Fish-school-imitating underwater intelligent Internet of Things topology reconstruction method and system

Abstract

The invention discloses a fish school-imitating underwater intelligent Internet of Things topology reconstruction method and system, and relates to the Internet of Things and communication technology, and the method comprises the steps: initializing an underwater intelligent Internet of Things; performing coverage optimization on the initialized underwater intelligent Internet of Things based on fish school behaviors; and according to the underwater intelligent Internet of Things after coverage optimization, performing connectivity optimization based on the edge nodes so as to complete reconstruction of the underwater intelligent Internet of Things after connectivity optimization. The method is based on network coverage optimization of fish shoal behaviors, an underwater intelligent Internet of Things is formed by establishing a model of non-uniformly distributed to-be-monitored nodes in a target area, in a specific example, the distribution area of underwater network nodes is adjusted by referring to the foraging behavior, the rear-end behavior and the gathering behavior of a fish shoal, and the network coverage optimization of the underwater network nodes is realized. The coverage rate of the underwater intelligent Internet of Things is improved, the disconnected position of the underwater intelligent Internet of Things is repaired, and the connectivity of the underwater intelligent Internet of Things is enhanced.

Description

Technical Field

[0001] The present application relates to the fields of Internet of Things and communication technology, and in particular to a method and system for reconstructing the topology of an underwater intelligent Internet of Things that simulates a school of fish. Background Art

[0002] Underwater intelligent Internet of Things can be widely used in underwater ecological monitoring, marine resource exploration, water resource data collection, seabed scientific research, underwater safety protection and other fields. However, the communication quality between network nodes and the wireless LAN topology are easily affected by the complex marine environment, such as signal attenuation, multipath effect, etc., which cause large transmission delay and low bandwidth. Summary of the invention

[0003] The embodiments of the present application provide a method and system for reconstructing the topology of an underwater intelligent Internet of Things that simulates a school of fish, so as to improve the coverage of the underwater intelligent Internet of Things, repair disconnected locations in the underwater intelligent Internet of Things, and enhance the connectivity of the underwater intelligent Internet of Things.

[0004] The present application provides a method for reconstructing the topology of an underwater intelligent Internet of Things that simulates a school of fish, including:

[0005] Initialize underwater intelligent Internet of Things;

[0006] Optimize the coverage of the initialized underwater smart Internet of Things based on fish school behavior;

[0007] According to the underwater intelligent Internet of Things after coverage optimization, connectivity optimization is performed based on edge nodes to complete the reconstruction of the underwater intelligent Internet of Things after connectivity optimization.

[0008] Optionally, coverage optimization of the initialized underwater smart IoT based on fish school behavior includes:

[0009] The movement of the network nodes of the underwater intelligent Internet of Things after the fish school behavior configuration is initialized, wherein the target within the detection range of the network node is used as bait, and the movement of the network node toward the target is used as the behavior of the fish foraging for food. The network node adjusts the distribution area through the foraging behavior, tail-chasing behavior and aggregation behavior of the fish school.

[0010] Optionally, the coverage optimization of the initialized underwater intelligent Internet of Things based on the fish school behavior specifically includes: setting the network node detection range to a sphere with itself as the center and a radius of r, and the maximum movement step length is l step , the number of iterations is MaxIter, where:

[0011] The number of targets within the monitoring range in the vicinity of the network node is used as the fitness, satisfying:

[0012] N sensor (s i)=|{t j |d(s i , t j )≤r s , j = 1, 2, ..., m}| (1)

[0013] Among them, N sensor represents fitness, t j Indicates the target coverage time, s i represents artificial fish;

[0014] Covering target time t j The number of sensors is taken as the coverage number, satisfying:

[0015]

[0016] Among them, J(·) represents the judgment function;

[0017] The allowed congestion level around a certain location in the target monitoring area is taken as the congestion coefficient, which satisfies:

[0018]

[0019] in, is the expected coverage number of the target, N sensor (s i ) is at node s i The number of targets within the monitoring range, N neigh (s i ) is node s i The number of neighbor nodes, N near (s i ) indicates sensor s i The number of network nodes within coverage area.

[0020] Optionally, the coverage optimization of the initialized underwater smart Internet of Things based on the fish school behavior specifically includes the following node movement conditions:

[0021] N neigh (s i )=0, start foraging behavior: according to the perception ability of artificial fish, the step length is limited to l step Within, the direction is random, and the position obtained by iterative calculation is:

[0022]

[0023] Among them, rand(l step ) represents from 0 to l step If the artificial fish is moved, it means that the network node is moving The movement of N sensor (s i) increase, successful foraging, and failed foraging;

[0024] N neigh (s i )>0, start tailgating behavior: find the best neighbor node s b ,Right now If N sensor (s b )>N sensor (s i ) and N sensor (s b )<δ(s b ), then the network node s b The number of coverage targets is greater than the number of network nodes s i The number of coverage targets is large, and the network nodes s i If the location is not congested, move the network node s i To network nodes b , do the following:

[0025]

[0026] If the network node s i Move to Make N senso (s i ) increases, the rear-end collision is successful, otherwise the rear-end collision fails; N neigh (s i )>1, start the aggregation behavior: define the node center s c The location is Computing center nodes c The number of targets within the coverage area is Central Nodes c The number of network nodes within the coverage area is

[0027] If N sensor (s c )>N sensor (s i ) and N sensor ( sc )>δ(s c ), it indicates that the network node s c The number of coverage targets is greater than the number of network nodes s i The number of coverage targets, and the network nodes s c The location is not congested, and the network node s i Move to network node s c As a step, do the following:

[0028]

[0029] If the network node s i Move to Make N sensor (s i ) is increased, the aggregation behavior succeeds, otherwise the aggregation behavior fails.

[0030] Optionally, optimizing the coverage of the initialized underwater intelligent Internet of Things based on the fish school behavior specifically includes: if N sensor (s i )=0, network node s i Move to the center of the target coverage area and repeat the node movement to perform node movement on each network node in the target area to complete the underwater intelligent Internet of Things coverage optimization.

[0031] Optionally, according to the underwater smart Internet of Things after coverage optimization, connectivity optimization based on edge nodes includes:

[0032] Select the key nodes that need to be repaired;

[0033] Generate and send a request message for executing a repair instruction, so that the edge node generates a request message;

[0034] The key node generates a response message to cause the edge node to generate an acceptance message.

[0035] Optionally, according to the underwater intelligent Internet of Things after coverage optimization, the connectivity optimization based on the edge node specifically includes:

[0036] The positions of disconnected nodes and key nodes of the underwater intelligent Internet of Things are taken as the target positions, and the repair node movement is guided by virtual force. A virtual artificial potential field is defined during the position movement process, wherein the virtual artificial potential field includes the gravitational force generated by the target, the repulsive force generated by the adjacent nodes, the repulsive force generated by the obstacles, and the random disturbance force, which respectively satisfy:

[0037]

[0038] Among them, k g , k r , and k o is the gravitational coefficient, m i and m j represents the quality of the network nodes and m i =m j , represents the Euclidean distance between the repair node and the target position, represents the Euclidean distance between two network nodes, Q represents the disturbance amplitude of the random disturbance force;

[0039] During the process of adjusting the network node position, the neighbors of the repair node are affected by the gravitational force of the original position of the repair node and the force between nodes, satisfying:

[0040]

[0041] where k b and k c is the proportionality coefficient, d opt Represents the distance between network nodes. If d opt =1.78r 2 and r c >1.78r s , then it meets the connectivity;

[0042] The resultant force of the original adjacent nodes of the repaired node satisfies:

[0043]

[0044] Under the action of the combined force, the positions of the nodes in the original neighborhood of the repair node are adjusted.

[0045] Optionally, according to the underwater intelligent Internet of Things after coverage optimization, the connectivity optimization based on the edge node specifically includes:

[0046] After selecting the optimal node, obtain information about other network nodes in the connection branch;

[0047] The nearest edge nodes are selected from random key nodes, and the domain topology of the key nodes is reconstructed using the triangulation mechanism.

[0048] An embodiment of the present application also proposes an underwater intelligent Internet of Things topology reconstruction system that simulates a school of fish, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the underwater intelligent Internet of Things topology reconstruction method that simulates a school of fish as described above are implemented.

[0049] The embodiment of the present application optimizes network coverage based on the behavior of fish schools. By establishing a model of unevenly distributed nodes to be monitored in the target area, an underwater intelligent Internet of Things is formed. In the specific example, the distribution area of ​​the underwater network nodes is adjusted with reference to the foraging behavior, tail-chasing behavior and aggregation behavior of the fish school, so as to improve the coverage rate of the underwater intelligent Internet of Things, repair the disconnected locations in the underwater intelligent Internet of Things, and enhance the connectivity of the underwater intelligent Internet of Things.

[0050] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0052] Figure 1 The following is a flowchart of the underwater intelligent Internet of Things topology reconstruction method according to an embodiment of the present application;

[0053] Figure 2 This is a schematic diagram of the network node division of the application example of this application;

[0054] Figure 3 This is a schematic diagram of the repair node selection process for the practical application example of this application;

[0055] Figure 4 This is a schematic diagram of the network topology re-establishment process for the application example of this application;

[0056] Figure 5 This is a schematic diagram of the change in coverage during the optimization process of the underwater intelligent Internet of Things in the application example of this application;

[0057] Figure 6 This is a schematic diagram of the network connectivity recovery test results of the application example of this application. DETAILED DESCRIPTION

[0058] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0059] The embodiment of the present application provides a method for reconstructing the topology of an underwater intelligent Internet of Things that simulates a school of fish. The method of the present application first initializes the position and coverage of the underwater intelligent Internet of Things, then optimizes the coverage of the underwater intelligent Internet of Things through an artificial fish school algorithm, then repairs the unconnected positions in the underwater intelligent Internet of Things, eliminates key nodes, optimizes the connectivity of the underwater intelligent Internet of Things, and finally iteratively optimizes the coverage and network connectivity. Figure 1 As shown, the method of the embodiment of the present application includes the following steps:

[0060] In step S101, the underwater intelligent Internet of Things is initialized.

[0061] In step S102, the coverage of the initialized underwater intelligent Internet of Things is optimized based on the behavior of the school of fish. In some embodiments, the coverage of the initialized underwater intelligent Internet of Things is optimized based on the behavior of the school of fish, including:

[0062] The movement of the network node of the underwater intelligent Internet of Things after the initialization of the fish school behavior configuration, wherein the target within the detection range of the network node is used as bait, and the behavior of the network node moving toward the target is used as the behavior of the fish foraging for food. The network node adjusts the distribution area through the foraging behavior, tailing behavior and aggregation behavior of the fish school. The distance that the artificial fish can monitor is greater than the distance at which they communicate with each other. In some examples of the present application, the monitoring range of the artificial fish is set to a sphere with itself as the center and a radius of r, and the maximum movement step length is l step , Artificial fish i At the current position, the number of iterations is MaxIter.

[0063] In step S103, connectivity optimization is performed based on edge nodes according to the underwater intelligent Internet of Things after coverage optimization, so as to complete reconstruction of the underwater intelligent Internet of Things after connectivity optimization.

[0064] In some embodiments, the coverage optimization of the initialized underwater intelligent Internet of Things based on the fish school behavior specifically includes: setting the network node detection range to a sphere with itself as the center and r as the radius, and the maximum movement step length is l step , the number of iterations is MaxIter, where:

[0065] The number of targets within the monitoring range in the vicinity of the network node is used as the fitness, satisfying:

[0066] N sensor (s i )=|{t j |d(s i , t j )≤r s , j = 1, 2, ..., m}| (1)

[0067] Among them, N sensor represents fitness, t j Indicates the target coverage time, s i represents artificial fish;

[0068] Covering target time t j The number of sensors is taken as the coverage number, satisfying:

[0069]

[0070] Among them, J(·) represents the judgment function. When d(s i , tj )≤r s If satisfied, its value is 1, otherwise it is 0.

[0071] The congestion coefficient is the degree of congestion allowed around a certain location in the target monitoring area. For example, the congestion coefficient of sensor A satisfies:

[0072]

[0073] in, is the expected coverage number of the target, N sensor (s i ) is at node s i The number of targets within the monitoring range, N neigh (s i ) is node s i The number of neighbor nodes, N near (s i ) indicates sensor s i The number of network nodes within coverage area.

[0074] In some embodiments, the coverage optimization of the initialized underwater intelligent Internet of Things based on the fish school behavior specifically includes the following node movement conditions:

[0075] N neigh (s i )=0, start foraging behavior: according to the perception ability of artificial fish, the step length is limited to l step Within, the direction is random, and the position obtained by iterative calculation is:

[0076]

[0077] Among them, rand(l step ) represents from 0 to l step If the artificial fish is moved, it means that the network node is moving The movement of N sensor (s i ) increases, the foraging behavior is successful, otherwise the foraging behavior fails.

[0078] N neigh (s i )>0, start tailgating behavior: find the best neighbor node s b ,Right now If N sensor (s b )>N sensor (s i ) and N sensor (s b )<δ(s b ), then the network node s bThe number of coverage targets is greater than the number of network nodes s i The number of coverage targets is large, and the network nodes s i The location is not congested, and the mobile network node s i To network nodes b , do the following:

[0079]

[0080] If the network node s i Move to Make N sensor (s i ) increases, the rear-end collision is successful, otherwise the rear-end collision fails;

[0081] N neigh (s i )>1, start the aggregation behavior: define the node center s c The location is Computing center nodes c The number of targets within the coverage area is Central Nodes c The number of network nodes within the coverage area is

[0082] If N sennsor (s c )>N sennsor (s i ) and N sensor (s c )>δ(s c ), it indicates that the network node s c The number of coverage targets is greater than the number of network nodes s i The number of coverage targets, and the network nodes s c The location is not congested, and the network node s i Move to network node s c As a step, do the following:

[0083]

[0084] If the network node s i Move to Make N sensor (s i ) is increased, the aggregation behavior succeeds, otherwise the aggregation behavior fails.

[0085] In some embodiments, optimizing the coverage of the initialized underwater intelligent Internet of Things based on the fish school behavior specifically includes: if N sensor (s i )=0, network node s iMove to the center of the target coverage area and repeat the above node movement process to perform node movement on each network node in the target area to complete the underwater intelligent Internet of Things coverage optimization.

[0086] After the network coverage optimization is completed, there are still disconnected nodes in the network. It is necessary to repair the disconnected locations, eliminate key nodes, and optimize the connectivity of the underwater intelligent Internet of Things. The division of key nodes and edge nodes is as follows: Figure 2 In some embodiments, according to the underwater intelligent Internet of Things after coverage optimization, connectivity optimization based on edge nodes includes:

[0087] Select the key nodes that need to be repaired;

[0088] Generate and send a request message for executing a repair instruction, so that the edge node generates a request message;

[0089] The key node generates a response message to cause the edge node to generate an acceptance message.

[0090] In some embodiments, Figure 3 As shown in the figure, according to the underwater intelligent Internet of Things after coverage optimization, the connectivity optimization based on edge nodes specifically includes:

[0091] The positions of disconnected nodes and key nodes of the underwater intelligent Internet of Things are taken as the target positions, and the repair node movement is guided by virtual force. A virtual artificial potential field is defined during the position movement process, wherein the virtual artificial potential field includes potential field force, which includes the gravitational force generated by the target, the repulsive force generated by the adjacent nodes, the repulsive force generated by the obstacles, and the random disturbance force, which respectively satisfy:

[0092]

[0093]

[0094] Among them, k g , k r , and k o is the gravitational coefficient, m i and m j represents the quality of the network nodes and m i =m j , represents the Euclidean distance between the repair node and the target location, represents the Euclidean distance between two network nodes, Q Represents the disturbance amplitude of the random disturbance force.

[0095] During the process of adjusting the network node position, the neighbors of the repair node are affected by the gravitational force of the original position of the repair node and the force between nodes, satisfying:

[0096]

[0097] where k b and k c is the proportionality coefficient, d opt Represents the distance between network nodes. If d opt =1.78r 2 and r c >1.78r s , then connectivity is met and optimal coverage is achieved.

[0098] The resultant force of the original adjacent nodes of the repaired node satisfies:

[0099]

[0100] Under the action of the combined force, the positions of the nodes in the original neighborhood of the repair node are adjusted to fill the gaps caused by removing the repair nodes, thereby preventing the coverage of the underwater intelligent Internet of Things from being reduced.

[0101] Figure 4 The process of re-establishing the network topology to eliminate key nodes. To improve the performance of the underwater intelligent Internet of Things, it is necessary to eliminate key nodes after the repair is completed. In some embodiments, according to the underwater intelligent Internet of Things after coverage optimization, the connectivity optimization based on edge nodes specifically includes:

[0102] After selecting the optimal node, obtain information about other network nodes in the connection branch;

[0103] Select the nearest edge node from the random key node and use the triangle mechanism to reconstruct the domain topology of the key node. Due to the stability of the triangle structure and the limited energy of the underwater intelligent Internet of Things, at least three nodes are required to eliminate the key node. According to the cosine theorem and the sine theorem, at least select the node near the key node that can be connected to the key node to establish a connection with the key node (as shown in Figure e 5，10 (s5,s 10 ) and e 7，10 (s7,s 10 )), after eliminating the key nodes, the above domain topology determination method can be used (as shown by the dotted line e in the figure) 6，10 (s6,s 10 ) and e 9，10 (s9,s 10 )).

[0104] Figure 5 The experimental results of coverage optimization show the curve of how the coverage changes over time under the effect of the optimization algorithm. As can be seen from the figure, the TO-A algorithm can eventually achieve a coverage rate of more than 95%.

[0105] Figure 6This is a demonstration of the effect of the connectivity recovery experiment, which is used to verify the connectivity optimization capability of the TO-A algorithm proposed in this invention. In the experiment, the target number is set to 50 and the number of network nodes is set to 20. Statistics on the connectivity recovery are collected in 100 repeated experiments, and the figure shows the connectivity recovery curves of 4 randomly selected experiments.

[0106] The method of the embodiment of the present application is based on the optimization of network coverage of fish school behavior, and an underwater intelligent Internet of Things is formed by establishing a model of unevenly distributed nodes to be monitored in the target area. In the process of coverage optimization, an artificial fish model, a bait model, and an artificial fish food-seeking model are constructed to simulate and characterize network nodes, targets occurring in the target monitoring area, and network nodes tending to targets, respectively, and the distribution area of ​​underwater network nodes is adjusted with reference to the foraging behavior, tail-chasing behavior, and aggregation behavior of fish schools.

[0107] The method of the present application is based on the optimization of network connectivity of edge nodes. By defining the positions of disconnected nodes and key nodes in the underwater intelligent Internet of Things as target positions, strategies such as repair node selection and virtual force guided movement are adopted. When the repair node is selected, the repair node begins to move to the target position. At the same time, after the position of the repair node changes, disconnected areas may appear in the network. In order to optimize the network connectivity, virtual force is used to guide the movement of the original neighbor nodes of the repair node to improve the network distribution. Therefore, under the action of the combined force, the node positions in the original neighborhood of the repair node are changed, thereby filling the gaps caused by removing the repair nodes and preventing the reduction of the coverage rate of the underwater intelligent Internet of Things.

[0108] The current underwater intelligent Internet of Things is difficult to ensure the connectivity of all network nodes. The method of the present application adjusts the position of network nodes by simulating the behavior of fish schools to optimize the coverage, and uses edge nodes to repair disconnected locations in the underwater acoustic sensor network, thereby eliminating key nodes and optimizing connectivity.

[0109] An embodiment of the present application also proposes an underwater intelligent Internet of Things topology reconstruction system that simulates a school of fish, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the underwater intelligent Internet of Things topology reconstruction method that simulates a school of fish as described above are implemented.

[0110] It should be noted that in the various embodiments of the present application, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0111] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0112] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0113] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for reconstructing the topology of an underwater intelligent Internet of Things that simulates a school of fish, characterized in that: include: Initialize underwater intelligent Internet of Things; Optimize the coverage of the initialized underwater smart Internet of Things based on fish school behavior; According to the underwater intelligent Internet of Things after coverage optimization, connectivity optimization is performed based on edge nodes to complete the reconstruction of the underwater intelligent Internet of Things after connectivity optimization.

2. The method for reconstructing the underwater intelligent Internet of Things topology by simulating a school of fish as claimed in claim 1, characterized in that: The coverage optimization of the initialized underwater smart IoT based on fish school behavior includes: The movement of the network nodes of the underwater intelligent Internet of Things after the fish school behavior configuration is initialized, wherein the target within the detection range of the network node is used as bait, and the movement of the network node toward the target is used as the behavior of the fish foraging for food. The network node adjusts the distribution area through the foraging behavior, tail-chasing behavior and aggregation behavior of the fish school.

3. The method for reconstructing the underwater intelligent Internet of Things topology by simulating a school of fish as claimed in claim 2, characterized in that: The coverage optimization of the initialized underwater intelligent Internet of Things based on the fish school behavior specifically includes: setting the network node detection range to a sphere with itself as the center and a radius of r, and the maximum movement step length is l step , the number of iterations is MaxIter, where: The number of targets within the monitoring range in the vicinity of the network node is used as the fitness, satisfying: N sensor (s i )=|{t j |d(s i ,t j )≤r s ,j=1,2,…,m}| (1) Among them, N sensor represents fitness, t j Indicates the target coverage time, s i represents artificial fish; Covering target time t j The number of sensors is taken as the coverage number, satisfying: Among them, J(·) represents the judgment function; The allowed congestion level around a certain location in the target monitoring area is taken as the congestion coefficient, which satisfies: in, is the expected coverage number of the target, N sensor (s i ) is at node s i The number of targets within the monitoring range, N neigh (s i ) is node s i The number of neighbor nodes, N near (s i ) indicates sensor s i The number of network nodes within coverage area.

4. The method for reconstructing the underwater intelligent Internet of Things topology by simulating a school of fish as claimed in claim 3, characterized in that: The coverage optimization of the initialized underwater intelligent Internet of Things based on the fish school behavior also includes the following node movement conditions: N neigh (s i )=0, start foraging behavior: according to the perception ability of artificial fish, the step length is limited to l step Within, the direction is random, and the position obtained by iterative calculation is: Among them, rand(l step ) represents from 0 to l step If the artificial fish is moved, it means that the network node is moving The movement of N sensor (s i ) increase, successful foraging, and failed foraging; N neigh (s i )>0, start tailgating behavior: find the best neighbor node s b ,Right now If N sensor (s b )>N sensor (s i ) and N sensor (s b )<δ(s b ), then the network node s b The number of coverage targets is greater than the number of network nodes s i The number of coverage targets is large, and the network nodes s i If the location is not congested, move the network node s i To network nodes b , do the following: If the network node s i Move to Make N sensor (s i ) increases, the rear-end collision is successful, otherwise the rear-end collision fails; N neigh (s i )>1, start the aggregation behavior: define the node center s c The location is Computing center nodes c The number of targets within the coverage area is Central Nodes c The number of network nodes within the coverage area is If N sensor (s c )>N sensor (s i ) and N sensor (s c )>δ(s c ), it indicates that the network node s c The number of coverage targets is greater than the number of network nodes s i The number of coverage targets, and the network nodes s c The location is not congested, and the network node s i Move to network node s c As a step, do the following: If the network node s i Move to Make N sensor (s i ) is increased, the aggregation behavior succeeds, otherwise the aggregation behavior fails.

5. The method for reconstructing the underwater intelligent Internet of Things topology by simulating a school of fish as claimed in claim 4, characterized in that: The coverage optimization of the initialized underwater intelligent Internet of Things based on the fish school behavior specifically includes: if N sensor (s i )=0, network node s i Move to the center of the target coverage area and repeat the node movement to perform node movement on each network node in the target area to complete the underwater intelligent Internet of Things coverage optimization.

6. The method for reconstructing the topology of an underwater intelligent Internet of Things simulating a school of fish as claimed in claim 5, characterized in that: According to the coverage-optimized underwater smart IoT, connectivity optimization based on edge nodes includes: Select the key nodes that need to be repaired; Generate and send a request message for executing a repair instruction, so that the edge node generates a request message; The key node generates a response message to cause the edge node to generate an acceptance message.

7. The method for reconstructing the topology of an underwater intelligent Internet of Things simulating a school of fish as claimed in claim 6, characterized in that: According to the underwater intelligent Internet of Things after coverage optimization, the connectivity optimization based on edge nodes also includes: The positions of disconnected nodes and key nodes of the underwater intelligent Internet of Things are taken as the target positions, and the repair node movement is guided by virtual force. A virtual artificial potential field is defined during the position movement process, wherein the virtual artificial potential field includes the gravitational force generated by the target, the repulsive force generated by the adjacent nodes, the repulsive force generated by the obstacles, and the random disturbance force, which respectively satisfy: Among them, k g , k r , and k o is the gravitational coefficient, m i and m j represents the quality of the network nodes and m i =m j , represents the Euclidean distance between the repair node and the target location, represents the Euclidean distance between two network nodes, Q represents the disturbance amplitude of the random disturbance force; During the process of adjusting the network node position, the neighbors of the repair node are affected by the gravitational force of the original position of the repair node and the force between nodes, satisfying: where k b and k c is the proportionality coefficient, d opt Represents the distance between network nodes. If d opt =1.78r 2 and r c >1.78r s , then it meets the connectivity; The resultant force of the original adjacent nodes of the repaired node satisfies: Under the action of the combined force, the positions of the nodes in the original neighborhood of the repair node are adjusted.

8. The method for reconstructing the topology of an underwater intelligent Internet of Things simulating a school of fish as claimed in claim 7, characterized in that: According to the underwater intelligent Internet of Things after coverage optimization, the connectivity optimization based on edge nodes also includes: After selecting the optimal node, obtain information about other network nodes in the connection branch; The nearest edge nodes are selected from random key nodes, and the domain topology of the key nodes is reconstructed using the triangulation mechanism.

9. An underwater intelligent Internet of Things topology reconstruction system that simulates a school of fish, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the fish school simulating underwater intelligent Internet of Things topology reconstruction method as described in any one of claims 1 to 8 are implemented.

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