchy mutation are used to perturb the optimal solution, expand the search area, and update its position according to the following formula:

[0092]

[0093] where, is the opposition solution of the globally optimal solution position in the m-th iteration, b 1 is the information exchange control parameter, caushy(0, 1) is the standard Cauchy distribution, P S is the selection probability, and δ is a random number between (0, 1);

[0094] Calculate the opposition solution of the globally optimal solution position in the m-th iteration according to the following formula:

[0095]

[0096] where, ub and lb are the upper and lower bounds respectively, and r is a 1×D random number matrix following the standard uniform distribution on (0, 1);

[0097] Calculate the information exchange control parameter according to the following formula:

[0098]

[0099] Calculate the selection probability according to the following formula:

[0100]

[0101] Among them, θ is the adjustment parameter;

[0102] S1027. Determine whether to update the position of the optimal solution according to the following formula:

[0103]

[0104] S1028. Determine whether the end condition is reached; if so, proceed to the next step, otherwise jump to step S1022;

[0105] S1029. The program ends and outputs the optimal result.

[0106] Compared with the prior art, the present invention has the following beneficial effects:

[0107] 1. The present invention considers the problem of ASNs and AUVs at different depths affected by ocean currents, uses mechanical analysis to study the influence of ocean currents on ASNs, determines the positions of ASNs, and determines the navigation direction of AUVs according to the synthesis of velocity vectors.

[0108] 2. The present invention adopts the MI communication method and MCR-WPT technology to solve the problems of low data transmission rate of traditional acoustic communication and insufficient energy of sensor nodes.

[0109] 3. The present invention jointly optimizes the transmission power of ASNs and the transmission power of AUVs to minimize the system energy consumption and improve the system performance. At the same time, compared with the benchmark algorithm, the present invention adopts the ISSA algorithm with a faster convergence speed and better global convergence ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 is the network structure diagram of the UWSN based on MI communication in an embodiment of the present invention;

[0111] Figure 2 is the flowchart of the resource allocation algorithm based on alternating iteration in an embodiment of the present invention;

[0112] Figure 3 is the flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0113] The technical solutions of the present invention will be specifically described below with reference to the drawings.

[0114] The present invention provides a resource allocation method for a UWSN based on MI communication in an ocean current scenario, including:

[0115] S1. Model the network structure of the UWSN based on magnetic induction MI communication;

[0116] S2. Model the offset positions of the modeling anchor sensor nodes ASNs;

[0117] S3. Model the equivalent circuit structure of the magnetic coupled resonance wireless energy;

[0118] S4. Model the energy consumed by the autonomous underwater vehicle AUV when charging the ASNs;

[0119] S5. Model the energy consumed by the ASNs when sending data to the AUV;

[0120] S6. Model the energy consumed by the AUV during navigation and hovering;

[0121] S7. Model the total sum of the energy consumption of the AUV during navigation, the hovering energy consumption, the charging loss, and the communication link energy consumption of the ASNs;

[0122] S8. Model the limiting conditions of the transmission power of the AUV and the ASNs;

[0123] S9. Model the optimization model for minimizing the total system energy consumption;

[0124] S10. Use the ISSA to solve the optimization model for minimizing the total system energy consumption.

[0125] The following is the specific implementation process of the present invention.

[0126] In view of the problems of low data rate and limited battery power in the traditional acoustic communication-based UWSN, the present invention studies a resource allocation strategy for UWSN based on MI communication in an ocean current scenario. Specifically, multiple ASNs are distributed in seawater at different depths. First, the AUV uses the MCR-WPT technology to charge the ASNs. Then, the ASNs transmit data to the AUV through MI communication. The influence of ocean currents on the ASNs and the AUV is analyzed. The GA-PSO algorithm is used to optimize the navigation trajectory of the AUV. After the AUV collects data from the SNs, it moves to a position under the ground base station and sends the collected data to the ground base station through MI communication. In order to reduce the system energy consumption, under the constraints of energy causality and transmission power, the transmission power of the AUV and the ASNs is jointly optimized. The ISSA algorithm is used for iterative solution to achieve the goal of minimizing the total system energy consumption. Compared with other solutions, this strategy can more effectively reduce the system energy consumption.

[0127] I. Network Model of UWSN Based on MI Communication

[0128] In this embodiment, a resource allocation strategy for UWSN based on MI communication in an ocean current scenario is proposed. The network structure diagram of the UWSN based on MI communication is as Figure 1As shown. In this system, a network structure of UWSN based on MI communication is constructed, including a surface base station, an AUV, and N ASNs. The AUV is used to charge the N ASNs and receive information transmitted from the ASNs. Each ASN contains a rechargeable battery, and the AUV can charge each ASN through MCR-WPT.

[0129] II. Establish a resource allocation model of UWSN based on MI communication under the ocean current scenario

[0130] Please refer to Figure 3 , a resource allocation strategy of UWSN based on MI communication proposed in this embodiment includes the following steps:

[0131] S1. Model the network structure of UWSN based on MI communication

[0132] The system network structure mainly includes ASNs, AUV, and surface base station. In this embodiment, a three-dimensional Cartesian coordinate system is adopted. The set of all ASNs is defined as S = {S 1 , S 2 , …, S N}. Assume that the size of the data collected by the AUV from each ASN is represented by the set I = {I 1 , I 2 , …, I N}. Assume that each ASN knows its anchor point coordinates as (x n , y n , 0) and the anchor chain length l i , L = {l 1 , l 2 , …, l N}. Assume an ocean with a constant depth H (flat bottom) and K different ocean current layers, and the height of the ocean current layer is ΔH = H / K. In the Kth layer, the velocity and direction of the ocean current are modeled as piecewise constant functions, which are v w (k) and k = 1, 2, …, K. In addition, for a more realistic situation, the velocity of the ocean current depends on the depth, and assume v w (k - 1) > v w (k).

[0133] S2. Model the offset position of ASNs

[0134] According to the following formula, calculate the offset position of the nth ASN:

[0135]

[0136] where, (x n,0 , y n,0 , zn,0 ), and (x n , y n , z n ) represent the ASNs coordinates of the static position and the ASNs coordinates of the offset position respectively. is the horizontal offset angle, which is equivalent to the angle between the ocean current layer where the current ASNs is located and the X-axis direction. α n represents the vertical offset angle, α n ∈(0, θ 2 . θ 1 and θ 2 are the maximum horizontal and vertical offset angles respectively.

[0137] According to the following formula, calculate α n as:

[0138]

[0139] where, F g = ρV S g is the gravity of the ASNs, ρ is the density of the ASNs, V s is the volume of the ASNs, and g is the acceleration of gravity of the earth. F b = ρ w V S g is the buoyancy of the seawater on the ASNs, ρ w is the density of the seawater. is the resistance of the ASNs to the ocean current, which is affected by the cross-sectional area A R of the ASNs, A R is the cross-sectional area of the ASNs, c is the resistance parameter of the object shape, and v w is the velocity of the ocean current.

[0140] S3. Modeling the equivalent circuit structure of magnetic coupled resonance wireless energy

[0141] When the transmitting coil and the receiving coil are both solenoid structures and are coaxially placed, the mutual inductance calculation formula between the coupling coils is In the formula, μ represents the magnetic permeability of seawater, N t and N r represent the number of turns of the transmitting coil and the receiving coil respectively, a t and a r represent the radii of the transmitting coil and the receiving coil respectively, and d represents the charging distance between the AUV and the ASNs.

[0142] If the system operating angular frequency is ω, then the self-impedance Z t of the transmitting coil and the self-impedance Z r of the receiving coil are respectively R Sis the equivalent internal resistance of the power supply, R L is the load resistance, R t and R r are the resistances of the transmitting and receiving coils, L t and L r are the self-inductances of the transmitting and receiving coils, C t and C r are the transmitting and receiving capacitors.

[0143] According to Kirchhoff's voltage law, the loop equation is listed as U S = Z t i 1 + jωM′i 2 , 0 = Z r i 2 + jωM′i 1 , where M′ is the mutual inductance between the coupled coils, U S is the voltage of the transmitter battery, i 1 and i 2 are the currents of the transmitting loop and the receiving loop respectively. And the solution is

[0144] When the system resonates, the resonant circuit only exhibits resistance characteristics, and the combined effect of capacitive reactance and inductive reactance is 0. At this time, the self-impedance of the transmitter and the self-impedance of the receiver are Z t = R s + R t , Z r = R L + R r . Then the input power P in and the output power P out can be defined as where Re{·} represents the real part of the imaginary number.

[0145] S4. Modeling the energy consumed by the AUV to charge the ASNs

[0146] According to the following formula, the energy consumed by the AUV to charge the nth ASN is calculated as:

[0147] E n,loss = E n,in - E n,out

[0148] where, E n,in = P n,in T n,out is the energy transmitted by the AUV to the nth ASN. The P n,in in the formula is the input power transmitted by the AUV to the nth ASN; is the energy received by the nth ASN from the AUV; The PL UMI = PLMI +PL EC is the total path loss; is the path loss in a lossless medium; is the medium loss in seawater; where σ and μ are the conductivity and permeability of seawater respectively; f is the operating frequency of the MCR-WPT system.

[0149] According to the following formula, calculate the charging time for the AUV to charge the nth ASNs:

[0150]

[0151] where the maximum energy of the ASNs battery is E max , and the remaining energy of the nth ASNs battery is E n,left .

[0152] S5. Model the energy consumed by the ASNs to send data to the AUV

[0153] According to the following formula, calculate the energy consumption of the nth ASNs to transmit data to the AUV:

[0154] E n,t =P n,t T n,MI

[0155] where, is the transmission power of the nth ASNs; is the time for the nth ASNs to transmit data to the AUV, and I in the formula n is the amount of data transmitted by the nth ASNs to the AUV; in the formula is the MI communication transmission rate of the nth ASNs, N 0 =kBT is the thermal noise power, B is the communication bandwidth of all ASNs MI communication, T is the Kelvin temperature, and k≈1.38×10 -23 J / K is the Boltzmann constant.

[0156] According to the following formula, calculate the energy consumption of the AUV to receive data from the nth ASNs:

[0157]

[0158] S6. Model the energy consumed by the AUV during navigation and hovering

[0159] According to the following formula, calculate the energy consumption of the AUV hovering at the nth ASNs:

[0160]

[0161] where, S AUVis the surface area of the AUV, η is the efficiency of the AUV propulsion system, ρ w is the density of water, c d is the drag coefficient of the AUV, is the magnitude of the velocity of the k-th ocean current layer where the AUV hovers, ||·|| 2 represents the two-norm of a vector, i.e., the magnitude of the vector.

[0162] The trajectory of the AUV was optimized using a Genetic Algorithm-Based Particle Swarm Optimization Algorithm (GA-PSO), i.e., finding a shortest trajectory that traverses N ASNs, and searching and sorting the set S = {S 1 , S 2 , …, S N} to minimize the energy consumption of the AUV's navigation.

[0163] According to the following formula, calculate the energy consumption of the AUV traversing all ASNs:

[0164]

[0165] Among them, is the magnitude of the navigation speed of the AUV in still water, T n,n+1 is the navigation time of the AUV from the n-th ASN to the n + 1-th ASN.

[0166] According to the following formula, calculate the navigation time of the AUV from the n-th ASN to the n + 1-th ASN:

[0167]

[0168] Among them, d n,n+1 (k) is the distance traveled by the AUV in the k-th ocean current layer, is the actual speed of the AUV in the k-th ocean current layer starting from the n-th ASN, and i, j respectively represent the ocean current layer numbers where the n-th and n + 1-th ASNs are located.

[0169] According to the following formula, calculate the distance traveled by the AUV in the k-th ocean current layer:

[0170]

[0171] Among them, h n,n+1 (k) is the vertical height from the upper bound of the ocean current layer where the AUV is located to the n-th ASN, h n,n+1 (j) is the vertical height from the n-th ASN to the n + 1-th ASN, d n,n+1 is the distance between the n-th ASN and the n + 1-th ASN.

[0172] Calculate the actual speed of the AUV starting from the n-th ASNs in the k-th layer of ocean currents according to the following formula:

[0173]

[0174]

[0175] where β is the and the included angle between, ω is the and the included angle between, S n and S n+1 respectively represent the position coordinates of the n-th ASNs and the (n + 1)-th ASNs, is the vector from the n-th ASNs to the (n + 1)-th ASNs.

[0176] S7. Model the total energy consumption of AUV navigation energy consumption, hovering energy consumption, charging loss, and ASNs communication link energy consumption

[0177] Calculate the total energy consumption of the system according to the following formula:

[0178]

[0179] S8. Model the preset constraints of AUV transmission power and ASNs transmission power

[0180] Model the limiting conditions of AUV transmission power, ASNs transmission power, and ASNs energy harvesting.

[0181] The limiting condition of AUV transmission power is: P in,min ≤P n,in ≤P in,max , n ∈ {1, 2, …, N}, P in,max =P in ;

[0182] The limiting condition of ASNs transmission power is: P t,min ≤P n,t ≤P t,max , n ∈ {1, 2, …, N},

[0183] The limiting condition of ASNs energy harvesting is: E n,out +E n,left ≥E n,t ;

[0184] where, P in,min and P t,min are the minimum transmission powers of the AUV and ASNs respectively, P in,max and P t,maxThey are the maximum transmission powers of the AUV and the ASNs respectively.

[0185] S9. Optimization model for minimizing the total energy consumption of the modeling system

[0186] After optimizing the AUV trajectory with the GA-PSO algorithm, the order in which the AUV traverses the ASNs is known. Then E voyage is a fixed value. Therefore, under the constraint conditions of the AUV transmission power, the ASNs transmission power, and the ASNs energy harvesting, the optimization model aims to minimize the system energy consumption and determine the optimized resource allocation strategy, that is where P in =(P 1,in , P 2,in , …, P N,in ) is the transmission power of the AUV for charging N ASNs, and P t =(P 1,t , P 2,t , …, P N,t ) is the transmission power of N ASNs for MI communication.

[0187] S10. Solving the optimization model for minimizing the total energy consumption of the system using ISSA

[0188] Use the ISSA algorithm to solve the optimization model for minimizing the total system energy consumption. The algorithm flow chart is as Figure 2 shown. The specific steps are as follows:

[0189] S101. First, for the optimization problem with constraint conditions, the inequality constraint problem is transformed into an unconstrained problem through the penalty function method, and a fitness function composed of the objective function and the penalty function is constructed. It is given by the following formula:

[0190] f i (P in , P t ) = f obj (P in , P t ) + λ P f P (P in , P t )

[0191] where f obj (P in , P t ) is the objective function, λ p is the penalty factor, and f P (P in , P t ) is the penalty function, which includes the following three formulas:

[0192]

[0193] S102. The following introduces the specific steps of the ISSA algorithm:

[0194] S1021. Initialize the sparrow population size M, the maximum number of iterations m max , the problem dimension D, the discoverer ratio r f , the scout ratio r s , the warning threshold T A , the safety threshold T S , and initialize the sparrow position X using the following Sin chaotic mapping i =(P in ,P t ):

[0195]

[0196] S1022. Calculate the fitness value of each sparrow, find the current optimal fitness value and the worst fitness value, and the corresponding positions.

[0197] S1023. Select some sparrows with better fitness values as discoverers, and update the discoverer positions according to the following formula:

[0198]

[0199] Among them, represents the position of the i-th sparrow in the j-th dimension at the m-th iteration, represents the optimal position of the sparrow in the j-th dimension at the m-th iteration, δ is a random number between (0,1), T A ∈[0,1], T S ∈[0,0.5] represent the warning value and the safety value respectively, Q is a random number obeying the normal distribution of [0,1], and w is the adaptive weight coefficient.

[0200] Calculate the adaptive weight coefficient w according to the following formula:

[0201]

[0202] S1024. The remaining sparrows are used as followers, and the follower positions are updated according to the following formula:

[0203]

[0204] Among them, is the optimal position occupied by the discoverer at the (m + 1)-th iteration, is the globally worst position of the sparrow at the m-th iteration. A represents a 1×D matrix, where each element is randomly assigned 1 or -1, and A + =A T (AAT ) -1 Let \(L\) be a \(1\times D\) matrix, where each element in the matrix is all \(1\).

[0205] S1025. Select a part of the sparrows as scouts according to the ratio \(r\) from the sparrows, and update the position according to the following formula: s Select a part of the sparrows as scouts according to the ratio \(r\) from the sparrows, and update the position according to the following formula:

[0206]

[0207] where is the position of the global optimum at the \(m\) -th iteration, \(k\) is a random number in \([-1,1]\), \(f\) i is the fitness value of the current sparrow individual, \(f\) g and \(f\) w are the current global best and worst fitness values respectively, and \(\varepsilon\) is the minimum constant to prevent the denominator from being \(0\).

[0208] S1026. Opposition - based learning and Cauchy mutation are used to perturb the optimal solution, expand the search area, and update its position according to the following formula:

[0209]

[0210] where is the opposition - based solution of the global optimal solution at the \(m\) -th iteration, \(b\) 1 is the information exchange control parameter, \(caushy(0,1)\) is the standard Cauchy distribution, \(P\) S is the selection probability, and \(\delta\) is a random number between \((0,1)\).

[0211] Calculate the opposition - based solution of the global optimal solution at the \(m\) -th iteration according to the following formula:

[0212]

[0213] where \(ub\) and \(lb\) are the upper and lower bounds respectively, and \(r\) is a \(1\times D\) random number matrix following the standard uniform distribution \((0,1)\).

[0214] Calculate the information exchange control parameter according to the following formula:

[0215]

[0216] Calculate the selection probability according to the following formula:

[0217]

[0218] where \(\theta\) is the adjustment parameter, and its value can be taken as \(0.05\).

[0219] S1027. Determine whether to update the position of the optimal solution according to the following formula:

[0220]

[0221] S1028. Determine whether the end condition is reached. If so, proceed to the next step; otherwise, jump to step S1022

[0222] S1029. The program ends and the optimal result is output.

[0223] The above are the preferred embodiments of the present invention. All changes made according to the technical solution of the present invention, when the functions and effects produced do not exceed the scope of the technical solution of the present invention, fall within the protection scope of the present invention.

Claims

1. A UWSN resource allocation method based on MI communication in an ocean current scenario, characterized in that: include: S1. Modeling the network structure of UWSN based on magnetic induction MI communication; S2, modeling the offset position of anchor sensor nodes ASNs; S3, Modeling the equivalent circuit structure of magnetically coupled resonant wireless energy; S4, modeling the energy consumed by autonomous underwater vehicles (AUVs) to charge ASNs; S5,modeling the energy consumed by ASNs to send data to AUV; S6, modeling the energy consumed by AUV navigation and hovering; S7, modeling the total energy consumption of AUV navigation, hovering, charging loss and ASNs communication link; S8, modeling the constraints of AUV transmission power and ASNs transmission power; S9, optimization model for minimizing the total energy consumption of the modeling system; S10. Use ISSA to solve the optimization model for minimizing the total energy consumption of the system.

2. According to claim 1, a UWSN resource allocation method based on MI communication in an ocean current scenario is characterized in that: The step S1 is specifically as follows: Step S11, constructing a network structure of UWSN based on MI communication, including a surface base station, an AUV and N ASNs; the AUV is used to charge the N ASNs and receive information transmitted from the ASNs; each ASN includes a rechargeable battery, and the AUV charges each ASN through magnetic coupling resonance wireless power transmission MCR-WPT; Step S12: The set of all ASNs is defined as S = {S1, S2, ..., S N },S n represents the nth ASN in the set, n∈[1,N]; assuming that the size of the data collected by the AUV from each ASN is represented by the set I={I1,I2,…,I N } indicates that each ASN knows its anchor point coordinates (x n ,y n ,0) and the anchor chain length l i ,L={l1,l2,…,l N }; an ocean with a constant depth H and K different current layers, the height of the current layer is ΔH = H / K; in the Kth layer, the speed and direction of the current are modeled as piecewise constant functions, respectively v w (k) and k = 1, 2, ..., K; In addition, for a more realistic situation, the speed of the ocean current depends on the depth, assuming that v w (k-1)>v w (k).

3. According to claim 2, a UWSN resource allocation method based on MI communication in an ocean current scenario is characterized in that: The step S2 is specifically as follows: According to the following formula, the offset position of the nth ASNs is calculated: Among them, (x n,0 ,y n,0 ,z n,0 ) and (x n ,y n ,z n ) represent the ASNs coordinates of the static position and the ASNs coordinates of the offset position respectively; is the horizontal deviation angle, which is equal to the angle between the ocean current layer where the ASNs are currently located and the X-axis direction. α n represents the vertical offset angle, α n ∈(0,θ2]; θ1 and θ2 are the maximum horizontal and vertical offset angles respectively; According to the following formula, calculate α n for: Among them, F g =ρV S g is the gravity of ASNs, ρ is the density of ASNs, V s is the volume of ASNs, g is the gravitational acceleration of the Earth; F b =ρ w V S g is the buoyancy of seawater on ASNs, ρ w is the density of seawater; is the resistance of ASNs to ocean currents, which is affected by the cross-sectional area A of ASNs. R The impact of A R is the cross-sectional area of ​​the ASNs, c is the drag parameter of the object shape, and v w It is the speed of the ocean current.

4. The UWSN resource allocation method based on MI communication in an ocean current scenario according to claim 2 is characterized in that: The step S3 is specifically as follows: When both the transmitting coil and the receiving coil are solenoid structures and are coaxially placed, the mutual inductance calculation formula between the coupled coils is: Where μ represents the magnetic permeability of seawater, N t and N r Respectively represent the number of turns of the transmitting coil and the receiving coil, a t and a r denote the radii of the transmitting coil and the receiving coil, respectively, and d denotes the charging distance between the AUV and the ASNs; If the system operating angular frequency is ω, the self-impedance Z of the transmitting coil is t and the self-impedance Z of the receiving coil r They are R S is the equivalent internal resistance of the power supply, R L is the load resistance, R t and R r is the resistance of the transmitting and receiving coils, L t and L r is the self-inductance of the transmitting and receiving coils, C t and C r are the transmitting capacitor and the receiving capacitor; According to Kirchhoff's voltage law, the circuit equation is listed as U S =Z t i1+jωM′i2,0=Z r i2+jωM′i1, where M′ is the mutual inductance between the coupled coils, U S is the voltage of the transmitter battery, i1 and i2 are the currents of the transmitting circuit and the receiving circuit respectively; and the solution is When the system resonates, the resonant circuit only exhibits resistance characteristics, and the combined effect of capacitive reactance and inductive reactance is 0. At this time, the self-impedance of the transmitter and the self-impedance of the receiver are Z t =R s +R t , Z r =R L +R r ; Then the input power P in And the output power P out Defined as where Re{·} represents the real part of the imaginary number.

5. The UWSN resource allocation method based on MI communication in an ocean current scenario according to claim 4 is characterized in that: The step S4 is specifically as follows: According to the following formula, the energy consumed by AUV to charge the nth ASNs is calculated as: AND n,loss =And n,in -AND n,out Among them, E n,in =P n,in T n,out is the energy transmitted from AUV to the nth ASNs, where P n,in is the input power transmitted by AUV to the nth ASNs; is the energy received by the nth ASNs from the AUV; PL UMI =PL MI +PL EC is the total path loss; is the path loss in lossless media; is the dielectric loss in seawater; where σ and μ are the electrical conductivity and magnetic permeability of seawater respectively; f is the operating frequency of the MCR-WPT system; The charging time of AUV for charging the nth ASNs is calculated according to the following formula: Among them, the maximum energy of ASNs battery is E max , the remaining energy of the battery of the nth ASNs is E n,left .

6. The UWSN resource allocation method based on MI communication in an ocean current scenario according to claim 5 is characterized in that: The step S5 is specifically as follows: The energy consumption of the nth ASNs transmitting data to the AUV is calculated according to the following formula: E n,t =P n,t T n,MI in, is the transmission power of the nth ASNs; is the time for the nth ASNs to transmit data to the AUV, where I n is the amount of data transmitted from the nth ASNs to the AUV; is the MI communication transmission rate of the nth ASNs, N0=kBT is the thermal noise power, B is the communication bandwidth of MI communication of all ASNs, T is the Kelvin temperature, k≈1.38×10 -23 J / K is the Boltzmann constant; The energy consumption of AUV receiving the nth ASNs data is calculated according to the following formula:

7. The UWSN resource allocation method based on MI communication in an ocean current scenario according to claim 6 is characterized in that: The step S6 is specifically as follows: The energy consumption of AUV hovering in the nth ASNs is calculated according to the following formula: Among them, S AUV is the surface area of ​​the AUV, η is the AUV propulsion system efficiency, ρ w is the density of water, c d is the drag coefficient of the AUV, is the velocity of the kth layer of ocean current where the AUV is hovering, ||·||2 represents the second norm of the vector, that is, the magnitude of the vector; The GA-PSO algorithm based on genetic algorithm is used to optimize the trajectory of AUV, that is, to find a shortest trajectory that traverses N ASNs, and the set S = {S1, S2, ..., S N } Perform search sorting to minimize the energy consumption of AUV navigation; The energy consumption of AUV traversing all ASNs is calculated according to the following formula: in, is the navigation speed of the AUV in still water, T n,n+1 is the navigation time of the AUV from the nth ASNs to the n+1th ASNs; The navigation time of the AUV from the nth ASNs to the n+1th ASNs is calculated according to the following formula: Among them, d n,n+1 (k) is the distance that the AUV travels in the kth layer of ocean current, is the actual speed of the AUV starting from the nth ASNs in the kth layer of ocean current, i and j represent the number of the ocean current layers where the nth and n+1th ASNs are located respectively; According to the following formula, the distance traveled by the AUV in the kth layer of ocean current is calculated: Among them, h n,n+1 (k) is the vertical height from the upper boundary of the ocean current layer where the AUV is located to the nth ASNs, h n,n+1 (j) is the vertical height from the nth ASNs to the n+1th ASNs, d n,n+1 is the distance between the nth ASNs and the n+1th ASNs; According to the following formula, the actual speed of the AUV starting from the nth ASNs in the kth layer of ocean current is calculated: Among them, β is and The angle between them, ω is and The angle between n and S n+1 Respectively represent the position coordinates of the nth ASNs and the n+1th ASNs, is the vector of ASNs from nth to n+1th ASNs.

8. The UWSN resource allocation method based on MI communication in an ocean current scenario according to claim 7, characterized in that: The step S7 is specifically as follows: According to the following formula, calculate the total energy consumption of the system: The step S8 is specifically as follows: The AUV transmission power limit condition is: P in,min ≤P n,in ≤P in,max , n∈{1,2,…,N},P in,max =P in ; The ASNs transmission power limit condition is: P t,min ≤P n,t ≤P t,max , The constraints of ASNs energy harvesting are: E n,out +E n,left ≥E n,t ; Among them, P in,min and P t,min are the minimum transmission power of AUV and ASNs, P in,max and P t,max are the maximum transmission powers of AUV and ASNs, respectively.

9. The UWSN resource allocation method based on MI communication in an ocean current scenario according to claim 8, characterized in that: The step S9 is specifically as follows: after the AUV trajectory is optimized by the GA-PSO algorithm, the order in which the AUV traverses the ASNs is known, then E voyage is a constant value, so the optimization model determines the optimal resource allocation strategy under the constraints of AUV transmission power, ASNs transmission power and ASNs energy collection, with the goal of minimizing system energy consumption, that is, Among them, P in =(P 1,in ,P 2,in ,…,P N,in ) is the transmission power of AUV charging N ASNs, P t =(P 1,t ,P 2,t ,…,P N,t ) is the transmission power of MI communication among N ASNs.

10. The UWSN resource allocation method based on MI communication in an ocean current scenario according to claim 9, characterized in that: The step S10 is specifically as follows: S101. For optimization problems with constraints, the problem with inequality constraints is transformed into an unconstrained problem by using the penalty function method, and a fitness function consisting of an objective function and a penalty function is constructed; it is given by the following formula: f i (P in ,P t )=f obj (P in ,P t )+λ P f P (P in ,P t ) Among them, f obj (P in ,P t ) is the objective function, λ p is the penalty factor, f P (P in ,P t ) is the penalty function, which contains the following three formulas: S102, using ISSA to solve the optimization model for minimizing the total energy consumption of the system, the specific steps are as follows: S1021, initialize the number of sparrow population M, the maximum number of iterations m max , Problem Dimension D, Proportion of Discoverers r f , Scout Ratio s , warning threshold T A , safety threshold T S , and use the following Sin chaotic map to initialize the sparrow position X i =(P in ,P t ): S1022, calculating the fitness value of each sparrow, finding the current optimal fitness value and the worst fitness value, and the corresponding position; S1023. Select some sparrows from the sparrows with better fitness values ​​as discoverers, and update the discoverer positions according to the following formula: in, represents the position of the i-th sparrow in the j-th dimension of the m-th iteration, represents the optimal position of the j-dimensional sparrow in the m-th iteration, a random number between δ∈(0,1), T A ∈[0,1], T S ∈[0,0.5] represents the warning value and safety value respectively, Q is a random number that obeys the [0,1] normal distribution, and w is the adaptive weight coefficient; According to the following formula, the adaptive weight coefficient w is calculated: S1024. The remaining sparrows act as followers, and the follower positions are updated according to the following formula: in, is the optimal position occupied by the discoverer in the m+1th iteration, is the global worst position of the sparrow in the mth iteration; A represents a 1×D matrix, in which each element is randomly assigned a value of 1 or -1, and A + =A T (AA T ) -1 . L represents a 1×D matrix, in which all elements in the matrix are 1; S1025, from the sparrows according to the ratio r s Select some sparrows as scouts and update their positions as follows: in, is the global optimal position of the mth iteration, k∈[-1,1] is a random number, f i is the fitness value of the current sparrow individual, f g and f w are the current global best and worst fitness values ​​respectively, and ε is the minimum constant to prevent the denominator from being 0; S1026, reverse learning and Cauchy mutation are used to perturb the optimal solution, expand the search area, and update its position by the following formula: in, is the reverse solution of the global optimal solution position in the mth iteration, b1 is the information exchange control parameter, caushy(0,1) is the standard Cauchy distribution, P S is the selection probability, a random number between δ∈(0,1); According to the following formula, the reverse solution of the global optimal solution position of the mth iteration is calculated: Among them, ub and lb are the upper and lower bounds respectively, and r is a 1×D random number matrix that obeys the (0,1) standard uniform distribution. The information exchange control parameter is calculated according to the following formula: The selection probability is calculated according to the following formula: Among them, θ is the adjustment parameter; S1027: Determine whether to update the position of the optimal solution according to the following formula: S1028, determine whether the end condition is met; if so, proceed to the next step, otherwise jump to step S1022; S1029. The program ends and the optimal result is output.

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