Underwater wireless sensor network clustering method based on improved AP clustering

Abstract

The invention discloses an underwater wireless sensor network clustering method based on improved AP clustering, and relates to the field of underwater wireless sensor network topology optimization, and the method comprises the steps: initializing node feature data through obtaining node state information in an underwater wireless sensor network; a similarity calculation formula is improved, the inter-node distance, the received signal strength and the node residual energy are comprehensively considered, and the adaptability of the method to underwater is improved; an energy and position sensing mechanism is adopted, attraction information and affiliation information are iteratively updated, a preliminary cluster structure is formed, and the problems of node energy consumption and position change in the underwater complex environment are effectively solved; according to a node communication range, updating a preference value to adjust a clustering structure, and ensuring that no out-of-cluster node exists; a comprehensive weighting function of cluster head updating is designed, and bias of single factors is avoided. The method can better adapt to the complex underwater environment, effectively optimize the network topology structure, balance the energy consumption of the sensor nodes, and improve the stability and energy efficiency of the underwater wireless sensor network.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater wireless sensor network topology optimization, and in particular to a clustering method for underwater wireless sensor networks based on improved AP clustering. Background Art

[0002] With the rapid progress of marine technology and the increasing demand for marine resource development, underwater wireless sensor networks are increasingly widely used in the marine field. However, due to the complexity of the marine environment and the limited energy of nodes, designing a clustering method suitable for underwater wireless sensor networks is a huge challenge.

[0003] In recent years, with the development of machine learning, clustering algorithms have been applied to the clustering process of underwater wireless sensor networks. Usually, clustering algorithms are applied to the process of initial cluster formation. However, most existing algorithms ignore special factors in the underwater environment, such as the non-linear attenuation of acoustic wave propagation, multi-path effects, and the interference of ocean currents on signals, making the applicability of these algorithms in the underwater environment poor. In addition, considering the dynamic changes of nodes is also crucial. In the underwater environment, nodes may change their positions due to factors such as water flow and turbulence, and existing algorithms fail to adjust the network topology in time to cope with these changes, resulting in network interruption or data loss. At the same time, the unreasonable cluster head election causes uneven energy consumption of some nodes, seriously affecting the network lifetime. Especially in the underwater environment, sensor nodes usually rely on limited battery power supply, and balancing the energy consumption of sensor nodes has become a key factor determining the energy efficiency and lifetime of the network.

[0004] In summary, the current clustering-based clustering methods have problems such as poor adaptability to the underwater environment, poor network stability, and low network energy efficiency, which increase the difficulty of underwater wireless sensor network clustering design. Summary of the Invention

[0005] In order to overcome the above problems existing in the prior art, the present invention proposes a clustering method for underwater wireless sensor networks based on improved AP clustering.

[0006] The technical solution adopted by the present invention to solve its technical problems is: a clustering method for underwater wireless sensor networks based on improved AP clustering, including the following steps: Step 1, obtain the state information of nodes in the underwater wireless sensor network and initialize the characteristic data of the nodes; Step 2: Calculate the similarity between nodes by combining the relative remaining energy of the nodes, the received signal strength, and the physical distance between the nodes, construct the similarity matrix S of the underwater wireless sensor network, and set the preference value S(k,k); Step 3: Optimize the clustering of nodes through the similarity matrix S obtained in Step 2. Combine the energy awareness mechanism and the location awareness mechanism, and iteratively update the attraction information and the belonging information to form a preliminary cluster structure; Step 4: Adjust the cluster structure by updating the preference value S(k,k) according to the communication range between nodes to ensure that there are no off-cluster nodes; Step 5: Update the cluster head within the cluster according to the comprehensive weight of the nodes.

[0007] For the above-mentioned method for clustering underwater wireless sensor networks based on improved AP clustering, Step 2 is specifically as follows: ; ; ; ; ; ; Among them, represents the similarity between node i and node k; when i = k, is the preference value, and the preference value determines the number of clusters formed; and are the coordinates of node i and node k respectively; N is the total number of nodes in the sensor network; is the three-dimensional Euclidean distance between node i and node k; and are the spatial coordinates of the node; is the received signal strength, indicating the signal power magnitude received by node i from node k; is the transmission power of the sensor node, is the underwater acoustic channel propagation loss; is the normalization coefficient; d is the propagation distance between nodes; k is the expansion factor; is the absorption coefficient; is the relative remaining energy of the node, and its value reflects the current energy state of the node; is the current remaining energy of sensor node k, is the initial energy of node k.

[0008] For the above-mentioned method for clustering underwater wireless sensor networks based on improved AP clustering, Step 3 is specifically as follows: Iteratively update the attraction information and the belonging information through the energy awareness mechanism and the location awareness mechanism. When the change in the value of r(i,i)+a(i,i) for each node i is less than 10 -6 or when the maximum preset number of iterations is reached, stop the update; When a(i, i) + r(i, i) > T, node i is selected as the cluster head node; when a(i, i) + r(i, i) < T, node i is assigned to the cluster where the cluster head node with the maximum r(i, k) + a(i, k) is located. Here, T is a set threshold representing the standard for node cluster head selection; a(i, i) is the attraction information of node i, and r(i, i) is the belonging information of node i.

[0009] In the above clustering method for underwater wireless sensor networks based on improved AP clustering, the role of the energy awareness mechanism and the location awareness mechanism is to comprehensively consider the energy level and location change of nodes when updating the attraction information r(i, k) and the belonging information a(i, k), and dynamically adjust the network topology. The specific formula is: ; ; ; ; ; Among them, w(i) is a regulation factor that combines energy awareness and location awareness, used to dynamically adjust the message transmission frequency of nodes; is the energy awareness coefficient, used to reflect the influence of the remaining energy of nodes; is the current remaining energy of node i; is the maximum energy of the node; is the location awareness coefficient, used to represent the influence of node location change on message transmission; is the amplitude of location change of node i; is the maximum allowable amplitude of location change; λ is the weight factor of energy awareness and location awareness, 0 ≤ λ ≤ 1.

[0010] In the above clustering method for underwater wireless sensor networks based on improved AP clustering, the specific step 4 is: calculate the actual physical distance between each cluster head node h and its cluster member node m, and compare with the communication radius of the cluster head node. If there exists a node that satisfies , then go back to step 2 to adjust and update the preference value s(k, k); if there does not exist a node that satisfies , then enter step 5.

[0011] In the above clustering method for underwater wireless sensor networks based on improved AP clustering, the update formula of the preference value s(k, k) is specifically: ; Among them, is an adjustment factor that controls the amplitude of preference value adjustment.

[0012] The above-mentioned clustering method for underwater wireless sensor networks based on improved AP clustering, the specific step 5 is as follows: ; ; ; ; ; ; ; ; Among them, is the cluster head of each cluster; are all the nodes in cluster a; is the comprehensive weight of node k; is the energy weight of the node; is the current remaining energy of node k, is the maximum energy of the node; is the communication quality weight of the node; RSS(k) is the received signal strength of node k, is the propagation delay of node k; is the load weight of the node; is the frequency of transmitting data of node k; is the neighbor density weight of the node; is the number of neighbor nodes of node k, is the maximum number of neighbor nodes in the network; is the mobility weight of the node; is the average displacement, and the position change of the node within a certain time is used as the mobility index; T is the total length of the time series, and t is the time point, is the three-dimensional space coordinate of node k at time point t.

[0013] The beneficial effects of the present invention are as follows. First, the present invention initializes the characteristic data of nodes by obtaining the status information of nodes in the underwater wireless sensor network. Then, a method for improving the similarity calculation formula is proposed, comprehensively considering the distance between nodes, the received signal strength, and the remaining energy of nodes to improve the adaptability of the method to underwater environments. Secondly, an energy and position awareness mechanism is adopted to iteratively update the attraction information and the attribution information to form a preliminary cluster structure, effectively addressing the problems of node energy consumption and position changes in complex underwater environments. Next, according to the node communication range, the cluster structure is adjusted by updating the preference value to ensure that there are no off-cluster nodes. Finally, a weighted scoring function for updating the cluster head within the cluster is designed, considering various factors such as the communication quality between nodes, the load condition, and the neighbor density, to avoid the bias caused by a single factor and balance the energy consumption of sensor nodes.

[0014] The present invention enables the network to better adapt to complex underwater environments, effectively optimizes the network topology structure, balances the energy consumption of sensor nodes, and improves the stability and energy efficiency of the underwater wireless sensor network. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic flowchart of the present invention; Figure 2 is a schematic diagram of the result of the improved AP clustering algorithm of the present invention; Figure 3 is a schematic diagram for determining off-cluster nodes of the present invention; Figure 4 is a schematic diagram of the cluster structure of the underwater wireless sensor network of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] This embodiment proposes a clustering method for underwater wireless sensor networks based on improved AP clustering. The overall flowchart is as Figure 1 shown, and includes the following steps: Step 1: Obtain the status information such as the position, remaining energy, and transmission power of nodes in the underwater wireless sensor network, and initialize the characteristic data of the nodes.

[0018] Step 2: In the improved AP clustering algorithm, to better adapt to the underwater environment, the similarity matrix is initialized by combining the remaining energy of nodes, the signal strength, and the physical distance.

[0019] Step 2 is specifically as follows: By calculating the similarity between nodes, a similarity matrix S of the underwater wireless sensor network is constructed. The calculation of similarity comprehensively considers the physical distance between nodes, the received signal strength (RSS), and the relative remaining energy of nodes , to accurately evaluate the communication quality and energy status between nodes, and its formal definition is as follows: ; Among them, represents the similarity between node i and node k. When i = k, is called the preference value, which determines the number of clusters. and are the coordinates of node i and node k respectively, and N is the total number of sensor network nodes.

[0020] is the three-dimensional Euclidean distance between node i and node k, and its calculation formula is: ; Among them and are the spatial coordinates of the node.

[0021] is the received signal strength, indicating the magnitude of the signal power received by node i from node k, and its calculation formula is: ; Among them, is the transmission power of the sensor node, is the underwater acoustic channel propagation loss, and its calculation formula is: ; Among them, is the normalization coefficient, d is the propagation distance between nodes, k is the expansion factor, and the value of k is different in different scenarios. In practical applications, k = 1.5 is often taken.

[0022] is the absorption coefficient, which changes with the change of frequency f. According to different frequencies, it has two calculation formulas: ; is the relative remaining energy of the node, and its value reflects the current energy status of the node. The calculation formula is: ; Among them is the current remaining energy of sensor node k, is the initial energy of node k.

[0023] The generated similarity matrix S is used for the subsequent formation of the initial cluster structure. During the clustering process, the nodes are clustered and optimized through the similarity matrix S. Considering the underwater environmental factors, nodes with higher remaining energy, better communication quality, and relatively smaller distance from member nodes are preferentially selected as cluster head nodes. This strategy can better adapt to the complex underwater environment, ensure the balance of cluster head nodes in terms of energy consumption and communication performance, and thus improve the overall stability and data transmission efficiency of the network.

[0024] Step 3: To improve the network stability, combine the energy awareness mechanism and the location awareness mechanism, and iteratively update the attraction information and the membership information to form a preliminary cluster structure.

[0025] Step 3 is specifically as follows: In the dynamic environment of the underwater wireless sensor network, in order to better adapt to the complex node energy consumption and position change problems, a message update mechanism combining energy awareness and location awareness is introduced. The core of the energy awareness mechanism and the location awareness mechanism is to comprehensively consider the energy level and position change of nodes when updating the attraction information r(i,k) and the membership information a(i,k), so as to dynamically adjust the network topology. The specific formulas are as follows: ; ; where w(i) is a regulation factor combining energy awareness and location awareness, which is used to dynamically adjust the message transmission frequency of nodes. It is defined as follows: .

[0026] is the energy awareness coefficient, which is used to reflect the influence of the remaining energy of nodes. The formula is as follows: ; where, is the current remaining energy of node i, is the maximum energy of the node.

[0027] is the location awareness coefficient, which is used to represent the influence of node position change on message transmission. The formula is as follows: ; where, is the position change amplitude of node i, is the maximum allowable position change amplitude.

[0028] λ is the weight factor of energy awareness and location awareness, satisfying 0 ≤ λ ≤ 1. By adjusting the value of λ, a balance can be achieved between energy priority and location priority.

[0029] The schematic diagram of the improved AP clustering algorithm result is asFigure 2 As shown, the algorithm iteratively updates the attraction information and the membership information through the above formula. When the change in the value of r(i, i) + a(i, i) for each node i is less than 10 -6 or reaches the maximum preset number of iterative times, the update stops. When a(i, i) + r(i, i) > T, node i is selected as the cluster head node. When a(i, i) + r(i, i) < T, node i is assigned to the cluster where the cluster head node with the maximum r(i, k) + a(i, k) is located. Among them, T is a set threshold, indicating the standard for node selection of the cluster head. In practical applications, it is necessary to adjust the threshold T according to environmental factors such as the node energy level, communication quality, and underwater noise of the underwater network to adapt to different working scenarios.

[0030] Step 4: According to the communication range between sensor nodes, adjust the clustering structure by updating the preference value to ensure that there are no off-cluster nodes.

[0031] Specifically, Step 4 is as follows: First, calculate the actual physical distance between each cluster head node h and its cluster member node m. The calculation formula is as follows: ; Among them and are the three-dimensional space coordinates of member node m and cluster head node h respectively.

[0032] Secondly, the schematic diagram for judging off-cluster nodes is as Figure 3 shown. To ensure that all member nodes m can communicate effectively with the cluster head node h, it is necessary to compare with the communication radius of the cluster head node. The judgment condition is: ; If there is no node that satisfies this condition, it means that the cluster head node and the member nodes can all communicate effectively, and then directly proceed to the next step S5; if there is a node that satisfies this condition, it means that node i cannot communicate effectively with the cluster head node k due to excessive distance, and then it is necessary to return to step S2 to adjust and update the preference value s(k, k). If the distance between the cluster member node and the cluster head node is greater than the communication range, it means that the current preference value s(k, k) is set too large, and the preference value needs to be reduced. The preference value update formula is as follows: ; Among them is the adjustment factor, which controls the adjustment amplitude of the preference value.

[0033] The schematic diagram of the clustering structure of the underwater wireless sensor network is as Figure 4As shown in the figure, after clustering through the above steps, a certain communication relationship is formed between the cluster head node and the member nodes. The cluster head node is responsible for collecting the data of the member nodes within the cluster and transmitting it to the sink node or the upper-layer network to achieve effective data aggregation and transmission. Then, preliminary processing and fusion of the data within the cluster are carried out to reduce data redundancy and transmission overhead. However, due to the complexity of the underwater environment and the dynamic changes of the node states, the performance of the cluster head node may be affected by various factors, such as energy depletion, communication quality degradation, etc. Therefore, in order to ensure the long-term stable operation of the network and efficient data transmission, it is necessary to dynamically update the cluster head node according to the comprehensive performance of the nodes.

[0034] Step 5: Update the cluster head within the cluster by comprehensively considering various factors such as the communication quality, load condition, neighbor density, and mobility among nodes.

[0035] In the cluster head update stage, various factors such as the remaining energy of the nodes, communication quality, load condition, neighbor density, and mobility are comprehensively considered. The introduction of these factors can make the selection of the cluster head more reasonable, avoid the bias of a single factor, and improve the stability and energy efficiency of the network. The cluster head of each cluster is updated and selected by the following formula. According to the comprehensive weight of the nodes, the node with the maximum weight is selected as the cluster head. The calculation formula is: ; where are all the nodes in cluster a.

[0036] is the comprehensive weight of node k, considering factors such as energy, communication quality, neighbor density, and mobility. The calculation formula is: .

[0037] is the energy weight of the node. The calculation formula is: ; where is the current remaining energy of node k, is the maximum energy of the node.

[0038] is the communication quality weight of the node. The calculation formula is: ; where RSS(k) is the received signal strength of node k, is the propagation delay of node k.

[0039] is the load weight of the node. The calculation formula is: ; wherein is the frequency of the transmission data of node k.

[0040] is the neighbor density weight of the node, and the calculation formula is: ; wherein is the number of neighbor nodes of node k, is the maximum number of neighbor nodes in the network.

[0041] is the mobility weight of the node, and the calculation formula is: ; wherein is the average displacement. The position change of the node within a certain time is used as the mobility index, and the calculation formula is as follows: ; where T is the total length of the time series, t is the time point, is the three-dimensional space coordinate of node k at time point t.

[0042] The above embodiments are only exemplary embodiments of the present invention and are not used to limit the present invention. Those skilled in the art can make various modifications or equivalent replacements within the essence and protection scope of the present invention, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present invention.

Claims

1. The underwater wireless sensor network clustering method based on improved AP clustering is characterized by: It includes the following steps: Step 1: Obtain the status information of nodes in the underwater wireless sensor network and initialize the feature data of the nodes; Step 2: Calculate the similarity between nodes by combining the relative remaining energy, received signal strength, and physical distance between nodes, construct the similarity matrix S of the underwater wireless sensor network, and set the preference value S(k, k); Step 3: Cluster and optimize the nodes through the similarity matrix S obtained in Step 2, combine the energy awareness mechanism and the location awareness mechanism, iteratively update the attraction information and the belonging information, and form a preliminary cluster structure; Step 4: Adjust the cluster structure by updating the preference value S(k, k) according to the communication range between nodes to ensure that there are no off-cluster nodes; Step 5: Update the cluster head within the cluster according to the comprehensive weight of the nodes.

2. The underwater wireless sensor network clustering method based on improved AP clustering according to claim 1, characterized in that: The specific content of Step 2 is as follows: ; ; ; ; ; ; in, Represents the similarity between node i and node k; when i=k, is the preference value, which determines the number of clusters; and are the coordinates of node i and node k respectively; N is the total number of sensor network nodes; is the three-dimensional Euclidean distance between node i and node k; and is the spatial coordinate of the node; is the received signal strength, which indicates the signal power received by node i from node k; is the transmit power of the sensor node, is the propagation loss of the underwater acoustic channel; is the normalization coefficient; d is the propagation distance between nodes; k is the expansion factor; is the absorption coefficient; is the relative remaining energy of the node, and its value reflects the current energy status of the node; is the current remaining energy of sensor node k, is the initial energy of node k.

3. The underwater wireless sensor network clustering method based on improved AP clustering according to claim 1, characterized in that: The step 3 is specifically as follows: the attraction information and the belonging information are updated iteratively through the energy perception mechanism and the location perception mechanism. When the value of r(i,i)+a(i,i) of each node i changes by less than 10 -6 Or stop updating when the maximum preset number of iterations is reached; When a(i, i) + r(i, i) > T, then node i is selected as the cluster head node; when a(i, i) + r(i, i) < T, then node i is assigned to the cluster where the cluster head node with the maximum r(i, k) + a(i, k) is located, where T is a set threshold representing the standard for node selection of the cluster head; a(i, i) is the attraction information of node i, and r(i, i) is the belonging information of node i.

4. The underwater wireless sensor network clustering method based on improved AP clustering according to claim 3, characterized in that: The role of the energy awareness mechanism and the location awareness mechanism is to comprehensively consider the energy level and location change of nodes when updating the attraction information r(i, k) and the belonging information a(i, k), and dynamically adjust the network topology. The specific formula is: ; ; ; ; ; Among them, w(i) is a regulatory factor that combines energy awareness and location awareness, which is used to dynamically adjust the message transmission frequency of the node; is the energy perception coefficient, which is used to reflect the influence of the node's remaining energy; is the current remaining energy of node i; is the maximum energy of the node; is the position awareness coefficient, which is used to represent the impact of node position change on message transmission; is the magnitude of the position change of node i; is the maximum allowable position change; λ is the weight factor of energy perception and position perception, 0≤λ≤1.

5. The underwater wireless sensor network clustering method based on improved AP clustering according to claim 1, characterized in that: The step 4 specifically includes: calculating the actual physical distance between each cluster head node h and its cluster member node m ,Will Communication radius with cluster head node Compare, if there is a node satisfying , then return to step 2 to adjust the updated preference value s(k, k); if there is no node satisfying , then go to step 5.

6. The underwater wireless sensor network clustering method based on improved AP clustering according to claim 5, characterized in that: The update formula of the preference value s(k, k) is specifically: ; in, is the adjustment factor, which controls the magnitude of the adjustment of the preference value.

7. The underwater wireless sensor network clustering method based on improved AP clustering according to claim 1, characterized in that: The specific content of Step 5 is as follows: ; ; ; ; ; ; ; ; in, is the cluster head of each cluster; are all nodes in cluster a; is the comprehensive weight of node k; is the energy weight of the node; is the current remaining energy of node k, is the maximum energy of the node; is the communication quality weight of the node; RSS(k) is the received signal strength of node k, is the propagation delay of node k; is the load weight of the node; is the frequency of data transmission of node k; is the neighbor density weight of the node; is the number of neighbor nodes of node k, is the maximum number of neighbor nodes in the network; is the mobility weight of the node; is the average displacement, and the position change of the node within a certain period of time is used as the mobility index; T is the total length of the time series, t is the time point, is the three-dimensional space coordinate of node k at time point t.

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