A target coverage deployment method for underwater wireless sensor networks based on seabed topography

Abstract

The present invention discloses a method for deploying underwater wireless sensor network target coverage based on seabed topography, comprising: considering the target coverage problem of the underwater wireless sensor network based on the seabed topography, constructing an integer nonlinear programming model (INLP); and solving the integer nonlinear programming model (INLP) using the Harris Eagle optimization algorithm. The present invention innovatively incorporates seabed topography features, uses the underwater acoustic simulator BELLHOP3D to calculate the loss of underwater acoustic signals, and further derives the transmission success rate of the link, thereby effectively improving the transmission quality of the underwater communication system. By constructing the integer nonlinear programming model, the deployment positions of underwater sensor nodes are optimized, maximizing coverage and uniformity, and resolving the problem that traditional underwater network deployment methods fail to fully consider the impact of complex seabed topography on signal transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater wireless sensor networks, and in particular to a target coverage deployment method of an underwater wireless sensor network based on seabed topography. Background Art

[0002] As an effective tool for human exploration of the deep ocean, underwater wireless sensor networks (UWSNs) have the advantages of fast transmission rate, low energy consumption, strong penetration and good environmental adaptability. They are widely used in marine disaster monitoring, seabed resource exploration, seabed-based monitoring and other fields.

[0003] The present invention is applied to underwater wireless sensor networks using a multi-hop transmission method. A group of submarine sensing nodes are deployed on the seabed (target) to collect various data on the seabed environment, such as oil, natural gas, marine life, and seabed activity. These submarine sensing nodes are also equipped with acoustic modules that can monitor and transmit the collected parameters in real time. To effectively utilize this data, it needs to be transmitted to a surface aggregation node on the sea surface, typically located on a buoy or vessel near the sea surface. The surface aggregation node is responsible for collecting, integrating, and transmitting data from the submarine sensing nodes and sending it to a ground station or data center for processing and analysis. To achieve this data transmission, a certain number of sensor nodes (network nodes) are deployed between the submarine sensing nodes and the surface aggregation node to form a network node set. The submarine sensing nodes transmit the collected data through this network to the nearest network node, which then transmits the data to a more distant network node, ultimately reaching the surface aggregation node. This process faces several challenges. First, the presence of complex seabed topography such as seamounts, seabed slopes, and seabed depressions can cause irregularities in sound propagation loss and even create large acoustic shadows. Secondly, underwater acoustic communication is susceptible to factors such as the Doppler effect, multipath, and ambient ocean noise. This noise can interfere with the normal operation of underwater acoustic communication systems, causing signal loss or errors, resulting in higher bit error rates than terrestrial radio communications. These factors collectively lead to poor coverage, reduced data transmission reliability, and poor network connectivity in underwater wireless sensor networks.

[0004] In recent years, the problem of target coverage in underwater acoustic sensor networks has been extensively studied. However, no recent research specifically addresses target coverage in UWSNs that considers seafloor topography. By focusing on recent research on target coverage in wireless sensor networks and urban networks, we have discovered that the underlying perception models of most algorithms in wireless sensor networks are based on Bresenham's line of sight (LOS) algorithm. For example, the IEEE TRANSACTIONS ON MOBILE COMPUTING paper "Toward a Realistic Approach for the Deployment of 3D Wireless Sensor Networks" proposes a realistic coverage model for 3D environments based on Bresenham's line of sight, taking both environmental factors and the physical space into account. This type of perception model is more suitable for describing radio signals that propagate along straight lines. However, underwater acoustic communication, currently the mainstream communication method between underwater wireless sensor nodes, cannot be well described using LOS. The underwater acoustics toolbox BELLHOP3D can be used to calculate the loss of underwater acoustic signals that considers seafloor topography. Based on this data, the link transmission success rate can be calculated. Based on this data, the overall network coverage can be improved by optimizing the deployment of network nodes to maximize coverage and other metrics. Considering that the introduction of terrain increases the difficulty of solving the problem, it is necessary to ensure that the designed algorithm can still find a high-quality solution while taking the terrain into account. At the same time, the introduction of terrain can also lead to uneven coverage, with different seabed sensing nodes being covered by the network at significantly different frequencies. Given these factors, a method for deploying underwater wireless sensor networks based on seabed terrain is urgently needed. This method can find an optimal deployment method while taking the seabed terrain into consideration, thereby improving the overall coverage efficiency of the wireless sensor network. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a target coverage deployment method for an underwater wireless sensor network based on seabed topography. The underwater acoustics toolbox BELLHOP3D is used to calculate the loss of underwater acoustic signals taking into account the seabed topography, and a new method for calculating the link transmission success rate is designed. Based on the sound field analysis in space and the characteristics of the distribution of seabed sensing nodes, the final deployment method can be adjusted as much as possible to achieve higher coverage quality. This method not only takes into account the existence of seabed topography, but also increases the network coverage of seabed sensing nodes on this basis and improves the uniformity of coverage.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] The present invention includes two stages: the target coverage problem of underwater wireless sensor networks considering the seabed topography, the construction of an integer nonlinear programming model INLP stage, and the deployment algorithm to solve the integer nonlinear programming model INLP stage. First, in the target coverage problem of underwater wireless sensor networks considering the seabed topography, the integer nonlinear programming model INLP stage is constructed, BELLHOP3D is used to calculate the loss TL of the underwater acoustic signal considering the seabed topography, and then the transmission signal-to-noise ratio SNR is calculated. The link transmission success rate TSR is calculated based on the signal-to-noise ratio, and the integer nonlinear programming model INLP is established with the average coverage in the probability space as the optimization target, and relevant constraints are set. Then, in the stage of solving the integer nonlinear programming model INLP by the deployment algorithm, the present invention designs an underwater node deployment method based on the Harris Hawk optimization algorithm. The population initialization strategy based on the sound field distribution and the Harris Hawk movement strategy based on the target and terrain information are used to improve the performance of the deployment algorithm. The deployment method generated by the above algorithm can produce better coverage effects under different seabed terrain conditions, and the coverage is more uniform. Specifically:

[0008] A target coverage deployment method for an underwater wireless sensor network based on seabed topography, comprising:

[0009] S1. Considering the target coverage problem of underwater wireless sensor networks based on seabed topography, an integer nonlinear programming model (INLP) is constructed. Specifically, the model includes:

[0010] S101. Calculate the success rate of underwater acoustic signal transmission taking into account the seabed topography;

[0011] S102. Define the optimization target cov;

[0012] S103. Convert the target coverage problem of the underwater wireless sensor network based on the seabed topography into an integer nonlinear programming model INLP and set relevant constraints;

[0013] S2. Use the Harris Eagle optimization algorithm to solve the integer nonlinear programming model INLP; specifically include:

[0014] S201. Gridding the entire solution space of the integer nonlinear programming model INLP, where the size of the grid depends on the accuracy of the two-dimensional elevation array ea corresponding to the deployment area;

[0015] S202. Set the encoding rules of the Harris Hawk optimization algorithm;

[0016] S203. Simulate all submarine sensing nodes to transmit underwater acoustic signals simultaneously, simulate the sound field distribution corresponding to the submarine sensing node set U, analyze the sound field distribution to obtain the propagation characteristics of the submarine sensing nodes after considering the terrain, and initialize the Harris Hawk population;

[0017] S204. Initialize a portion of Harris Hawks according to the sound field distribution, where each Harris Hawk represents a deployment method. The initial optimization target obtained by the initialized Harris Hawks is larger.

[0018] S205. Use the two-dimensional elevation array ea corresponding to the deployment area in combination with the Voronoi diagram to analyze the terrain characteristics around the submarine sensing node to calculate the coverage requirements of different submarine sensing nodes;

[0019] S206. Setting a Harris Hawk mobile method based on the distribution of seabed sensing nodes and their surrounding terrain information;

[0020] S207. Before the Harris Hawk optimization algorithm begins iteration, complete the initialization of part of the Harris Hawk through steps S203 and S204; then enter the calculation and iteration of the Harris Hawk optimization algorithm; at the end of each round of iteration, complete the adjustment of the Harris Hawk through steps S205 and S206. After the Harris Hawk optimization algorithm iteration is completed, the Harris Hawk with the highest optimization target cov value will be used as the final output solution of the Harris Hawk optimization algorithm, completing the target coverage deployment of the underwater wireless sensor network based on the seabed topography.

[0021] Furthermore, in step S101, the loss of the underwater acoustic signal taking into account the seabed topography is first calculated by BELLHOP3D, and the signal-to-noise ratio of the underwater acoustic signal taking into account the seabed topography is further calculated in combination with the empirical formula of ocean noise. Finally, the transmission success rate of the underwater acoustic signal taking into account the seabed topography is calculated taking into account BPSK modulation.

[0022] Furthermore, the optimization target cov is used to describe the deployed network node set V s The receiving capability of the underwater acoustic signal sent by the seabed sensing node is large. The larger the cov, the more comprehensive the information received from the seabed sensing node. The specific steps of defining the optimization target cov in step S102 are as follows:

[0023] First, we use the relevant formula in the probability space to quantitatively describe the network node set V s For different seabed sensing nodes u i The fusion reception probability cov i , and then find the network node set V s The average coverage of all submarine sensing nodes U is used as the optimization target cov, which represents the network node set V after the deployment operation. s For each seabed sensing node u i The average fusion reception probability of the emitted underwater acoustic signal.

[0024] Furthermore, in step S103, the integer nonlinear programming model INLP is used to find a deployment method that can maximize the optimization target cov value. The deployment method needs to meet the following three constraints at the same time: first, the altitude of the deployed network nodes is greater than the altitude of the current terrain to ensure the rationality of the solution; second, the corresponding deployment method cannot have coverage holes, that is, each submarine sensing node u i At least able to communicate with the network node set V s A node v in j Establish a reliable link; Finally, in order to ensure the network node set V s Overall connectivity, each network node v i can converge to the sea surface node v through several reliable links o Send data; the above constraints together ensure that each seabed sensing node u i There is a directed connection to the sea surface sink node v0.

[0025] Furthermore, in step S202, one Harris Hawk in the Harris Hawk optimization algorithm represents a possible deployment method. Since there is more than one Harris Hawk in the Harris Hawk population, the entire Harris Hawk population records several possible deployment methods.

[0026] Furthermore, in step S203, BELLHOP3D is used to simulate the sound field distribution corresponding to the seabed sensing node set U.

[0027] Furthermore, in step S204, the probability of a network node being initialized to a certain position is positively correlated with the sound intensity value of the corresponding position. Positions with large sound intensity values ​​can simultaneously receive signals transmitted by several seabed sensing nodes, or the signal-to-noise ratio corresponding to these positions with large sound intensity values ​​is relatively high, the transmission success rate is relatively high, and the corresponding initial optimization target of the Harris Hawk is larger.

[0028] Furthermore, in step S206, the dimensions of the solution vector and the vector of the seabed sensing node set are unified, and the center of the seabed sensing node set and the center of the network node set are respectively used as representatives of their respective sets, where the center of the seabed sensing node set is weighted by the coverage demand calculated in step S205, that is, the greater the coverage demand of a certain seabed sensing node, the shorter the distance between the center of the seabed sensing node set and the seabed sensing node; finally, the dimensions of the solution vector and the vector of the seabed sensing node set are unified; the vector difference between the center of the seabed sensing node set and the center of the network node set is added to each network node, completing a movement of a Harris hawk.

[0029] The present invention also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the steps of the underwater wireless sensor network target coverage deployment method based on seabed topography are implemented.

[0030] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the underwater wireless sensor network target coverage deployment method based on seabed topography.

[0031] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0032] The method of the present invention innovatively combines the characteristics of the seabed topography, uses the underwater acoustic simulator BELLHOP3D to calculate the loss of underwater acoustic signals, and further derives the transmission success rate of the link, thereby effectively improving the transmission quality of the underwater communication system. By constructing an integer nonlinear programming (INLP) model, this method optimizes the deployment location of underwater sensor nodes, maximizes coverage and uniformity, and solves the problem that traditional underwater network deployment methods cannot fully consider the impact of complex seabed topography on signal transmission. Specifically, the present invention has the following beneficial effects:

[0033] 1. Improved coverage accuracy and reliability: This paper is the first to investigate underwater target coverage using real-world seafloor topography. Using the underwater acoustics toolbox BELLHOP3D, this method analyzes the impact of seafloor topography on underwater acoustic propagation and calculates the transmission success rate of underwater communication links, thereby optimizing the overall coverage of underwater wireless sensor networks. Compared to traditional line-of-sight propagation models, this approach accounts for more realistic signal propagation losses, improving network coverage accuracy and reliability.

[0034] 2. Optimize network deployment and improve coverage uniformity: The method of the present invention fully considers the complexity of the distribution of seabed sensing nodes and the seabed terrain, and can efficiently solve the optimal deployment plan. By improving the Harris Hawk optimization algorithm, the improved Harris Hawk optimization algorithm will first initialize a part of the Harris Hawk according to the sound field distribution after considering the terrain in space, and then integrate the seabed sensing node position information and terrain data into the Harris Hawk optimization algorithm. Experimental results show that the method of the present invention exhibits higher solution quality and more uniform coverage when dealing with the target coverage problem of underwater wireless sensor networks considering terrain. The optimized Harris Hawk optimization algorithm of the present invention can adjust the node position more accurately in each iteration, ensuring that the coverage effect of each seabed sensing node is more uniform, avoiding the uneven coverage problem caused by uneven seabed terrain in traditional deployment methods.

[0035] 3. Enhanced system adaptability to complex submarine environments: By incorporating an integer nonlinear programming model and optimization algorithm based on submarine topography, this invention can automatically adjust the layout of network nodes in various complex submarine environments to accommodate different submarine sensing nodes and terrain features, significantly improving network connectivity and reliability. Whether in complex terrain such as submarine ridges, slopes, or depressions, it achieves excellent target coverage, ensuring stable operation of the underwater wireless sensor network.

[0036] 4. High computational efficiency and practical optimization results: By leveraging the Harris Eagle optimization algorithm, which leverages its strengths in solving complex optimization problems, this method improves deployment quality while ensuring computational efficiency. This method, especially when considering multiple factors (such as seabed topography and network node distribution), can provide fast and high-quality optimization solutions, demonstrating strong engineering application value.

[0037] In summary, the deployment method of the present invention significantly improves the target coverage efficiency and network connectivity of underwater wireless sensor networks while taking into account the seabed topography, overcomes the limitations of traditional methods, and has broad application prospects and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a schematic diagram of a three-dimensional underwater scene involved in this embodiment;

[0039] Figure 2 It is a diagram to visualize transmission loss;

[0040] Figure 3 It is a schematic diagram of a three-dimensional grid scene;

[0041] Figure 4 It is a Harris Eagle coding diagram;

[0042] Figure 5 It is a schematic diagram of prey coding;

[0043] Figure 6 It is a roulette strategy flowchart;

[0044] Figure 7 This is a schematic diagram of Voronoi partitioning of the terrain using seabed sensing nodes as generators;

[0045] Figure 8 is a schematic diagram of position-weighted average;

[0046] Figure 9 This is a pseudo-code diagram of Harris Hawk's movement method;

[0047] Figure 10 It is a visual diagram of Harris Hawk's movement method;

[0048] Figure 11 1 is a flow chart of a deployment method based on the Harris Hawk optimization algorithm in this embodiment. DETAILED DESCRIPTION

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] To facilitate the subsequent description of the specific technical solutions in the method for deploying underwater wireless sensor network target coverage based on seabed topography in this embodiment, the following definitions are first given:

[0051] |·|: Indicates the number of elements in a set.

[0052] v0: Sea surface aggregation node.

[0053] V s : A collection of network nodes, excluding sea surface aggregation nodes,

[0054] V: The set of network nodes, including the sea surface aggregation node, v0∈V.

[0055] v i :V s A network node in the.

[0056] Network Node v i Three-dimensional coordinates in space.

[0057] U: A collection of seabed sensing nodes.

[0058] u i : A seabed sensing node in U.

[0059] Seabed sensing node u i Three-dimensional coordinates in space.

[0060] (i,j): seabed sensing node u i or network node v i To network node v j transmission link.

[0061] SNR ij :Undersea sensing node u i or network node v i To network node v j The signal-to-noise ratio of the transmitted underwater acoustic signal.

[0062] P ij :Undersea sensing node u i or network node vi To network node v j The transmission success rate of sent data packets.

[0063] D: The set of submarine sensing nodes U and the set of network nodes V s The set of links between.

[0064] a ij : binary variable, if the seabed sensing node u i With network node v j There is a reliable transmission link (i, j) between them, then a ij =1, otherwise a ij =0.

[0065] E: The set of links between network nodes.

[0066] x ij : binary variable, if the network node v i To network node v through link (i, j) j Send a unit stream, then x ij =1, otherwise x ij =0.

[0067] c ij : binary variable, if the network node v i With network node v j There is a reliable transmission link (i, j) between them, then c ij =1, otherwise c ij =0.

[0068] ea: A two-dimensional elevation array corresponding to the deployment area. The triple (x, y, ea[x][y]) represents a point on the terrain. Vertically upward from this point is the seawater, and vertically downward from this point is the seabed geology.

[0069] G: The set of all grid points in the solution space.

[0070] g i : A grid point i in the set G.

[0071] Grid point g i Three-dimensional coordinates in space.

[0072] To describe the implementation method more clearly, consider an underwater three-dimensional scene such as Figure 1 As shown in the figure, there is a seabed terrain at the bottom of the scene, and several seabed sensing nodes are deployed on the seabed terrain, which is represented by the set U = {u1,u2…u |U|}, this set is the target in the underwater wireless sensor network target coverage problem based on seabed topography in this embodiment. The surface aggregation node is located on the sea surface. It is necessary to deploy underwater acoustic sensor nodes to complete the coverage of the target and transmit the underwater information perceived by the target to the surface aggregation node. The set of underwater acoustic sensor nodes is recorded as In this embodiment, it is called a network node set, and the underwater acoustic sensor node is fixed by an anchor chain. The seabed topography is described by a two-dimensional elevation array. The index of the two-dimensional elevation array is (x, y), and the corresponding array value is ea[x][y]. Therefore, the index and the corresponding array value together constitute the coordinate point (x, y, ea[x][y]) in space. It is a point on the terrain. The vertical direction of this point is the sea water, and the vertical direction downward is the seabed geology. The topology of the underwater wireless sensor network is composed of several directed communication links. This topology ensures that each seabed sensing node is connected to the sea surface aggregation node in a directed manner. In conjunction with the accompanying drawings, the specific method, structure, characteristics and function of the underwater wireless sensor network target coverage deployment method based on seabed topography provided in this embodiment are described in detail as follows.

[0073] S1. Considering the target coverage problem of underwater wireless sensor networks based on seabed terrain, an integer nonlinear programming model INLP is constructed:

[0074] S101: In this part, the transmission success rate of the communication link is first calculated considering the seabed topography, so as to derive the optimization target cov. In the underwater wireless sensor network target coverage problem based on seabed topography, the process of each seabed sensing node sending a data packet to the sea surface aggregation node is divided into two parts. The first part is the seabed sensing node u i Through the link (i, j) to a network node v in the underwater wireless sensor network j The collected data is transmitted, where (i, j)∈D, and the set D represents the set of links between the seabed sensing node and the network node; the second part is that the network node relays the received data packet to the sea surface aggregation node through several other nodes in the network. In the second part, the communication path of each network node sending a data packet to the sea surface aggregation node can be regarded as a set of links between network nodes, which is a subset of the link set E between network nodes. The transmission success rate (TSR) of each link is calculated. If the TSR of a link is greater than the preset threshold P0, then the link is considered to be reliable. For a directed link (i, j), when BPSK modulation is used, the transmission success rate from the seabed sensing node u i or network node v i Sent and sent by network node v j The transmission success rate P of the received 1 bit data ij It is expressed as follows:

[0075]

[0076] In the above formula, SNR ij Indicates the node u sensed from the seabed i or network node v i To network node v j The signal-to-noise ratio when sending data packets, when the sound source intensity (dB) and the communication frequency (Hz) are SL and f respectively, SNR ij It can be calculated by the following formula:

[0077] SNR ij =SL-TL ij -10log 10 N(f) (2)

[0078] Among them TL ij represents the seabed sensing node u i or network node v i To network node v j The propagation loss of the transmitted underwater acoustic signal after considering the seabed topography is simulated using BELLHOP3D, as shown in Figure 2 The figure shows a visualization of the transmission loss at a depth of 500 meters.

[0079] The environmental noise source can be defined by Gaussian statistics and continuous power spectrum density. N(f) is the ocean environmental noise, which is composed of turbulence noise N t (f) Ship noise N s (f) Wind and wave noise N w (f), thermal noise N th (f) composition, N(f) = N t (f)+N s (f)+N w (f)+N th (f), these four types of noise are calculated by the following formula, where f represents the communication frequency:

[0080]

[0081] where m s is the spreading activity factor, w s It's the wind speed.

[0082] S102: Define the optimization target cov and provide the calculation formula for the optimization target. In the underwater wireless sensor network target coverage problem based on seabed topography in this embodiment, cov is used to describe the overall receiving ability of the deployed underwater wireless sensor network for the underwater acoustic signals sent by all seabed sensing nodes. The larger the cov value, the more comprehensive the deployed network is in receiving the data sent by the seabed sensing nodes, and thus the better the coverage effect of the deployed network. i Represents all network nodes V s For the seabed sensing node u i The fusion reception probability of the sent underwater acoustic signal data is: i The coverage can be calculated by the following formula:

[0083]

[0084] Among them, P ij represents the seabed sensing node u i To network node v j The transmission success rate. cov is the optimization target, which represents the average fusion reception probability of the underwater acoustic signal emitted by each seabed sensing node in the network after deployment. Its calculation method is given by the following formula:

[0085]

[0086] S103: With the above definition and calculated optimization objectives, the following integer nonlinear programming model INLP is given:

[0087] Max cov (6)

[0088]

[0089] The purpose of the integer nonlinear programming model INLP in this embodiment is to find a deployment method that can maximize the optimization objective cov among all deployment methods that meet these constraints. The decision variable of the INLP model is v i ∈V s , the designed algorithm needs to continuously change the position of the deployed nodes to complete the solution of the optimal deployment method. Constraint (7) ensures that all deployed network nodes v i Altitude Are both greater than the altitude of the current terrain This ensures the rationality of the understanding. Coverage constraints (8) and (10) ensure that for any submarine sensing node, at least one reliable communication link can be used to send data to the network, where a ij is a binary variable, when u i With v jTSRP ij When it is greater than the threshold P0, a ij Set to 1, otherwise a ij = 0. The flow conservation constraints (9) and (11) guarantee that at least one unit flow can be generated from the network node v s Flows to the sea surface aggregation node v0, in other words, network node v s Data can be sent to the sea surface aggregation node v0 through several reliable communication links, where x ij is a binary variable, if the network node v i To network node v through link (i, j) j Send a unit stream and then have x ij =1, otherwise x ij = 0. Therefore, constraints (8) to (11) together ensure that any seabed sensing node u i Data can always be sent to the sea surface aggregation node v0 through at least one communication link, that is, each seabed sensing node u i There is a direction connection to the sea surface sink node v0.

[0090] In order to obtain a solution for the INLP model, the following three aspects have to be considered:

[0091] (1) It is difficult to find the best deployment method that maximizes the optimization objective because the number of feasible solutions is very large (a s | and the number of seabed sensing nodes |U| grows exponentially).

[0092] (2) The introduction of terrain increases the difficulty of solving the problem. The designed algorithm needs to be able to find a high-quality solution while taking the terrain into consideration.

[0093] (3) The introduction of terrain may bring about uneven coverage problems, and the coverage frequencies of different seabed sensing nodes vary greatly.

[0094] Considering the above analysis, this embodiment designs a deployment method based on the Harris Hawk optimization algorithm (CTC-HHO) to meet the above-mentioned problems of high solution quality and uniform coverage.

[0095] S2. Use Harris Eagle optimization algorithm to solve the integer nonlinear programming model INLP;

[0096] S201: In order to facilitate analysis and solution, the solution space of the entire integer nonlinear programming model INLP is gridded, such as Figure 3As shown, the size of the grid depends on the accuracy of the two-dimensional elevation array ea, that is, if the unit length of the x-coordinate and y-coordinate in the two-dimensional elevation array is 1m, then the unit length of the entire three-dimensional grid is also 1m. The set of all grid points is defined as the set G = {g1, g2…g |G|}, grid point g i The three-dimensional coordinates in space are

[0097] S202: A Harris Hawk represents a possible deployment method, using a 3×|V s |Matrix storage. Figure 4 As shown, the matrix has a total of |V s | row 3 columns, where row i represents network node v i Three-dimensional coordinates in space

[0098] Similarly, the encoding rules of prey in Harris Hawk optimization algorithm need to be given. First, we need to clarify the specific meaning of prey in Harris Hawk optimization algorithm. Prey in Harris Hawk optimization algorithm represents the optimal solution that can be found in the current number of iterations. After reaching a certain number of iterations, the algorithm will return to this solution. Therefore, its physical meaning in space is the same as that of Harris Hawk, and it also uses a 3×|V s |Matrix storage. Figure 5 As shown in Figure 2, the difference is that in this algorithm, the number of Harris's hawks is controlled by the population size, but there is only one prey. Since there is more than one Harris's hawk in the population, several possible deployment methods are recorded for the entire population.

[0099] S203: In the heuristic algorithm, the initialization of the population is very important for the subsequent iterative solution, which directly affects the convergence speed of the algorithm and the quality of the final result. A suitable population initialization can help the algorithm explore the problem space faster and help avoid falling into the local optimal solution. Therefore, the careful design and selection of the population initialization method is crucial to the performance and effect of the algorithm. In this embodiment, an improved Harris Hawk initialization strategy is proposed, which initializes a part of the Harris Hawk in the population according to the sound field distribution generated by the seabed sensing nodes in three-dimensional space.

[0100] use Represents the grid point g i The sum of the sound intensity values ​​at the grid point is calculated by the sum of the sound intensity values ​​generated by different seabed sensing nodes at the grid point using the following formula, where represents the seabed sensing node u i At the grid point g i The sound intensity value generated at .

[0101]

[0102] It can be concluded that each point in the grid has only one sound intensity value corresponding to it, so the grid point can be given a weight by the sound intensity value. The calculation formula is as follows:

[0103]

[0104] S204: After completing the analysis of the sound intensity field, we will now discuss how to rationally utilize the grid point weight data. Here, we will introduce the roulette wheel strategy in the genetic algorithm. First, we will briefly introduce the roulette wheel strategy:

[0105] In genetic algorithms, the roulette wheel strategy is a common selection operation used to select individuals for the next generation. Its basic concept is similar to gambling on a roulette wheel: each individual is assigned a corresponding section on the wheel, and the size of this section is proportional to the individual's fitness. When selecting individuals for the next generation, the wheel is divided into sections based on fitness, and the individual is then selected by randomly selecting a pointer.

[0106] It can be seen that the roulette strategy is a selection operation. In the problem of this embodiment, the roulette strategy is used to select grid points. All grid points are assigned to corresponding parts of the roulette wheel, and the size of this part is proportional to the size of the grid weight. During the grid point selection process, the position of the random pointer determines the selected grid point. The roulette wheel generation process is calculated using the following formula.

[0107]

[0108] Finally, the Harris Hawk population is initialized using the roulette wheel generated above. The number of populations is N. Each Harris Hawk is initialized through a loop, and α is used to control the initialization ratio, where α is a fixed value between 0 and 1. A random number s is generated using the random function rand(), where s∈[0,1]. If s≤α, the current Harris Hawk is initialized based on the sound field, otherwise the current Harris Hawk is initialized randomly. The initialization steps based on the sound field are as follows: each node in the current Harris Hawk is initialized through a loop. The grid point where the node is initialized in the solution space is determined by the roulette wheel strategy. A random number r is generated using the rand() function, where r∈(0,1). Then r must point to a certain position on the roulette wheel, i.e., R i-1 <r≤R i , which means that the node in the Harris Hawk is initialized to the grid point g i Coordinates in three-dimensional space The algorithm process is as follows Figure 6 shown.

[0109] S205: Perform Voronoi partitioning on the entire terrain using the seabed sensing node set U as the generator. Figure 7 The Voronoi partitioning is performed on the two-dimensional elevation array using the distribution of seabed sensing nodes as the generator. i The corresponding set of elevation points inside the polygon is defined as in Represents the set VN i An element in , that is, an elevation point in the two-dimensional elevation array ea, is expressed as In order to facilitate subsequent calculations, the set VN i The seabed sensing node u is not included i The three-dimensional coordinates of Figure 7 marks the V polygon set VN1 generated by the seabed sensing node u1.

[0110] With the above definition, the calculation method of the submarine sensing node coverage requirement is given. There are many factors that affect the sound propagation of the submarine sensing node. In order to simplify this process, the algorithm here gives two factors. Factor 1 is the altitude of the terrain around the submarine sensing node. The higher the altitude around the submarine sensing node, the more difficult it is for the sound signal to propagate and the more restricted the sound line is. Factor 2 is the complexity of the terrain around the submarine sensing node. The variance is used here to measure it. The larger the variance, the more complex the terrain around the submarine sensing node, and vice versa. i The calculation method of the acoustic propagation factor 1 is given by formula (16), which is calculated by calculating the average altitude of the terrain around the seabed sensing node The altitude of the seabed sensing node Since the difference involves the subsequent weight calculation and normalization processing, the calculation results less than 0 are set to 0. The calculation method of the sound propagation factor 2 is given by formula (17), which reflects the complexity of the terrain around the seabed sensing node.

[0111]

[0112]

[0113] Use β to control the influence of sound propagation factor 1 and sound propagation factor 2 on the overall factor. From formula (18), we can see that factor The larger the seabed sensing node u is, the i The more easily the transmitted underwater acoustic signal is restricted by the terrain, the greater its coverage requirement is. Formula (19) is used to calculate the seabed sensing node u i Factors affecting sound propagation The proportion of all submarine sensing nodes. At this step, different submarine sensing nodes u have been analyzed. i All submarine sensing nodes U have different coverage requirements.

[0114]

[0115] S206: Use the weighted average position v of all network nodes in the network node set center To represent this network node set, use the weighted average position u of all seabed sensing nodes in the seabed sensing node set center To represent this set of seabed sensing nodes. Figure 8 This process is described, noting that the default weight in the weighted average of the network node position is 1, but the weight in the weighted average of the seabed sensing node position is calculated as According to the above The physical meaning of the description shows that when The larger the value, the lower the seabed sensing node u i The greater the coverage requirement, the greater the u center Will be closer to u i .

[0116] Got v center and u center , and then calculate the Euclidean distance between the two in space, and then calculate the move vector MoveVector, using the absolute value of the distance and the escape energy of the prey in the Harris Hawk optimization algorithm as a scaling factor, which means u center and v center The farther the distance, the greater the movement amplitude. At the same time, the more iterations of the algorithm, the smaller the movement amplitude. This is because in the early iteration of the algorithm, the entire population is more exploratory and the movement distance is larger. As the number of iterations increases, the algorithm enters the convergence stage, so the movement distance decreases accordingly. Figure 9 The pseudo code of this process is given. Figure 10 Visualize this process in three dimensions.

[0117] S207: Harris Hawk optimization algorithm can be divided into two stages:

[0118] Exploration phase and development phase; the value of e decreases with the increase of the number of iterations, e represents the degree of closeness to the optimal solution, calculated using formula (20), J represents the jump intensity, calculated using formula (21), T represents the maximum number of iterations, t represents the number of completed iterations, and e0 represents the initial energy state.

[0119]

[0120] J = 2×(1 - rand()) (21)

[0121] Exploration stage:

[0122] If |e|≥E1, it means that the optimal deployment method is far from the current solution, where E1 is a preset energy value. At this time, the algorithm enters the exploration stage (22):

[0123]

[0124] Here, q is the probability of choosing different change methods for different deployment methods. X(t + 1) represents the position vector of this deployment method in the upcoming iteration t + 1. X rabbit (t) is the best deployment method found so far, and X(t) is the current position vector. Variables r1, r2, r3, r4, and q are randomly selected, with values between 0 and 1, and are updated in each iteration. LB and UB represent the lower and upper bounds of the variables. X rand (t) is a deployment method randomly selected from the current population, and X m (t) is the average position of all deployment methods in the current population, calculated as follows, where N is the number of deployment methods in the population:

[0125]

[0126] Exploitation stage:

[0127] If |e| < E1, it means that the optimal deployment method is close to the current solution. The algorithm enters the exploitation stage, and there are four different change strategies in the exploitation stage, namely soft siege, hard siege, soft siege with fast progressive dive, and hard siege with fast progressive dive. Let a random variable p between 0 and 1 represent whether the prey has escaped the pursuit of the Harris hawk. p < P1 describes the possibility that the prey escapes the Harris hawk, while p ≥ P1 describes the possibility that the hawk captures the prey, where P1 is a preset escape possibility value.

[0128] Soft siege: If the energy level |e|≥E2 and p≥P1, the prey has enough energy to escape the pursuit of the hawk through some random and misleading actions, where E2 is a preset energy value in the exploitation stage. In this strategy, the Harris hawks gather around the prey until it is exhausted, and then launch a raid, which is the so-called soft siege, calculated using formula (24).

[0129] X(t + 1) = ΔX(t) - e|JX rabbit (t) - X(t)| (24)

[0130] ΔX(t) = X rabbit (t) - X(t) (25)

[0131] Hard Siege: If the energy level |e| < E2 and p ≥ P1, the prey does not have enough energy to escape and is exhausted, and the Harris hawk can simply pounce to capture the prey. This strategy is called hard siege and is calculated using formula (26).

[0132] X(t + 1) = X rabbit (t) - e|ΔX(t)| (26)

[0133] Soft Siege with Fast Progressive Dive: If the energy level |e| ≥ E2 and p < P1, the prey has enough energy to successfully escape from the hawk through some movement, which can be mathematically described using the Levy Flight function. In this case, the Harris hawk tries to perfectly aim at the prey but fails to catch it. Considering the deceptive movements of the prey, it makes multiple fast dives. This strategy is called soft siege with fast progressive dive and is calculated using formulas (27), (29), (30), and (31).

[0134] Hard Siege with Fast Progressive Dive: If the energy level |e| < E2 and p < P1, the prey is exhausted and has no energy to escape. In this case, a hard encirclement is built before the raid to capture and kill the prey while shortening the distance to the average position of the prey. This strategy is called hard siege with fast progressive dive. Formulas (27) and (28) correspond to the two kinds of sieges, where S is a random vector with dimension of 3×|V s |, and LF(x) represents the Levy flight function, which is calculated using formula (31).

[0135] Y = X rabbit (t) - e|JX rabbit (t) - X(t)| (27)

[0136] Y = X rabbit (t) - e|JX rabbit (t) - X m (t)| (28)

[0137] Z = Y + S×LF(|V s |) (29)

[0138]

[0139] Figure 11A flowchart of the CTC-HHO deployment method based on the Harris Hawk optimization algorithm in this embodiment is provided. This method uses the Harris Hawk optimization algorithm as its framework. Before the Harris Hawk optimization algorithm begins iteration, steps S203 and S204 initialize a portion of the Harris Hawk. The Harris Hawk optimization algorithm then begins calculation and iteration. At the end of each iteration, steps S205 and S206 fine-tune the Harris Hawk. After the algorithm completes, the Harris Hawk with the highest optimization objective cov value is used as the final output solution.

[0140] Preferably, the embodiments of the present application further provide a specific implementation of an electronic device capable of implementing all steps of the method for target coverage deployment of an underwater wireless sensor network based on seabed topography in the above embodiment, wherein the electronic device specifically includes the following contents:

[0141] Processor, memory, communications interface, and bus;

[0142] Among them, the processor, memory, and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between related devices such as server-side devices, metering devices, and user-side devices.

[0143] The processor is used to call the computer program in the memory, and when the processor executes the computer program, it implements all the steps in the underwater wireless sensor network target coverage deployment method based on seabed topography in the above embodiment.

[0144] An embodiment of the present application also provides a computer-readable storage medium capable of implementing all steps of the underwater wireless sensor network target coverage deployment method based on seabed topography in the above-mentioned embodiment. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements all steps of the underwater wireless sensor network target coverage deployment method based on seabed topography in the above-mentioned embodiment.

[0145] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the hardware + program embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0146] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0147] Although the present application provides method operation steps such as embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many steps and does not represent the only execution order. When an actual device or client product is executed, it can be executed in the order shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0148] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0151] The present invention is not limited to the embodiments described above. The above description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention. The above specific embodiments are merely illustrative and not restrictive. Without departing from the scope of the present invention and the scope of protection of the claims, those skilled in the art may make various specific modifications based on the teachings of the present invention, all of which fall within the scope of protection of the present invention.

Claims

1. A method for target coverage deployment of an underwater wireless sensor network based on seabed topography, characterized in that: include: S1. Considering the target coverage problem of underwater wireless sensor networks based on seabed topography, an integer nonlinear programming (INLP) model is constructed. Specifically, the model includes: S101. Calculate the success rate of underwater acoustic signal transmission taking into account the seabed topography; S102. Define the optimization target cov; the optimization target cov is used to describe the set of deployed network nodes The receiving capability of the underwater acoustic signal sent by the seabed sensing node is large. The larger the cov, the more comprehensive the information received from the seabed sensing node. The specific steps of defining the optimization target cov in step S102 are as follows: First, we use the relevant formula in the probability space to quantitatively describe the set of network nodes. Different seabed sensing nodes The fusion reception probability , and then find the network node set For all seabed sensing nodes The average coverage of the network node after the deployment operation is represented by the average coverage as the optimization target cov. For each seabed sensing node The average fusion reception probability of the emitted underwater acoustic signal; S103. Convert the target coverage problem of underwater wireless sensor networks based on seabed topography into an integer nonlinear programming (INLP) model, setting constraints related to altitude, coverage holes, and connectivity. S2. Solve the integer nonlinear programming model INLP using the Harris Eagle optimization algorithm; specifically: S201. The entire solution space of the integer nonlinear programming model INLP is gridded, where the size of the grid depends on the accuracy of the two-dimensional elevation array ea corresponding to the deployment area; S202. Set the encoding rules for the Harris Hawk optimization algorithm; S203. Simulate all seabed sensing nodes to emit underwater acoustic signals simultaneously, simulating a seabed sensing node set The corresponding sound field distribution is analyzed to obtain the propagation characteristics of the seabed sensing node after considering the terrain, and the Harris Hawk population is initialized; S204. Initialize a portion of Harris Hawks according to the acoustic field distribution. Each Harris Hawk represents a deployment method. The initial optimization target obtained by the initialized Harris Hawks is larger. S205. Using the two-dimensional elevation array ea corresponding to the deployment area and the Voronoi diagram, analyze the terrain characteristics around the submarine sensing node to calculate the coverage requirements of different submarine sensing nodes; S206 sets the Harris Hawk mobile method based on the distribution of seabed sensing nodes and their surrounding terrain information; S207. Before the Harris Hawk optimization algorithm begins iteration, steps S203 and S204 initialize a portion of the Harris Hawk. The Harris Hawk optimization algorithm then begins calculation and iteration. At the end of each iteration, steps S205 and S206 adjust the Harris Hawk. After the Harris Hawk optimization algorithm iterations are complete, the Harris Hawk with the highest optimization target cov value is used as the final output solution of the Harris Hawk optimization algorithm, completing the target coverage deployment of the underwater wireless sensor network based on seabed topography.

2. The method for deploying underwater wireless sensor network target coverage based on seabed topography according to claim 1, characterized in that: In step S101, the loss of the underwater acoustic signal taking into account the seabed topography is first calculated using BELLHOP3D, and the signal-to-noise ratio of the underwater acoustic signal taking into account the seabed topography is further calculated using the empirical formula of ocean noise. Finally, the transmission success rate of the underwater acoustic signal taking into account the seabed topography is calculated taking into account BPSK modulation.

3. The method for deploying underwater wireless sensor network target coverage based on seabed topography according to claim 1, characterized in that: In step S103, the integer nonlinear programming model INLP is used to find a deployment method that can maximize the optimization target cov value. The deployment method needs to meet the following three constraints at the same time: first, the altitude of the deployed network nodes must be greater than the altitude of the current terrain to ensure the rationality of the solution; second, the corresponding deployment method must not have coverage holes, that is, each submarine sensing node At least able to gather with network nodes A node in Establish a reliable link; finally, to ensure the network node collection Overall connectivity, every network node All nodes can be gathered on the sea surface through several reliable links Send data; The above constraints together ensure that each seabed sensing node To the sea surface gathering node It is directed connected.

4. The method for target coverage deployment of an underwater wireless sensor network based on seabed topography according to claim 1, characterized in that: In step S202, one Harris Hawk in the Harris Hawk optimization algorithm represents one deployment method. Since there is more than one Harris Hawk in the Harris Hawk population, the entire Harris Hawk population records several deployment methods.

5. The method for target coverage deployment of an underwater wireless sensor network based on seabed topography according to claim 1, characterized in that: In step S203, BELLHOP3D is used to simulate the seabed sensing node set Corresponding sound field distribution.

6. The method for target coverage deployment of an underwater wireless sensor network based on seabed topography according to claim 1, characterized in that: In step S204, the probability of a network node being initialized to a certain position is positively correlated with the sound intensity value of the corresponding position. Positions with large sound intensity values ​​can simultaneously receive signals transmitted by several seabed sensing nodes, or the signal-to-noise ratio corresponding to these positions with large sound intensity values ​​is high, the transmission success rate is high, and the corresponding initial optimization target of the Harris Hawk is larger.

7. The method for deploying underwater wireless sensor network target coverage based on seabed topography according to claim 1, characterized in that: In step S206, the dimensions of the solution vector and the vector of the seabed sensing node set are unified, and the center of the seabed sensing node set and the center of the network node set are respectively used as representatives of their respective sets. The center of the seabed sensing node set is weighted by the coverage demand calculated in step S205, that is, the greater the coverage demand of a certain seabed sensing node, the shorter the distance between the center of the seabed sensing node set and the seabed sensing node; finally, the dimensions of the solution vector and the vector of the seabed sensing node set are unified; the vector difference between the center of the seabed sensing node set and the center of the network node set is added to each network node, completing a movement of a Harris hawk.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the underwater wireless sensor network target coverage deployment method based on seabed topography according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the underwater wireless sensor network target coverage deployment method based on seabed topography as described in any one of claims 1 to 7 are implemented.

Images