A method for deploying an underwater acoustic sensor network, a terminal device and a storage medium

Abstract

The present invention relates to a method for deploying an underwater acoustic sensor network, a terminal device and a storage medium, wherein the method comprises: constructing a cubic grid network based on the position corresponding to an underwater target event, setting all underwater target events to be included in the range of the cubic grid network; selecting a grid point from the cubic grid network as a sink node in the sensor network, setting all nodes corresponding to sensors in the sensor network to be located on the grid points of the cubic grid network; starting from the sink node, solving the deployment position of each node in the sensor network based on an improved greedy strategy and a depth-first search mechanism. The present invention can effectively solve the coverage problem of target event sets under various irregular distribution states underwater, can quickly achieve coverage of discrete distribution target events, fully considers the connectivity between nodes in the process of algorithm search solution, and has certain advantages in network deployment cost and target event set coverage efficiency.

Description

Technical Field

[0001] The present invention relates to the field of sensor networks, and in particular to an underwater acoustic sensor network deployment method, terminal equipment and storage medium. Background Art

[0002] With the development of underwater acoustic sensor network technology, the research on target event set coverage technology in underwater acoustic sensor networks has gradually increased in recent years. For example, underwater applications such as marine geological exploration, natural disaster warning, oil and mineral mining, and underwater target monitoring, so it is necessary to provide effective sensor node deployment technology to solve the coverage problem of underwater target event sets.

[0003] In an underwater environment full of uncertainty, the target event set that needs to be covered and monitored by sensor nodes often presents a discrete and irregular distribution due to the effect of water flow, which will bring a certain burden to the deployment of sensor nodes. For example, there may be a large number of deployed nodes, poor coverage and connectivity performance, etc. In solving the coverage problem of underwater target event sets, some scholars have achieved full coverage of target events and full connectivity of deployed networks by improving the ant colony optimization algorithm. However, due to the algorithm's own reasons and the influence of underwater objective factors, this type of algorithm is prone to blind search in the process of solving, and may even fall into local optimality, which increases the deployment cost to a certain extent. In addition, this type of method requires multiple iterations and has a high time cost, so it is not suitable for scenarios where the position of underwater target events changes dynamically. This type of strategy considers the problem from the perspective of global solution. However, considering the above-mentioned problems, this type of method cannot guarantee that the final result is the global optimal solution.

[0004] Therefore, aiming at the coverage problem of target event sets in underwater acoustic sensor networks, studying a complete and reliable deployment framework has become a key issue that needs to be solved urgently. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a method for deploying an underwater acoustic sensor network, a terminal device and a storage medium. The present invention models the underwater target event set coverage problem under an irregular distribution state as multiple local optimal solution problems, and proposes a local search scheme that combines an improved greedy strategy and a depth-first search mechanism. The scheme requires that the best grid point be selected as the deployment location of the node at each step until all target events are covered. Multiple local optima cannot guarantee that the final solution result is the global optimal, but considering the distribution characteristics of the underwater target event set and the limitations of the global solution ability of the swarm intelligence optimization algorithm in this scenario, it is feasible to use the scheme of the present invention to solve the coverage problem of the underwater target event set. Compared with the swarm intelligence optimization scheme, the scheme of the present invention has certain advantages, which are specifically reflected in the solution efficiency, deployment cost and coverage efficiency.

[0006] The specific plan is as follows:

[0007] A method for deploying an underwater acoustic sensor network comprises the following steps:

[0008] S1: construct a cube grid network based on the positions corresponding to the underwater target events, and set all the underwater target events to be included in the range of the cube grid network;

[0009] S2: Select a grid point from the cubic grid network as a sink node in the sensor network, and set the nodes corresponding to the sensors in the sensor network to be located on the grid point of the cubic grid network;

[0010] S3: Starting from the sink node, the deployment location of each node in the sensor network is solved based on the improved greedy strategy and the depth-first search mechanism;

[0011] When the communication range of a node contains grid points that can cover the remaining underwater target events, an improved greedy strategy is used to deploy the next node;

[0012] When there are no grid points that can cover the remaining underwater target events within the communication range of a certain node, the next node is deployed using a depth-first search mechanism. If there are still remaining underwater target events after the deployment using the depth-first search mechanism, the node closest to the remaining underwater target events is selected as the starting point, all grid points within the communication range of the node are found, and the distance between each grid point and the remaining underwater target events is calculated. The grid point with the smallest distance is used as the next node in the sensor network.

[0013] In the above description, the grid point that can cover the remaining underwater target events means that when the sensor node is deployed at the grid point, the remaining underwater target events are included in its communication range. In the following description, the communication range of the grid point is equivalent to the communication range of the node.

[0014] Furthermore, the improved greedy strategy is as follows: when deploying the next node, all grid points included in the communication range of the previous node are found, and the number of remaining underwater target events that can be covered within the communication range of each grid point is calculated, and the grid point with the largest number of remaining underwater target events that can be covered is used as the next node in the sensor network.

[0015] Furthermore, when there are multiple grid points within the communication range of the node that can cover the same number of remaining target events, the number of remaining underwater target events that can be covered within the communication range of the next selected node is calculated separately when these grid points are used as the previous nodes, and the previous node corresponding to the node with a larger number of remaining underwater target events that can be covered is selected as the preferred grid point.

[0016] Furthermore, the depth-first search mechanism is as follows: when all grid points within the communication range of a node cannot cover the remaining underwater target events, return to the previous node of the node to determine whether there are grid points within the communication range of the previous node that can cover the remaining underwater target events. If so, use the grid point that covers the largest number of remaining underwater target events as the next node in the sensor network; otherwise, return to the previous node of the previous node.

[0017] Furthermore, the distance between the remaining underwater target events and the grid points adopts the Euclidean distance.

[0018] Furthermore, when there are multiple remaining underwater target events, the distance between the remaining underwater target event and the grid point is the minimum value of the distances between all the remaining underwater target events and the grid point.

[0019] Furthermore, during the node search process, a record table with a real-time update mechanism is set to store the position information of the deployed nodes and the coverage status of the underwater target events.

[0020] A terminal device for deploying an underwater acoustic sensor network includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method described above in the embodiment of the present invention are implemented.

[0021] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described above in an embodiment of the present invention are implemented.

[0022] The present invention adopts the above technical solution, which can effectively solve the coverage problem of target event sets under various irregular distribution states underwater, can quickly achieve coverage of discrete distributed target events, and fully considers the connectivity between nodes during the algorithm search process, which has certain advantages in network deployment cost and target event set coverage efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Shown is a flow chart of Embodiment 1 of the present invention.

[0024] Figure 2 Shown is a schematic diagram of a cube grid network in this embodiment.

[0025] Figure 3 The figure shows a schematic diagram of the search process in this embodiment only in combination with the improved greedy strategy.

[0026] Figure 4 Shown is a schematic diagram of combining the improved greedy strategy and depth-first search solution in this embodiment.

[0027] Figure 5 The figure is a schematic diagram of the process of searching for t1 under the distribution state of the special target event set in this embodiment.

[0028] Figure 6 The figure is a schematic diagram of the process of searching for t2 under the distribution state of the special target event set in this embodiment. DETAILED DESCRIPTION

[0029] To further illustrate various embodiments, the present invention provides drawings. These drawings are part of the disclosure of the present invention, which are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementations and advantages of the present invention.

[0030] The present invention will now be further described with reference to the accompanying drawings and specific implementation methods.

[0031] Embodiment 1:

[0032] The embodiment of the present invention provides a method for deploying an underwater acoustic sensor network. Figure 1 As shown, the method comprises the following steps:

[0033] S1: Construct a cube grid network based on the positions corresponding to the underwater target events, and set all the underwater target events to be included in the range of the cube grid network.

[0034] In this embodiment, in order to reduce the complexity of the solution, the deployment area of ​​the sensor nodes is determined according to the distribution range of the underwater target events, so that the cubic deployment area containing all the underwater target events can be determined.

[0035] S2: Select a grid point from the cubic grid network as a sink node in the sensor network, and set the nodes corresponding to the sensors in the sensor network to be located on the grid point of the cubic grid network.

[0036] For a certain deployment area, different modeling methods can be used according to actual needs. In general, the node deployment problem can be divided into a continuous point deployment problem and a discrete grid point deployment problem. In short, the continuous point deployment problem means that the nodes can be deployed at any location in the network, that is, the solution space is infinite; while the grid point deployment problem means that the network is divided into several discrete grid points, such as Figure 2Therefore, nodes can only be deployed on these grid points, which narrows the scope of solution and significantly improves the efficiency of the algorithm. The reason for determining the cube deployment network is to facilitate its grid division, and the scale of grid division can be changed according to the actual scenario. After grid division, each small cube vertex, that is, grid point, is used as a candidate location for sensor node deployment. In order to record the results of the algorithm solution, the grid points are numbered in this embodiment to generate a candidate solution space G = {g1, g2, ..., g n}, and the position coordinates of each grid point are known.

[0037] In this embodiment, for the convenience of recording, the sink node is set to be located at any grid point on the top surface of the cubic grid network.

[0038] S3: Starting from the sink node, the deployment location of each node in the sensor network is solved based on the improved greedy strategy and the depth-first search mechanism.

[0039] Regarding the coverage problem of the target event set of the underwater acoustic sensor network, coverage and connectivity are the basic issues that the algorithm design must ensure. To ensure the connectivity of the network, each sensor node in the network needs to be able to communicate with the sink node directly or indirectly. According to this idea, the algorithm's search process starts from the sink node and searches for the required grid points within its communication range to deploy the sensor nodes. Similarly, the search area of ​​each step is limited to the communication range of the current node, and the most suitable grid point is selected as the node deployment location. When all the target events in the network are covered, the search process ends, and the coverage and connectivity of the network are effectively guaranteed.

[0040] Taking the coverage and connectivity performance of the deployment effect as the basic criteria, this embodiment solves the deployment position of each node in the sensor network based on an improved greedy strategy and a depth-first search mechanism.

[0041] (1) Improved Greedy Mechanism

[0042] In the algorithm solution process, whether the grid point meets the optional conditions is determined according to the improved greedy strategy. In each step of the search process, the grid point that covers the most target events is selected as the node deployment location to reduce the range of candidate solution sets and speed up the algorithm convergence. The expression for the optimal grid point selection is as follows:

[0043]

[0044] Among them, g is the grid point label, C n(i)(g) is the number of remaining underwater target events that can be covered after deploying nodes at a certain grid point g within the communication range of node n(i) (i.e., all underwater target events excluding those covered by existing nodes), G n(i) is the set of grid points within the communication range of node n(i), g s is the grid point selected in the next step, that is, the deployment location of the next node n(i+1).

[0045] When searching for the deployment location of the next node within the communication range of the current node, two or more grid points may meet the requirements. Nodes deployed on these grid points can cover the same number of underwater target events. It is worth considering which grid point to choose for node deployment.

[0046] In this embodiment, two grid points g appear during the search process. x and g y At the same time, it meets the requirement of covering the most underwater target events, that is, C n(i) (g x )=C n(i) (g y ), the current decision depends on the calculation result of the next step, that is, the judgment-feedback mechanism:

[0047] If the sensor nodes are deployed at the grid point g x , then n(i+1)=n(j), and the best grid point g1 is calculated within the communication range of node n(j):

[0048]

[0049] If the sensor nodes are deployed at the grid point g y , then n(i+1)=n(k), and the best grid point g2 is calculated within the communication range of node n(k):

[0050]

[0051] Among them, g s1 and g s2 The value of may be a point set, g1 and g2 are one of the values, then for the grid point g x and the grid point g y The selection problem can be calculated as follows:

[0052]

[0053] Among them, g select is the grid point selected according to the judgment-feedback mechanism. If max(C n(j) (g1))=max(C n(k)(g2)), the judgment-feedback calculation is performed again. In order to reduce the computational complexity, when similar extreme situations occur again, the algorithm performs a local random selection strategy.

[0054] When the above situation occurs during the search process, the traditional greedy strategy will randomly select a grid point for node deployment, while the search of the improved algorithm will have great guidance, which can reduce the possibility of blind search to a certain extent. The greedy strategy focuses more on considering local solutions, and covering the most underwater target events each time cannot guarantee the minimum number of nodes deployed in the end, especially in the underwater environment where the underwater target events are discrete and irregular. This phenomenon is more obvious. At the same time, considering the shortcomings of the greedy strategy in the above extreme cases, another strategy is combined in this embodiment to improve the search process.

[0055] (2) Depth-first search mechanism

[0056] The biggest drawback of the greedy strategy is the lack of a backtracking mechanism, that is, after the algorithm completes the deployment of a certain node, it cannot find a grid point that can cover the remaining underwater target events within its communication range, so it will fall into a state of search stagnation. This embodiment will optimize the overall algorithm by combining a depth-first search mechanism.

[0057] The depth-first search rule is introduced into the greedy strategy, and the specific implementation process can be described as follows. First, according to the improved greedy strategy, the algorithm searches for the node deployment location. When the search stagnates, the algorithm returns to the previous node of the current node to verify whether there is a grid point within the communication range of the node that can cover the remaining target events. If there is, the algorithm continues the greedy search process with the grid point as the starting point. If not, the algorithm continues to return to the previous node of the node for verification. Referring to the last-in-first-out data update mechanism of the "stack", the search path is continuously updated, and what is finally saved is the deployment location information of all sensor nodes. In order to illustrate the problem more intuitively, a two-dimensional grid plane is used to illustrate the solution process:

[0058] like Figure 3As shown in the figure, the algorithm's search starts from the sink node, and the best grid point is selected as the node deployment location at each step. The selection of the best grid point is determined by the improved greedy mechanism. When the location of node N5 is determined, the algorithm stagnates in the search. According to the depth-first search mechanism, the algorithm performs a backtracking search. According to the concept of the stack, node N4 is first verified. The algorithm will calculate the grid points within its communication range to verify whether the nodes deployed at these grid points can cover the remaining underwater target events. If so, the greedy search will continue. Otherwise, it will directly backtrack from node N4 to node N3. When the algorithm backtracks to node N2, there are grid points that meet the requirements within its communication range, so the deployment location of node N6 can be determined, and the greedy search continues until the deployment of node N9 is completed. At this point, the search process ends, the target events in the entire network are covered, and the connectivity of the deployed network is effectively guaranteed, as shown in Figure 1. Figure 4 shown.

[0059] When the distribution of underwater target events is special, such as when a certain underwater target event is far away from other underwater target events, that is, when an underwater target event appears outside the communication range of the deployed sensor network, the distance information between all deployed sensor nodes and the underwater target event is calculated, and the node closest to the underwater target event is selected as the starting point. The calculation formula for node selection is:

[0060]

[0061] Among them, dis(n(i),t j ) represents the node n(i) and the underwater target event t j The Euclidean distance between them, N represents the set of nodes deployed in the current network. After selecting the starting node, in each step of the search process, the grid point closest to the underwater target event within the communication range of the current node is searched for node deployment to save deployment costs. The calculation formula for grid point selection is:

[0062]

[0063] Among them, G n(i) is the set of grid points within the communication range of the current node n(i), G s is the underwater target event t at the selected distance j The nearest grid point is used as the deployment location of the next node. The sensor nodes are deployed according to this solution idea at each step until the underwater target event t j is covered.

[0064] It should be noted that, when there are multiple remaining underwater target events, the distance between the remaining underwater target event and the grid point is the minimum value of the distances between all the remaining underwater target events and the grid point.

[0065] like Figure 5 As shown in the figure, after the location of node N9 is determined, there are still two uncovered underwater target events t1 and t2 in the network. At this time, the grid points within the communication range of all deployed nodes cannot cover these two underwater target events. In order to ensure full coverage of underwater target events and connectivity of the deployed network, additional nodes need to be deployed. In order to save costs, the node closest to the underwater target event is selected as the starting point. By calculation, it is found that node N9 is closest to underwater target event t1. Therefore, the grid point closest to underwater target event t1 is found within the communication range of node N9 as the deployment position of the next node. According to this idea, when node N11 is deployed, underwater target event t1 is covered, as shown in the figure below. Figure 5 In this search process, the search priority based on the improved greedy strategy will be higher than the above search process. For example, when node N11 is deployed, there are grid points within its communication range that meet the conditions of the improved greedy search mechanism, that is, the underwater target event t2 can be covered by the grid points within the communication range of node N11, so the deployment position of node N12 can be directly determined. At this time, all underwater target events are covered, and the full connectivity of the deployment network is also guaranteed, as shown in Figure 6 shown.

[0066] In addition, in this embodiment, a record table with a real-time update mechanism is set to store the location information of the deployed nodes and the coverage status of the underwater target events to improve the algorithm solving efficiency. During the node search process, the underwater target events that have been covered by the sensor nodes will not affect the subsequent evaluation. After the deployment position of the node n(i) is determined, there are already underwater target events covered in the network. When determining the deployment position of the node n(i+1), when examining the number of underwater target events that can be covered by the grid points within the communication range of n(i), it is necessary to exclude the underwater target events that have been covered.

[0067] The embodiments of the present invention can effectively solve the coverage problem of target event sets under various irregular distribution states underwater, can quickly achieve coverage of discretely distributed target events, and fully consider the connectivity between nodes during the algorithm search solution process, which has certain advantages in network deployment cost and target event set coverage efficiency.

[0068] Embodiment 2:

[0069] The present invention also provides an underwater acoustic sensor network deployment terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above-mentioned method embodiment of embodiment 1 of the present invention when executing the computer program.

[0070] Further, as an executable solution, the underwater acoustic sensor network deployment terminal device can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The underwater acoustic sensor network deployment terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the composition structure of the above-mentioned underwater acoustic sensor network deployment terminal device is only an example of the underwater acoustic sensor network deployment terminal device, and does not constitute a limitation on the underwater acoustic sensor network deployment terminal device. It may include more or less components than the above-mentioned, or a combination of certain components, or different components. For example, the underwater acoustic sensor network deployment terminal device may also include input and output devices, network access devices, buses, etc., and the embodiments of the present invention do not limit this.

[0071] Further, as an executable solution, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the underwater acoustic sensor network deployment terminal device, and uses various interfaces and lines to connect various parts of the entire underwater acoustic sensor network deployment terminal device.

[0072] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the underwater acoustic sensor network deployment terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0073] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method in the embodiment of the present invention are implemented.

[0074] If the module / unit integrated in the terminal equipment for deploying the underwater acoustic sensor network is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory) and software distribution medium, etc.

[0075] Although the present invention has been specifically shown and described in conjunction with the preferred embodiments, it should be understood by those skilled in the art that various changes may be made to the present invention in form and details without departing from the spirit and scope of the present invention as defined by the appended claims, all of which are within the scope of protection of the present invention.

Claims

1. A method for deploying an underwater acoustic sensor network, characterized in that: The following steps are involved: S1: construct a cube grid network based on the positions corresponding to the underwater target events, and set all the underwater target events to be included in the range of the cube grid network; S2: Select a grid point from the cubic grid network as a sink node in the sensor network, and set the nodes corresponding to the sensors in the sensor network to be located on the grid point of the cubic grid network; S3: Starting from the sink node, the deployment position of each node in the sensor network is solved based on the improved greedy strategy and depth-first search mechanism; the improved greedy strategy is specifically: when deploying the next node, find all the grid points contained in the communication range of the previous node, and calculate the number of remaining underwater target events that can be covered within the communication range of each grid point, and use the grid point with the largest number of remaining underwater target events that can be covered as the next node in the sensor network; the depth-first search mechanism is specifically: when all the grid points contained in the communication range of a certain node cannot cover the remaining underwater target events, return to the previous node of the node, and determine whether there is a grid point that can cover the remaining underwater target events within the communication range of the previous node. If so, use the grid point that covers the largest number of remaining underwater target events as the next node in the sensor network; otherwise, return to the previous node of the previous node; When the communication range of a node contains grid points that can cover the remaining underwater target events, an improved greedy strategy is used to deploy the next node; When there are no grid points that can cover the remaining underwater target events within the communication range of a certain node, the next node is deployed using a depth-first search mechanism. If there are still remaining underwater target events after the deployment using the depth-first search mechanism, the node closest to the remaining underwater target events is selected as the starting point, all grid points within the communication range of the node are found, and the distance between each grid point and the remaining underwater target events is calculated. The grid point with the smallest distance is used as the next node in the sensor network.

2. The method for deploying an underwater acoustic sensor network according to claim 1, characterized in that: When there are multiple grid points within the communication range of a node that can cover the same number of remaining target events, the number of remaining underwater target events that can be covered within the communication range of the next selected node is calculated separately when these grid points are used as the previous nodes, and the previous node corresponding to the node with a larger number of remaining underwater target events that can be covered is selected as the preferred grid point.

3. The method for deploying an underwater acoustic sensor network according to claim 1, characterized in that: The distance between the remaining underwater target events and the grid points adopts the Euclidean distance.

4. The method for deploying an underwater acoustic sensor network according to claim 1, characterized in that: When there are multiple remaining underwater target events, the distance between the remaining underwater target event and the grid point is the minimum value of the distances between all the remaining underwater target events and the grid point.

5. The method for deploying an underwater acoustic sensor network according to claim 1, characterized in that: During the node search process, a record table with a real-time update mechanism is set to store the location information of the deployed nodes and the coverage status of underwater target events.

6. A terminal device for deploying an underwater acoustic sensor network, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the methods of claims 1 to 5 when executing the computer program.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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