Underwater wireless sensor network target coverage deployment method based on submarine topography

Abstract

The invention discloses an underwater wireless sensor network target coverage deployment method based on submarine topography, and the method comprises the steps: considering the underwater wireless sensor network target coverage problem of submarine topography, and constructing an integer nonlinear programming model INLP; and solving the integer nonlinear programming model INLP by adopting a Harris eagle optimization algorithm. According to the method, the submarine topographic features are creatively combined, the loss of the underwater acoustic signal is calculated by adopting the underwater acoustic simulator BELLHOP3D, and the transmission success rate of the link is further derived, so that the transmission quality of the underwater communication system is effectively improved. By constructing the integer nonlinear programming model, the deployment positions of the underwater sensor nodes are optimized, the coverage rate and the uniformity are maximized, and the problem that the influence of complex submarine topography on signal transmission cannot be fully considered in a traditional underwater network deployment method is solved.

Description

Technical Field

[0001] The 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 beings to explore 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 application scenario of the present invention is an underwater wireless sensor network with a multi-hop transmission mode. A group of submarine sensing nodes are deployed on the seabed (target), and they are intended to collect various data of the submarine environment, such as oil, natural gas, marine life and seabed activities. These submarine sensing nodes are also equipped with acoustic modules, which can monitor and send various collected parameters in real time. In order to effectively use these data, they need to be transmitted to the sea surface aggregation node on the sea surface, which is usually located on a buoy or ship near the sea surface. The sea surface aggregation node is responsible for collecting, integrating and transmitting data from the submarine sensing nodes, and sending it to the ground station or data center for processing and analysis. In order to achieve this data transmission, a certain number of sensor nodes (network nodes) need to be deployed between the submarine sensing nodes and the sea surface aggregation nodes to form a network node set. The submarine sensing nodes transmit the collected data to the nearest network node through this network, and then the network node transmits the data to the farther network node, and finally reaches the sea surface aggregation node. There are some challenges in this process. First, the existence of complex submarine terrain such as submarine mountains, submarine slopes and submarine depressions can cause irregular sound propagation losses and even produce larger sound shadow areas. Secondly, underwater acoustic communication is easily affected by factors such as Doppler effect, multipath effect, and ocean environmental noise. Ocean environmental noise can interfere with the normal operation of the underwater acoustic communication system, resulting in loss or error of communication signals, which makes its bit error rate higher than that of land radio communication. The above reasons together lead to poor coverage quality, reduced data transmission reliability, and poor network connectivity in underwater wireless sensor networks.

[0004] In recent years, the target coverage problem in underwater acoustic sensor networks has been widely studied. However, there is no latest research specifically targeting target coverage considering submarine topography in UWSN. By focusing on the latest research on target coverage in fields such as wireless sensor networks and urban networks, it is found that the underlying perception model of most algorithms in wireless sensor networks is based on Bresenham's line of sight algorithm (LOS). For example, the IEEE TRANSACTIONS ON MOBILE COMPUTING journal paper "Toward a Realistic Approach for the Deployment of 3D Wireless Sensor Networks" proposes a 3D environment real coverage model based on Bresenham's line of sight, and considers environmental factors and real space at the same time. This type of perception model is more suitable for describing radio signals propagating along a straight line. However, as the mainstream communication method between underwater wireless sensor nodes, underwater acoustic communication cannot be well described by LOS. The underwater acoustic toolbox BELLHOP3D can be used to calculate the loss of underwater acoustic signals considering submarine topography, and then the transmission success rate of the link can be calculated based on the data. On this basis, the deployment position of network nodes is optimized and the coverage rate and other indicators are maximized to improve the overall coverage of the network. Considering that the introduction of terrain will increase the difficulty of solving the problem, it is necessary to ensure that the designed algorithm can still find a high-quality solution under the premise of considering the terrain. At the same time, the introduction of terrain may bring about the problem of uneven coverage, and the frequency of different seabed sensing nodes being covered by the network varies greatly. Considering the above factors, there is an urgent need for an underwater wireless sensor network target coverage deployment method based on seabed terrain, which can find an excellent deployment method under the premise of considering the seabed terrain, thereby improving the overall coverage efficiency of the wireless sensor network for the target. Summary of the invention

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

[0006] The objective 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 network considering the seabed topography, the stage of constructing integer nonlinear programming model INLP, and the stage of deploying algorithm to solve integer nonlinear programming model INLP. First, in the target coverage problem of underwater wireless sensor network considering the seabed topography, the stage of constructing integer nonlinear programming model INLP, BELLHOP3D is used to calculate the loss TL of the hydroacoustic signal considering the seabed topography, and then the transmission signal-to-noise ratio SNR is calculated, and the link transmission success rate TSR is calculated by the signal-to-noise ratio. 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 deploying algorithm to solve the integer nonlinear programming model INLP, the present invention designs an underwater node deployment method based on Harris Hawk optimization algorithm, and the population initialization strategy based on sound field distribution and the Harris Hawk mobile strategy based on 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 effect under different seabed terrain conditions, and the coverage is more uniform. Specifically:

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

[0009] S1. Considering the underwater wireless sensor network target coverage problem of the seabed terrain, an integer nonlinear programming model INLP is constructed; specifically including:

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

[0011] S102. Define optimization target cov;

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

[0013] S2. The Harris Eagle optimization algorithm is used to solve the integer nonlinear programming model INLP; specifically, it includes:

[0014] S201. Gridding the entire solution space of the integer nonlinear programming model INLP, wherein 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 coding rules of the Harris Hawk optimization algorithm;

[0016] S203. Simulate all seabed sensing nodes to transmit water acoustic signals at the same time, simulate the sound field distribution corresponding to the seabed sensing node set U, analyze the sound field distribution to obtain the propagation characteristics of the seabed sensing nodes after considering the terrain, and initialize the Harris hawk population;

[0017] S204. Initializing a portion of Harris Hawks according to the sound field distribution, each Harris Hawk represents a deployment method, and the initial optimization target of the initialized Harris Hawks will be 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 the surrounding terrain information;

[0020] S207. Before the Harris Hawk optimization algorithm starts to iterate, the initialization of part of the Harris Hawk is completed through step S203 and step S204; then the calculation and iteration of the Harris Hawk optimization algorithm is entered; at the end of each round of iteration, the Harris Hawk is adjusted through step S205 and step S206, until the iteration of the Harris Hawk optimization algorithm 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 terrain.

[0021] Furthermore, in step S101, the loss of the hydroacoustic signal taking into account the seabed topography is first calculated by BELLHOP3D, and the signal-to-noise ratio of the hydroacoustic signal taking into account the seabed topography is further calculated in combination with the empirical formula of ocean noise, and finally the transmission success rate of the hydroacoustic 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 set of deployed network nodes V s The receiving capability of the underwater acoustic signal sent by the seabed sensing node is as follows: the larger the cov is, the more comprehensive the information received from the seabed sensing node is; 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 taken as the optimization target cov, which represents the network node set V after the deployment operation. s For each seafloor sensing node u i The average fusion reception probability of the emitted hydroacoustic 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, so as 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 nodes v on the sea surface through several reliable links o Send data; the above constraints together ensure that each seabed sensing node u i There is a direction connection to the sea surface aggregation node v0.

[0025] Further, 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, wherein 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 one movement of a Harris hawk.

[0029] The present invention also provides an electronic device, including 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 are implemented.

[0030] The present invention also provides a computer-readable storage medium on which a computer program is stored. 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 are implemented.

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

[0033] 1. Improve the accuracy and reliability of coverage: This paper combines the real seabed topography for the first time to study the underwater target coverage problem. It uses the hydroacoustic toolbox BELLHOP3D to analyze the impact of seabed topography on underwater acoustic propagation and calculate the transmission success rate of underwater communication links, thereby optimizing the overall coverage effect of underwater wireless sensor networks. Compared with the traditional line-of-sight propagation model, the signal propagation loss after considering the seabed topography is more realistic and can improve the coverage accuracy and reliability of the network.

[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 underwater wireless sensor network target coverage problems considering terrain. The optimized Harris Hawk optimization algorithm of the present invention can more accurately adjust the node position in each iteration to ensure 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. Enhance system adaptability and adapt to complex submarine environments: By introducing an integer nonlinear programming model and optimization algorithm based on submarine topography, the present invention can automatically adjust the layout of network nodes in various complex submarine environments for different submarine sensing nodes and terrain features, thereby greatly improving the connectivity and reliability of the network. Whether in complex terrains such as submarine ridges, submarine slopes or submarine depressions, it can achieve excellent target coverage effects and ensure the stable operation of underwater wireless sensor networks.

[0036] 4. High computational efficiency, optimization results have high practical value: By combining the Harris Eagle optimization algorithm and giving full play to its advantages in solving complex optimization problems, the present invention improves the deployment quality while ensuring the computational efficiency of the solution. Especially when multiple factors need to be considered (such as seabed topography, network node distribution, etc.), the method of the present invention can provide fast and high-quality optimization solutions, and has strong engineering application value.

[0037] In summary, the deployment method of the present invention significantly improves the target coverage efficiency and network connectivity of the underwater wireless sensor network while taking the seabed topography into consideration, 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 visual diagram of transmission loss;

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

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

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

[0043] Figure 6 It is a roulette strategy flow chart;

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

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

[0046] Fig. 9 This is a pseudo-code diagram of the Harris Hawk move method;

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

[0048] Fig.11 It is a flow chart of the 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 in conjunction with 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 used to limit the present invention.

[0050] To facilitate the subsequent description of the specific technical solutions in the underwater wireless sensor network target coverage deployment method 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: A 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 seafloor 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 hydroacoustic 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 there is 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: Two-dimensional elevation array corresponding to the deployment area. The triple (x, y, ea[x][y]) represents a point on the terrain. The vertical direction upwards is the seawater, and the vertical direction downwards 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 more clearly describe the implementation method, 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 = 2u1,u2…u |U|}, which is the target in the underwater wireless sensor network target coverage problem based on the seabed topography in this embodiment. The sea 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 sea surface aggregation node. The set of acoustic sensor nodes is recorded as In this embodiment, it is called a network node set, and the hydroacoustic 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, and the vertical direction of this point is the sea water, and the vertical direction of this point is the seabed geology. The topology of the underwater wireless sensor network is composed of a number of directed communication links. This topology ensures that each seabed sensing node is directed connected to the sea surface aggregation node. 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 accordance with this embodiment are described in detail as follows.

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

[0074] S101: In this part, the transmission success rate of the communication link considering the seabed topography is first calculated, so as to derive the optimization target cov. In the underwater wireless sensor network target coverage problem based on the 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 Transmit the data it collects, 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. Calculate the transmission success rate (TSR) of each link. 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, 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 lbit 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 hydroacoustic 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 spectral 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 noises 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 give the calculation formula of the optimization target. In the underwater wireless sensor network target coverage problem based on the seabed topography in this embodiment, cov is used to describe the overall receiving ability of the deployed underwater wireless sensor network to the acoustic signals sent by all seabed sensing nodes. The larger the cov value, the more comprehensive the data sent by the seabed sensing nodes can be received by the deployed network, so the coverage effect of the deployed network is better. 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, then the network deployed in the probability space is i The coverage can be calculated by the following formula:

[0083]

[0084] Where 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 the deployment operation. Its calculation method is given by the following formula:

[0085]

[0086] S103: With the above definition and the calculated optimization target, 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 satisfy these constraints. The decision variables of the INLP model are 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 current terrain's altitude This ensures the rationality of the solution. 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 value P0, a ij Set to 1, otherwise a ij = 0. The flow conservation constraints (9) and (11) ensure that at least one unit flow can be obtained 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 Sending a unit stream has x ij =1, otherwise x ij = 0. Therefore, constraints (8) to (11) together ensure that any seafloor 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 aggregation 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 seafloor 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 lead to uneven coverage, 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 problems of high solution quality and uniform coverage.

[0095] S2. The Harris Eagle optimization algorithm is used 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 column, where the i-th row represents the network node v i Three-dimensional coordinates in space

[0098] Similarly, the encoding rules of prey in the Harris Hawk optimization algorithm need to be given. First, the specific meaning of prey in the Harris Hawk optimization algorithm needs to be clarified. Prey in the 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 the Harris Hawk, and it also uses a 3×|V s | matrix storage. Figure 5 As shown in Figure 2, the difference is that in the 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 node in the 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 sound intensity value can be used to assign a weight to the grid point. The calculation formula is as follows:

[0103]

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

[0105] In genetic algorithms, the roulette strategy is a commonly used selection operation used to select individuals to enter the next generation. Its basic idea is similar to gambling on a roulette wheel: each individual is assigned a corresponding part on the roulette wheel, and the size of this part is proportional to the fitness of the individual. When selecting individuals for the next generation, the roulette wheel is divided into corresponding parts according to the size of the fitness, and then a pointer is randomly selected to determine which individual to select.

[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, and all grid points are allocated to the corresponding part of the roulette wheel, and the size of this part is proportional to the size of the grid weight. In the process of selecting the grid point, the selected grid point is determined by the position of the random pointer. The generation process of the roulette wheel is calculated using the following formula.

[0107]

[0108] Finally, the Harris Hawk population is initialized through the roulette wheel generated above. The number of the population 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 randomly initialized. 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 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, that is, 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 a generator. Figure 7 The Voronoi partitioning is performed on a two-dimensional elevation array using the distribution of seafloor sensing nodes as generators. 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 coverage requirement of the submarine sensing node 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 restrictions the sound line is subject to. 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 result less than 0 is set to 0. The calculation method of the acoustic propagation factor 2 is given by formula (17), which reflects the complexity of the terrain around the seabed sensing node.

[0111]

[0112]

[0113] β is used 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 point, 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, the weighted average position u of all seabed sensing nodes in the seabed sensing node set is used 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 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 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 moving 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 that u center and v center The farther the distance is, the greater the movement amplitude is. At the same time, the more iterations of the algorithm, the smaller the movement amplitude is. 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. Fig. 9 The pseudo code for this process is given below. Fig.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 strength, calculated using formula (21), T represents the maximum number of iterations, t represents the number of iterations that have been completed, and e0 represents the initial energy state.

[0119]

[0120] J = 2 × (1 - rand()) (21)

[0121] Exploration stage:

[0122] If |e| ≥ E1, it indicates 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 different deployment methods choosing different change 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, 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 indicates 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 rapid progressive dive, and soft siege with rapid 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. 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 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 sieges, where S is a random vector of dimension 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] Fig.11The overall flow chart of the deployment method CTC-HHO based on the Harris Hawk optimization algorithm of this embodiment is given. The method uses the Harris Hawk optimization algorithm as the framework. Before the Harris Hawk optimization algorithm starts to iterate, the initialization of part of the Harris Hawk is completed through steps S203 and S204; then the calculation and iteration of the Harris Hawk optimization algorithm are entered; at the end of each round of iteration, the fine-tuning of the Harris Hawk is completed through steps S205 and S206. After the algorithm ends, the Harris Hawk with the highest optimization target cov value will be used as the final output solution of the algorithm.

[0140] Preferably, the embodiment of the present application also provides a specific implementation of an electronic device capable of implementing all steps in the underwater wireless sensor network target coverage deployment method based on seabed topography in the above embodiment, and 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, all the steps in the underwater wireless sensor network target coverage deployment method based on seabed topography in the above embodiment are implemented.

[0144] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the method for target coverage deployment of an underwater wireless sensor network based on seabed topography in the above-mentioned embodiments. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, all the steps in the method for target coverage deployment of an underwater wireless sensor network based on seabed topography in the above-mentioned embodiments are implemented.

[0145] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0146] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some 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 flow charts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0148] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 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 capable of directing a computer or other programmable data processing device to operate 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 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified 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 solution 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, a person of ordinary skill in the art can also make many forms of specific changes under the guidance of the present invention, which all 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 underwater wireless sensor network target coverage problem of the seabed terrain, an integer nonlinear programming model INLP is constructed; specifically including: S101. Calculate the success rate of underwater acoustic signal transmission considering the seabed topography; S102. Define optimization target cov; S103. Convert the underwater wireless sensor network target coverage problem based on the seabed topography into an integer nonlinear programming model INLP, and set relevant constraints; S2. The Harris Eagle optimization algorithm is used to solve the integer nonlinear programming model INLP; specifically, it includes: S201. Gridding the entire solution space of the integer nonlinear programming model INLP, wherein the size of the grid depends on the accuracy of the two-dimensional elevation array ea corresponding to the deployment area; S202. Set the coding rules of the Harris Hawk optimization algorithm; S203. Simulate all seabed sensing nodes to transmit acoustic signals simultaneously, simulate the sound field distribution corresponding to the seabed sensing node set U, analyze the sound field distribution to obtain the propagation characteristics of the seabed sensing nodes after considering the terrain, and initialize the Harris hawk population; S204. Initializing a portion of Harris Hawks according to the sound field distribution, each Harris Hawk represents a deployment method, and the initial optimization target of the initialized Harris Hawks will be larger; 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; S206. Setting a Harris Hawk mobile method based on the distribution of seabed sensing nodes and the surrounding terrain information; S207. Before the Harris Hawk optimization algorithm starts to iterate, the initialization of part of the Harris Hawk is completed through step S203 and step S204; then the calculation and iteration of the Harris Hawk optimization algorithm is entered; at the end of each round of iteration, the Harris Hawk is adjusted through step S205 and step S206, until the iteration of the Harris Hawk optimization algorithm 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 terrain.

2. According to claim 1, a method for deploying underwater wireless sensor network target coverage based on seabed topography is characterized in that: In step S101, the loss of the hydroacoustic signal taking into account the seabed topography is first calculated by BELLHOP3D, and the signal-to-noise ratio of the hydroacoustic 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 hydroacoustic signal taking into account the seabed topography is calculated taking into account BPSK modulation.

3. According to claim 1, a method for deploying underwater wireless sensor network target coverage based on seabed topography is characterized in that: The optimization target cov is used to describe the set of deployed network nodes V s The receiving capability of the underwater acoustic signal sent by the seabed sensing node is as follows: the larger the cov is, the more comprehensive the information received from the seabed sensing node is; 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 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 taken as the optimization target cov, which represents the network node set V after the deployment operation. s For each seafloor sensing node u i The average fusion reception probability of the emitted hydroacoustic signal.

4. According to claim 1, a method for deploying underwater wireless sensor network target coverage based on seabed topography is 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 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 All of them can send data to the sea surface aggregation node v0 through several reliable links; The above constraints jointly ensure that each seafloor sensing node u i There is a direction connection to the sea surface aggregation node v0.

5. According to claim 1, a method for deploying underwater wireless sensor network target coverage based on seabed topography is characterized in that: 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.

6. According to claim 1, a method for deploying underwater wireless sensor network target coverage based on seabed topography is characterized in that: In step S203, BELLHOP3D is used to simulate the sound field distribution corresponding to the seabed sensing node set U.

7. According to claim 1, a method for deploying underwater wireless sensor network target coverage based on seabed topography is 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 relatively high, the transmission success rate is relatively high, and the corresponding initial optimization target of the Harris Hawk is larger.

8. According to claim 1, a method for deploying underwater wireless sensor network target coverage based on seabed topography is 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, wherein 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.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, 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 8 are implemented.

10. 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 8 are implemented.

Images