Multi-resource collaborative optimization method and system in underwater Internet of Things data acquisition system based on multi-mode communication

Abstract

The invention discloses a multi-mode communication-based multi-resource collaborative optimization method and system in an underwater Internet of Things data acquisition system. The method comprises the steps of constructing a UIoT data acquisition system communication model; acquiring a signal-to-noise ratio of the underwater acoustic channel according to the channel gain on the underwater acoustic channel and the variance of the environmental noise between the buoy collector and the USN; obtaining the signal-to-noise ratio of the water-optical channel according to the channel gain between the buoy collector and the USN on the water-optical channel; by jointly regulating and controlling four-dimensional communication resources of an underwater acoustic channel allocation variable, a water-optical channel allocation variable, an underwater acoustic power allocation variable and a water-optical power allocation variable, a mathematical representation model of a UIoT data acquisition system is constructed for an optimization problem of minimizing the total power overhead of all USNs in the system under the condition that the constraint of a service transmission rate is met; and solving the mathematical representation model of the UIoT data acquisition system to obtain a multi-resource collaborative optimization result of the UIoT data acquisition system. According to the invention, the data acquisition efficiency and accuracy of the UIoT data acquisition system are improved, and the power consumption of the system is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to a multi - resource collaborative optimization method and system in an underwater Internet of Things data acquisition system based on multi - mode communication, belonging to the field of wireless communication technology. Background Art

[0002] In recent years, with the increasing demand for marine resource exploration, environmental monitoring, and disaster prevention, the data acquisition of the underwater Internet of Things (UIoT) has received extensive attention from academia and industry. However, existing studies have all adopted a single underwater communication technology. The mainstream technologies include underwater acoustic communication (UAC) and underwater visible light communication (UVLC). However, the above - mentioned settings are difficult to meet the communication requirements in complex underwater environments, and the power consumption is generally high. Specifically, the transmission distance of UAC is long, reaching dozens of kilometers, but the supported peak communication rate is only dozens of Kbps, which cannot meet the requirements of high - rate services. While UVLC can support higher data transmission rates, usually reaching dozens of Mbps, suitable for the transmission of high - bandwidth data such as high - definition video and real - time data streams, its effective coverage range is only one or two hundred meters.

[0003] Therefore, in order to achieve efficient acquisition of marine data in complex underwater environments, it is necessary to combine UAC and UVLC in the UIoT data acquisition system, so that the sensing data of underwater sensor nodes (USN) can be collected through dual - mode communication to adapt to various underwater task requirements. The key to the design lies in how to design channel allocation and power allocation strategies to achieve a balance between data acquisition efficiency and power consumption. Summary of the Invention

[0004] The present invention provides a multi - resource collaborative optimization method in an underwater Internet of Things data acquisition system based on multi - mode communication. This method jointly optimizes channel allocation and power allocation to minimize the total power consumption of all USNs in the system, thereby improving data acquisition efficiency and reducing system power consumption.

[0005] The technical solution of the present invention is as follows:

[0006] According to the first aspect of the present invention, there is provided a multi - resource collaborative optimization method in an underwater Internet of Things data acquisition system based on multi - mode communication, including the following steps:

[0007] S1. Build the communication model of the UIoT data acquisition system; among them, the communication model of the UIoT data acquisition system takes the underwater Internet of Things data acquisition system as the research object. In the underwater Internet of Things data acquisition system, each USN deploys a transmitter at the same time. The transmitter sends data to the buoy collector through the underwater acoustic channel and the underwater optical channel at the same time, while the buoy collector deploys a receiver to receive the data; among them, the underwater Internet of Things data acquisition system includes one buoy collector and M USNs.

[0008] S2. Calculate the channel gain and the variance of the ambient noise on the underwater acoustic channel between the buoy collector and the USN.

[0009] S3. Obtain the signal-to-noise ratio of the underwater acoustic channel according to the channel gain and the variance of the ambient noise on the underwater acoustic channel between the buoy collector and the USN.

[0010] S4. Calculate the channel gain on the underwater optical channel between the buoy collector and the USN.

[0011] S5. Obtain the signal-to-noise ratio of the underwater optical channel according to the channel gain on the underwater optical channel between the buoy collector and the USN.

[0012] S6. By jointly regulating the four-dimensional communication resources of the underwater acoustic channel allocation variable, the underwater optical channel allocation variable, the underwater acoustic power allocation variable, and the underwater optical power allocation variable, under the constraint of the service transmission rate constructed according to the signal-to-noise ratio of the underwater acoustic channel and the signal-to-noise ratio of the underwater optical channel, construct a mathematical representation model of the UIoT data acquisition system for the optimization problem P1 of minimizing the total power consumption of all USNs in the system.

[0013] S7. Solve the mathematical representation model of the UIoT data acquisition system to obtain the multi-resource collaborative optimization result of the UIoT data acquisition system.

[0014] Further, the S2 is specifically: according to the transmission distance between the buoy collector and the USN and the operating frequency of the underwater acoustic channel, and in combination with the bandwidth of the underwater acoustic channel, calculate the channel gain on the i-th underwater acoustic channel between the buoy collector and the m-th USN; according to the operating frequency of the underwater acoustic channel, calculate the variance of the ambient noise on the i-th underwater acoustic channel between the buoy collector and the USN.

[0015] Further, the mathematical representation model of the UIoT data acquisition system has the following expression:

[0016]

[0017] Among them, represents the objective function, that is, the total power consumption of all USNs; the constraint C1 requires the underwater acoustic channel allocation variable is a binary value and restricts each underwater acoustic channel to be assigned to only one USN; Constraint C2 requires that the water - optical channel allocation variable is a binary value and restricts each water - optical channel to be assigned to only one USN; Constraint C3 requires that the underwater acoustic power allocation variable and the water - optical power allocation variable are non - negative values; Constraint C4 requires that the sum of the transmission rates of each USN on the underwater acoustic channel and the water - optical channel is not less than the minimum transmission rate requirement of the service where and represent the bandwidths corresponding to the i - th underwater acoustic channel and the j - th water - optical channel respectively, and represent the signal - to - noise ratios between the buoy collector and the m - th USN on the i - th underwater acoustic channel and the j - th water - optical channel respectively.

[0018] Furthermore, the specific steps for solving the mathematical representation model of the UIoT data acquisition system are as follows:

[0019] S7.1. According to the mathematical representation model of the UIoT data acquisition system, construct the sub - problem P2 and set the iteration number t = 1; then execute S7.2;

[0020] S7.2. Use the interior - point method to solve the sub - problem P2:

[0021] If the solution of the power allocation variable can be obtained by solving the sub - problem P2, take it as the first solution, and update the objective upper bound UB with the objective function value obtained by solving the sub - problem P2. Consider the sub - problem P2 as feasible, and then execute S7.3;

[0022] Otherwise, consider it as infeasible. When the sub - problem P2 is infeasible, construct the sub - problem P2 into a feasibility problem P3, and use the interior - point method to solve the feasibility problem P3 to obtain the solution of the power allocation variable as the second solution, and then execute S7.3;

[0023] S7.3. If the first solution is obtained at the current iteration number, add the optimality cut constraint generated by the first solution to construct the sub - problem P2; if the second solution is obtained at the current iteration number, add the feasibility cut constraint generated by the second solution to construct the feasibility problem P3; and according to the mathematical representation model of the UIoT data acquisition system, retain the constraints related to the binary channel allocation variable, and construct the master problem P4 containing only the binary channel allocation variable; use the interior - point method to solve the master problem P4 to obtain the solution of the binary channel allocation variable as the value of the binary channel allocation variable for the next iteration to solve the sub - problem / feasibility problem, and update the objective lower bound LB with the objective function value obtained by solving the master problem P4;

[0024] S7.4. Iteratively solve the sub-problem / feasibility problem and solve the main problem until convergence to obtain the multi-resource collaborative optimization result of the UIoT data acquisition system.

[0025] Further, based on the mathematical representation model of the UIoT data acquisition system, the sub-problem P2 is constructed as follows: Set the binary channel allocation variables in the mathematical representation model of the UIoT data acquisition system to the binary channel allocation variables obtained in the previous iteration (t-1). And the known feasible initial value is Therefore, given the binary channel allocation variables, construct a sub-problem P2 that only contains continuous power allocation variables.

[0026] Further, the convergence judgment is specifically as follows: Compare the difference between the current upper limit value and lower limit value of the objective with a preset threshold. When the difference between the two is less than or equal to the preset threshold, it is considered convergent.

[0027] According to the second aspect of the present invention, there is provided a multi-resource collaborative optimization system in an underwater Internet of Things data acquisition system based on multi-mode communication, including the module of the multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication described in any one of the above.

[0028] According to the third aspect of the present invention, there is provided a processor, and the processor is used to run a program, and when the program runs, it executes the multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication described in any one of the above.

[0029] The beneficial effects of the present invention are as follows: On the one hand, the present invention comprehensively considers the influence of various marine factors and effectively combines UAC and UVLC. By jointly regulating the four-dimensional communication resources of "underwater acoustic channel allocation variables, underwater optical channel allocation variables, underwater acoustic power allocation variables, and underwater optical power allocation variables" to minimize the total power consumption of all USNs in the system, the data acquisition efficiency and accuracy of the UIoT data acquisition system are improved, and the power consumption of the system is significantly reduced. On the other hand, the present invention decomposes the original problem into sub-problems and a main problem to solve the constructed mixed integer non-convex optimization problem, theoretically solving the complexity brought by binary variables and reducing the computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the present invention;

[0031] Figure 2 It is a schematic flow diagram for solving the mathematical representation model of the UIoT data acquisition system of the present invention;

[0032] Figure 3 ​UIoT data acquisition system communication model provided according to an embodiment of the present invention;

[0033] Figure 4 Convergence analysis of the proposed algorithm provided according to an embodiment of the present invention;

[0034] Figure 5 Show the total power overhead of each algorithm under different numbers of USNs provided according to an embodiment of the present invention. Specific implementation manners

[0035] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other arbitrarily.

[0036] Embodiment 1: As Figure 1-2 shown, according to the first aspect of the embodiment of the present invention, a multi-resource collaborative optimization method in an underwater Internet of Things data acquisition system based on multi-mode communication is provided, including the following steps:

[0037] S1. Construct a UIoT data acquisition system communication model; wherein, the UIoT data acquisition system communication model takes the underwater Internet of Things data acquisition system as the research object. In the underwater Internet of Things data acquisition system, each USN is simultaneously deployed with a transmitter, and the transmitter simultaneously sends data to the buoy collector through the underwater acoustic channel and the underwater optical channel, while the buoy collector is deployed with a receiver to receive data; wherein, the underwater Internet of Things data acquisition system includes one buoy collector and M USNs;

[0038] S2. Calculate the channel gain and the variance of the ambient noise between the buoy collector and the USN on the underwater acoustic channel;

[0039] S3. Obtain the signal-to-noise ratio of the underwater acoustic channel according to the channel gain and the variance of the ambient noise between the buoy collector and the USN on the underwater acoustic channel;

[0040] S4. Calculate the channel gain between the buoy collector and the USN on the underwater optical channel;

[0041] S5. Obtain the signal-to-noise ratio of the underwater optical channel according to the channel gain between the buoy collector and the USN on the underwater optical channel;

[0042] S6. By jointly regulating the four-dimensional communication resources of the underwater acoustic channel allocation variable, the underwater optical channel allocation variable, the underwater acoustic power allocation variable, and the underwater optical power allocation variable, a mathematical representation model of the UIoT data acquisition system is constructed for the optimization problem P1 of minimizing the total power overhead of all USNs in the system under the constraint of the service transmission rate constructed based on the signal-to-noise ratio of the underwater acoustic channel and the signal-to-noise ratio of the underwater optical channel.

[0043] S7. Solve the mathematical representation model of the UIoT data acquisition system to obtain the multi-resource collaborative optimization result of the UIoT data acquisition system.

[0044] Further, the specific content of S2 is as follows: According to the transmission distance between the buoy collector and the USN and the operating frequency of the underwater acoustic channel, and in combination with the bandwidth of the underwater acoustic channel, calculate the channel gain between the buoy collector and the m-th USN on the i-th underwater acoustic channel; According to the operating frequency of the underwater acoustic channel, calculate the variance of the ambient noise between the buoy collector and the USN on the i-th underwater acoustic channel.

[0045] Further, the mathematical representation model of the UIoT data acquisition system has the following expression:

[0046]

[0047] where represents the objective function, that is, the total power overhead of all USNs; Constraint C1 requires that the underwater acoustic channel allocation variable is a binary value and restricts that each underwater acoustic channel can only be allocated to one USN; Constraint C2 requires that the underwater optical channel allocation variable is a binary value and restricts that each underwater optical channel can only be allocated to one USN; Constraint C3 requires that the underwater acoustic power allocation variable and the underwater optical power allocation variable are non-negative values; Constraint C4 requires that the sum of the transmission rates of each USN on the underwater acoustic channel and the underwater optical channel is not less than the minimum service transmission rate requirement where and represent the bandwidths corresponding to the i-th underwater acoustic channel and the j-th underwater optical channel respectively, and represent the signal-to-noise ratios between the buoy collector and the m-th USN on the i-th underwater acoustic channel and the j-th underwater optical channel respectively.

[0048] Further, referring to Figure 2 , the specific steps for solving the mathematical representation model of the UIoT data acquisition system are as follows:

[0049] S7.1. Based on the mathematical representation model of the UIoT data acquisition system, construct the sub-problem P2, and set the number of iterations t = 1; then execute S7.2;

[0050] S7.2. Use the interior point method to solve the sub-problem P2:

[0051] If the solution of the power allocation variable can be obtained by solving the sub-problem P2, as the first solution, and update the upper bound value UB of the objective function with the objective function value obtained by solving the sub-problem P2, and consider the sub-problem P2 feasible, then execute S7.3;

[0052] Otherwise, consider it infeasible. When the sub-problem P2 is infeasible, construct the sub-problem P2 into a feasibility problem P3, and use the interior point method to solve the feasibility problem P3 to obtain the solution of the power allocation variable, as the second solution, then execute S7.3;

[0053] S7.3. If the first solution is obtained at the current iteration, then add the optimality cut constraint generated by the first solution to construct the sub-problem P2; if the second solution is obtained at the current iteration, then add the feasibility cut constraint generated by the second solution to construct the feasibility problem P3; and based on the mathematical representation model of the UIoT data acquisition system, retain the constraints related to the binary channel allocation variable, and construct the master problem P4 containing only the binary channel allocation variable; use the interior point method to solve the master problem P4 to obtain the solution of the binary channel allocation variable, as the value of the binary channel allocation variable for the next iteration to solve the sub-problem / feasibility problem, and update the lower bound value LB of the objective function with the objective function value obtained by solving the master problem P4;

[0054] S7.4. Iteratively solve the sub-problem / feasibility problem and solve the master problem until convergence to obtain the multi-resource collaborative optimization result of the UIoT data acquisition system.

[0055] Further, the construction of the sub-problem P2 based on the mathematical representation model of the UIoT data acquisition system is specifically as follows: set the binary channel allocation variable in the mathematical representation model of the UIoT data acquisition system to the binary channel allocation variable obtained in the previous iteration (t - 1) and the known feasible initial value is Therefore, given the binary channel allocation variable, construct the sub-problem P2 containing only the continuous power allocation variable of the sub-problem P2.

[0056] Further, the convergence judgment is specifically as follows: compare the difference between the current upper bound value and the lower bound value of the objective function with a preset threshold. When the difference between the two is less than or equal to the preset threshold, it is considered convergent.

[0057] According to a second aspect of the present invention, there is provided a multi-resource collaborative optimization system in an underwater Internet of Things (UIoT) data acquisition system based on multi-mode communication, including the module of the multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication described in any one of the above; specifically including: a first module for constructing a communication model of the UIoT data acquisition system; a second module for calculating the channel gain and the variance of ambient noise between the buoy collector and the USN on the underwater acoustic channel; a third module for obtaining the signal-to-noise ratio (SNR) of the underwater acoustic channel according to the channel gain and the variance of ambient noise between the buoy collector and the USN on the underwater acoustic channel; a fourth module for calculating the channel gain between the buoy collector and the USN on the underwater optical channel; a fifth module for obtaining the SNR of the underwater optical channel according to the channel gain between the buoy collector and the USN on the underwater optical channel; a sixth module for constructing a mathematical representation model of the UIoT data acquisition system for an optimization problem P1 of minimizing the total power consumption of all USNs in the system by jointly regulating four-dimensional communication resources including the underwater acoustic channel allocation variable, the underwater optical channel allocation variable, the underwater acoustic power allocation variable, and the underwater optical power allocation variable, under the constraint of the service transmission rate constructed based on the SNR of the underwater acoustic channel and the SNR of the underwater optical channel; a seventh module for solving the mathematical representation model of the UIoT data acquisition system to obtain the multi-resource collaborative optimization result of the UIoT data acquisition system.

[0058] According to a third aspect of the present invention, there is provided a processor, which is used to run a program, and when the program runs, it executes the multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication described in any one of the above.

[0059] According to a fourth aspect of an embodiment of the present invention, there is provided a computer-readable storage medium, which includes a stored program, and when the program runs, it controls the device where the computer-readable storage medium is located to execute the multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication described in any one of the above.

[0060] Embodiment 2: As Figure 1-5 shown, a multi-resource collaborative optimization method in an underwater Internet of Things data acquisition system based on multi-mode communication includes the following steps:

[0061] S1. Construct the communication model of the UIoT data acquisition system and initialize the system parameters. The communication model of the UIoT data acquisition system takes the underwater IoT data acquisition system as the research object. In the underwater IoT data acquisition system, each USN deploys both a transducer and a laser diode as transmitters at the same time. The transmitters send data to the buoy collector through both the underwater acoustic channel and the underwater optical channel simultaneously, while the buoy collector deploys both a hydrophone and a photodiode as receivers to receive data. Among them, the underwater IoT data acquisition system includes one buoy collector and M USNs. Define the sets of USN, underwater acoustic channel, and underwater optical channel as and The initialization of the system parameters includes the number of USNs, the number of underwater acoustic channels, the number of underwater optical channels, the transmission distance between the buoy collector and the USN, the operating frequency of the underwater acoustic channel, the operating frequency of the underwater optical channel, etc.

[0062] S2. Calculate the channel gain and the variance of the ambient noise on the underwater acoustic channel between the buoy collector and the USN.

[0063] The channel gain on the underwater acoustic channel between the buoy collector and the USN is specifically:

[0064] According to the transmission distance between the buoy collector and the USN and the operating frequency of the underwater acoustic channel, calculate the channel gain between the buoy collector and the m-th USN on the i-th underwater acoustic channel where is the bandwidth corresponding to the i-th underwater acoustic channel, f i U and f i L are respectively the upper and lower limits of the operating frequency of the i-th underwater acoustic channel, and f i L < f i U , A(d m , f) is the underwater acoustic channel fading model of the m-th USN, which can be characterized as a function of the transmission distance and the operating frequency f:

[0065]

[0066] where d m is the transmission distance between the buoy collector and the m-th USN, A0 is the fading constant, k is the expansion factor, 10log 10 a(f) is the absorption coefficient, which is given by the Thorp empirical formula:

[0067]

[0068] The variance of the ambient noise on the underwater acoustic channel between the buoy collector and the USN is specifically:

[0069] According to the operating frequency of the underwater acoustic channel, the variance of the ambient noise between the buoy collector and the USN on the \(i\)-th underwater acoustic channel is calculated as where \(N(f)=N_1(f)+N_2(f)+N_3(f)+N_4(f)\) is the total power spectral density of the ambient noise, which is usually composed of four sources: turbulence, shipping, sea waves, and thermal noise. Their respective power spectral densities can be characterized as functions of the operating frequency \(f\):

[0070] 10log 10 \(N_1(f)=17 - 30\log 10 f

[0071] 10log 10 \(N_2(f)=40 + 20(s - 0.5)+26\log 10 f

[0072] - 60\log 10 (f + 0.03)

[0073]

[0074] 10log 10 \(N_4(f)= - 15+20\log 10 f

[0075] where \(N_1(f)\), \(N_2(f)\), \(N_3(f)\), and \(N_4(f)\) represent the power spectral densities of turbulence, shipping, sea waves, and thermal noise, respectively; \(s\in[0,1]\) is the normalization degree of shipping activities, and \(w\) is the wind speed.

[0076] S3. Obtain the signal-to-noise ratio of the underwater acoustic channel;

[0077] According to the channel gain and the variance of the ambient noise between the buoy collector and the USN on the underwater acoustic channel, the signal-to-noise ratio of the underwater acoustic channel between the buoy collector and the \(m\)-th USN is obtained as:

[0078]

[0079] where, is the power allocation variable of the \(m\)-th USN on the \(i\)-th underwater acoustic channel.

[0080] S4. Calculate the channel gain between the buoy collector and the USN on the optical-acoustic channel;

[0081] The calculation of the channel gain between the buoy collector and the USN on the optical-acoustic channel is specifically as follows:

[0082] According to the influence of path loss, turbulence fading, and ocean misalignment on the water-optical channel when visible light signals propagate underwater, and combined with the electro-optical conversion rate η of the laser diode and the photoelectric response γ of the photodiode in the UIoT data acquisition system, the channel gain between the buoy collector and the m-th USN on the j-th water-optical channel is calculated as where is the path loss, D R is the receiver aperture, θ 1 / e is the full-width transmitter beam divergence angle, c is the extinction coefficient, ρ is the correction coefficient related to the water conditions and obtained through data fitting; h1 is the turbulence fading caused by weak turbulence, which follows a log-normal distribution, and the probability density function can be expressed as:

[0083]

[0084] where μ X and are the mean and variance of the statistic X = 0.5ln(h1), respectively. To ensure that the average power value of the turbulence fading h1 remains unchanged, it is necessary to normalize the fading amplitude and ensure that the expected value E[h1] = 1. It can be deduced that In addition, is the scintillation index, which is given by the following formula:

[0085]

[0086] where is the wave number, λ is the wavelength of light, κ is the spatial frequency, ζ is the normalized path length integration variable, and Φ(κ) is the spatial power spectrum of the ocean optical turbulence refractive index, which is related to the temperature and salinity of water. When the eddy thermal diffusion coefficient and the eddy salt diffusion coefficient are equal, its mathematical expression is:

[0087]

[0088] where ε is the turbulent kinetic energy dissipation rate per unit mass of fluid, α is the Kolmogorov microscale, χ T is the mean square temperature dissipation rate, ω is the relative intensity of temperature fluctuations and salinity fluctuations; A T , A S and A TS are all model parameters and are all positive constants;

[0089]

[0090] h2 is the ocean misalignment, and its probability density function f(h2) is:

[0091]

[0092] Among them, h0 and ξ are the ocean pointing constants of the underwater optical link.

[0093] S5. Obtain the signal-to-noise ratio of the water-optical channel;

[0094] According to the channel gain between the buoy collector and the USN on the water-optical channel, the signal-to-noise ratio of the water-optical channel between the buoy collector and the m-th USN is obtained as:

[0095]

[0096] Among them, is the power allocation variable of the m-th USN on the j-th water-optical channel, is the variance of the noise on the j-th water-optical channel.

[0097] S6. Construct a mathematical representation model of the UIoT data acquisition system;

[0098] The construction of the mathematical representation model of the UIoT data acquisition system is specifically as follows: In the communication model of the UIoT data acquisition system, by jointly regulating the underwater acoustic channel allocation variable water-optical channel allocation variable underwater acoustic power allocation variable water-optical power allocation variable the four-dimensional communication resources, and under the constraint of meeting the service transmission rate, a mathematical representation model of the UIoT data acquisition system is constructed for the optimization problem P1 of minimizing the total power overhead of all USNs in the system, and the expression is:

[0099]

[0100] Among them, represents the objective function, that is, the total power overhead of all USNs; the constraint C1 requires that the underwater acoustic channel allocation variable is a binary value and restricts that each underwater acoustic channel can only be allocated to one USN (if takes 1, it means that the i-th underwater acoustic channel is allocated to the m-th USN; if it takes 0, it means it is not allocated); the constraint C2 requires that the water-optical channel allocation variable is a binary value and restricts that each water-optical channel can only be allocated to one USN (if takes 1, it means that the j-th water-optical channel is allocated to the m-th USN; if it takes 0, it means it is not allocated); the constraint C3 requires that the power allocation variables and are non-negative values; the constraint C4 requires that the sum of the transmission rates of each USN on the underwater acoustic channel and the water-optical channel is not less than the minimum transmission rate requirement of the service Among them and respectively represent the bandwidths corresponding to the i-th underwater acoustic channel and the j-th underwater optical channel, and respectively represent the signal-to-noise ratios between the buoy collector and the m-th USN on the i-th underwater acoustic channel and the j-th underwater optical channel.

[0101] S7. Design an algorithm to effectively solve the mathematical representation model of the UIoT data acquisition system;

[0102] Since the optimization problem P1 in step S6 is a mixed-integer non-convex optimization problem, which includes binary channel allocation variables and continuous power allocation variables, and when the binary variables are given, this problem is a convex optimization problem only about continuous variables, so the present invention decomposes the optimization problem P1, which can be specifically as follows:

[0103] S7.1. According to the mathematical representation model of the UIoT data acquisition system, construct the subordinate problem P2 and set the iteration number t = 1; then execute S7.2; The construction of the subordinate problem P2 according to the mathematical representation model of the UIoT data acquisition system is specifically: set the binary channel allocation variables in the mathematical representation model of the UIoT data acquisition system as the binary channel allocation variables obtained in the previous iteration (t - 1) and the known feasible initial value is Therefore, when the binary channel allocation variables are given, construct a subordinate problem P2 that only contains continuous power allocation variables The subordinate problem P2 that only contains continuous power allocation variables is:

[0104]

[0105] where, respectively represent the optimal values of the channel allocation variables obtained in the (t - 1)-th iteration;

[0106] Through the above, it can be seen that only binary channel allocation variables are included in the constraints C1 and C2 of the optimization problem P1, so they are ignored in the subordinate problem P2. It can be analyzed that the subordinate problem P2 is a convex optimization problem about continuous variables The present invention uses the interior point method to solve this problem.

[0107] S7.2. Use the interior point method to solve the subordinate problem P2:

[0108] If the solution of the power allocation variable can be obtained from Problem P2, which is regarded as the first solution, and the objective function value obtained from solving Problem P2 is used to update the upper bound value UB, then Problem P2 is considered feasible, and then S7.3 is executed; otherwise, it is considered infeasible. When it is satisfied that Problem P2 is infeasible, Problem P2 is constructed into a feasibility problem P3, and the interior point method is used to solve the feasibility problem P3 to obtain the solution of the power allocation variable, which is regarded as the second solution, and then S7.3 is executed;

[0109] It should be noted that since the solution of Problem P2 is obtained when the binary variables are given, its optimal value is always greater than or equal to the optimal value of the optimization problem P1. Therefore, the solution of Problem P2 is always the upper bound value of the optimization problem P1. However, it is worth noting that not all binary channel allocation variables make Problem P2 feasible. When Problem P2 is infeasible, the corresponding feasibility problem P3 is constructed as follows:

[0110]

[0111] where Z m is the introduced auxiliary variable, representing the degree of violation of the m-th constraint. It can be seen that the feasibility problem P3 is always feasible, but when Problem P2 is feasible, the feasibility problem P3 will be skipped in the current iteration;

[0112] S7.3. If the first solution is obtained at the current iteration number, then the optimality cut constraint generated from Problem P2 is added with the first solution; if the second solution is obtained at the current iteration number, then the feasibility cut constraint generated from the feasibility problem P3 is added with the second solution; and according to the mathematical representation model of the UIoT data acquisition system, the constraints related to the binary channel allocation variables are retained, and the master problem P4 containing only the binary channel allocation variables is constructed; the interior point method is used to solve the master problem P4 to obtain the solution of the binary channel allocation variable, which is used as the value of the binary channel allocation variable for solving the problem / feasibility problem in the next iteration, and the objective function value obtained from solving the master problem P4 is used to update the lower bound value LB; the master problem P4 containing only the binary channel allocation variables is as follows:

[0113]

[0114] s.t.C1,C2

[0115]

[0116] where α is the introduced auxiliary variable, which is a continuous scalar, and C1, C2, and C8 are the constraints related to the binary channel allocation variables retained according to the mathematical representation model of the UIoT data acquisition system, where C8 corresponds to the deformation of C4; respectively represent those obtained in the t-th iteration respectively represent the first solution obtained after successfully solving the sub-problem for the p-th time, λ (p) represents the Lagrange multiplier generated by successfully solving the sub-problem for the p-th time; respectively represent the second solution obtained after solving the feasibility problem P3 for the q-th time, represents the Lagrange multiplier generated by solving the feasibility problem P3 for the q-th time, p + q = t; Constraint C9 is the optimality cut generated by the sub-problem P2, ψ is the set of sub-problem iteration times, Constraint C10 is the feasibility cut generated by the feasibility problem P3, φ is the set of feasibility problem iteration times, and in each iteration, according to whether the sub-problem is feasible, the optimality cut or the feasibility cut is added to the constraints of the master problem P4; In the t-th iteration, the Lagrangian functions in Constraints C9 and C10 of the master problem P4 are:

[0117]

[0118] wherein, and are respectively inferred from the sub-problem P2 and the feasibility problem P3. In each iteration, the feasible region of the master problem P4 is cut by the hyperplane space composed of Constraints C9 and C10 and gradually approaches the optimal solution of the binary variables;

[0119] S7.4. Iteratively solve the sub-problem or the feasibility problem, and solve the master problem until convergence to obtain the multi-resource collaborative optimization result of the UIoT data acquisition system.

[0120] Furthermore, the convergence judgment is specifically: compare the difference between the current upper bound value and the lower bound value of the objective with a preset threshold. When the difference between the two is less than or equal to the preset threshold, it is considered convergent.

[0121] Exemplarily, for the purpose of illustration, taking three iterations of convergence as an example, the process of further performing convergence judgment on UB and LB is described as follows: In the first iteration, execute the sub-problem to obtain the first solution, and further update UB, then solve the master problem and update LB; In the second iteration, execute the feasibility problem to obtain the second solution, then solve the master problem and update LB again; Then, at the beginning of the third iteration, perform convergence judgment, which is to take the difference between UB updated in the first iteration and LB updated in the second iteration, and compare the difference with the preset threshold ε. If UB - LB is less than or equal to ε, it is considered convergent and the result is output. It should be noted that the initial value of UB is set to positive infinity, and the initial value of LB is set to negative infinity, so as to satisfy non-convergence before the first iteration. In the embodiment of the present invention, ε is set to 10 -3 .

[0122] Applying the above technical solution, on the one hand, the present invention comprehensively considers the influence of various marine factors, effectively combines UAC and UVLC, and minimizes the total power consumption of all USNs in the system by jointly regulating the four-dimensional communication resources of "underwater acoustic channel allocation variable, underwater optical channel allocation variable, underwater acoustic power allocation variable, underwater optical power allocation variable", improving the data acquisition efficiency and accuracy of the UIoT data acquisition system and significantly reducing the power consumption of the system. On the other hand, according to the basic idea of generalized Benders decomposition, the constructed mixed-integer non-convex optimization problem is decomposed into a sub-problem containing only continuous variables and a master problem containing only binary integer variables, and the feasibility analysis of the sub-problem is carried out, and then iteratively solved and converged to the global optimal solution to obtain the resource allocation result of the UIoT data acquisition system, theoretically solving the complexity brought by binary variables and reducing the computational complexity.

[0123] Furthermore, it is illustrated as follows in combination with experimental data:

[0124] The present invention considers collecting the sensing data of USNs in a hemispherical shell-shaped underwater area with an inner radius and an outer radius of 10m and 500m respectively. All USNs are evenly distributed in this area. In order to be able to receive the optical signals emitted by all USNs, the buoy collector is located at the center of this area, and the field of view of the receiver of the buoy collector is set to 180°. A simulation experiment is carried out according to the specific experimental steps of the present invention, and the parameters involved are shown in Table 1.

[0125] Table 1 Parameter setting table

[0126]

[0127]

[0128] Figure 4 The convergence analysis of the proposed algorithm is shown. For the convenience of observation and to ensure the authenticity of the results, it is set to exit the loop only when the number of iterations is greater than the maximum number of iterations T = 20, and the test result is the mean of 100 samples. It can be seen from the figure that as the number of iterations increases, the upper limit value of the total power consumption of all USNs in the system decreases steadily, and the lower limit value increases steadily, and finally both tend to be unchanged. Due to system errors, the final upper limit value will be less than the lower limit value, but the absolute value of the difference between the upper limit value and the lower limit value is within the allowable error range, which indicates that the algorithm is convergent.

[0129] To further evaluate the performance of the algorithm proposed by the present invention, under 100 random network topologies, the present invention is compared with the following three comparison algorithms:

[0130] Comparison Algorithm 1: Relax the binary variables in problem P1 into continuous variables, solve the relaxed optimization problem containing only continuous variables using the SQP algorithm, then restore its binary characteristics using the greedy mechanism to obtain the optimal channel allocation variables. Then fix the channel allocation variables to the obtained optimal values, and use the interior point method to solve for the optimal power control variables.

[0131] Comparison Algorithm 2: Set the underwater acoustic channel allocation variables and the optical - water channel allocation variables as fixed values by means of random allocation, and then use the interior point method to solve for the underwater acoustic power control variables and the optical - water power control variables.

[0132] Comparison Algorithm 3: Set the underwater acoustic channel allocation variables and the optical - water channel allocation variables as fixed values by means of round - robin allocation, and then use the interior point method to solve for the underwater acoustic power control variables and the optical - water power control variables.

[0133] Figure 5 It shows the total power consumption of the proposed algorithm under different numbers of USNs (the numbers of USNs are taken as 6, 8, 10, 12 in sequence; the number of underwater acoustic channels is 12 and the number of optical - water channels is 12). To ensure the fairness of the comparison results, all test results are the means of 100 samples. It can be seen from the figure that: 1) As the number of USNs increases, the total power consumption of all algorithms shows an upward trend; 2) Compared with the other three comparison algorithms, the algorithm proposed in the present invention better handles the binary channel allocation variables in the model and optimizes the power allocation variables. Therefore, the total power consumption is generally lower than that of the other three comparison algorithms, significantly reducing the power consumption of the UIoT data acquisition system and improving the data acquisition efficiency.

[0134] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above - described embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the gist of the present invention.

Claims

1. A multi-resource collaborative optimization method in an underwater Internet of Things data acquisition system based on multi-mode communication, characterized in that: The following steps are involved: S1. Construct a UIoT data acquisition system communication model; the UIoT data acquisition system communication model takes the underwater Internet of Things data acquisition system as the research object. Each USN in the underwater Internet of Things data acquisition system deploys a transmitter at the same time. The transmitter sends data to the buoy collector through the hydroacoustic channel and the hydrooptical channel at the same time, and the buoy collector deploys a receiver to receive the data; the underwater Internet of Things data acquisition system includes a buoy collector and M USNs; S2, calculate the channel gain and the variance of the ambient noise in the hydroacoustic channel between the buoy collector and the USN; S3, obtaining the signal-to-noise ratio of the underwater acoustic channel according to the channel gain and the variance of the ambient noise on the underwater acoustic channel between the buoy collector and the USN; S4, calculating the channel gain between the buoy collector and the USN on the water optical channel; S5. Obtain the signal-to-noise ratio of the water-optical channel according to the channel gain between the buoy collector and the USN on the water-optical channel; S6. By jointly regulating the four-dimensional communication resources of underwater acoustic channel allocation variables, underwater optical channel allocation variables, underwater acoustic power allocation variables, and underwater optical power allocation variables, a mathematical representation model of the UIoT data acquisition system is constructed for the optimization problem P1 of minimizing the total power overhead of all USNs in the system under the constraints of the service transmission rate constructed based on the signal-to-noise ratio of the underwater acoustic channel and the signal-to-noise ratio of the underwater optical channel; S7. Solve the mathematical representation model of the UIoT data acquisition system and obtain the multi-resource collaborative optimization results of the UIoT data acquisition system.

2. The multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication according to claim 1 is characterized in that: The S2 is specifically: According to the transmission distance between the buoy collector and the USN and the operating frequency of the hydroacoustic channel, and combined with the bandwidth of the hydroacoustic channel, the channel gain between the buoy collector and the mth USN on the i-th hydroacoustic channel is calculated; According to the working frequency of the hydroacoustic channel, the variance of the environmental noise between the buoy collector and the USN on the i-th hydroacoustic channel is calculated.

3. The multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication according to claim 1 is characterized in that: The mathematical representation model of the UIoT data acquisition system is expressed as: in, represents the objective function, i.e., the total power cost of all USNs; constraint C1 requires that the underwater acoustic channel allocates variables is a binary value and restricts each underwater acoustic channel to be assigned to only one USN; constraint C2 requires that the variable is a binary value, and each water optical channel can only be assigned to one USN; constraint C3 requires that the water acoustic power allocation variable and water-optical power allocation variables is a non-negative value; Constraint C4 requires that the sum of the transmission rates of each USN on the underwater acoustic channel and the underwater optical channel is not less than the minimum transmission rate requirement of the service in and denote the bandwidths corresponding to the i-th underwater acoustic channel and the j-th underwater optical channel, respectively. and They represent the signal-to-noise ratio between the buoy collector and the mth USN on the ith hydroacoustic channel and the jth hydrooptical channel, respectively.

4. The multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication according to claim 1 is characterized in that: The specific steps of solving the mathematical representation model of the UIoT data acquisition system are: S7.

1. Based on the mathematical representation model of the UIoT data acquisition system, construct the problem P2 and set the number of iterations t=1; then execute S7.2; S7.

2. Solve problem P2 using the interior point method: If the solution of the power allocation variable can be obtained by solving problem P2, it is taken as the first solution, and the objective function value obtained by solving problem P2 is updated to the target upper limit value UB, and problem P2 is considered feasible, and then S7.3 is executed; Otherwise, it is considered infeasible. When the problem P2 is infeasible, the problem P2 is constructed into a feasibility problem P3. The feasibility problem P3 is solved by the interior point method to obtain the solution of the power allocation variable as the second solution, and then S7.3 is executed; S7.

3. If the first solution is obtained under the current number of iterations, then the first solution is added to construct the optimality cut constraint generated from problem P2; If the second solution is obtained under the current number of iterations, the feasibility cut constraint generated by constructing the feasibility problem P3 with the second solution is added; According to the mathematical representation model of the UIoT data acquisition system, the constraints related to the binary channel allocation variables are retained, and the main problem P4 containing only the binary channel allocation variables is constructed; the main problem P4 is solved by the interior point method to obtain the solution of the binary channel allocation variables, which is used as the value of the binary channel allocation variables for solving the slave problem / feasibility problem in the next iteration, and the objective function value obtained by solving the main problem P4 is used to update the target lower limit value LB; S7.

4. Iteratively solve the slave problem / feasibility problem and solve the master problem until convergence to obtain the multi-resource collaborative optimization result of the UIoT data acquisition system.

5. The multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication according to claim 1 is characterized in that: According to the mathematical representation model of the UIoT data acquisition system, the problem P2 is constructed, specifically: the binary channel allocation variable in the mathematical representation model of the UIoT data acquisition system is set to the binary channel allocation variable obtained in the previous iteration (t-1) And the feasible initial value is known to be Therefore, given the binary channel allocation variables, we construct a from question P2.

6. The multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication according to claim 1 is characterized in that: The convergence judgment is specifically as follows: comparing the difference between the current target upper limit value and the target lower limit value with a preset threshold value, and when the difference between the two is less than or equal to the preset threshold value, it is considered to be converged.

7. A multi-resource collaborative optimization system in an underwater Internet of Things data acquisition system based on multi-mode communication, characterized in that: A module comprising a multi-resource collaborative optimization method in an underwater Internet of Things data acquisition system based on multi-mode communication as described in any one of claims 1-6.

8. A processor, the processor being used to run a program, characterized in that: When the program is running, the multi-resource collaborative optimization method in the underwater Internet of Things data acquisition system based on multi-mode communication described in any one of claims 1 to 6 is executed.

Images