Underwater data acquisition method and system

Abstract

The invention discloses an underwater data acquisition method and system, and relates to the field of underwater sensor networks, and the method comprises the steps: dividing a network topology structure with underwater sensors as network nodes into a plurality of clusters, and selecting cluster head nodes; predicting the follow-up moving position of each underwater sensor through a multi-layer water flow model, and obtaining the data information value quantity of each underwater sensor when the underwater sensor moves along with water flow through a data information value quantity model; under the condition that the transmission condition is met, transmitting the data to a cluster head node in a multi-hop transmission mode; and carrying out data acquisition by using the predicted moving position of each cluster head node in the subsequent time when the AUV sails to the predicted moving position, and selecting an acquisition path with the shortest sailing time from all the acquisition paths to complete data acquisition of all the cluster head nodes. According to the invention, accurate data collection can be carried out based on actual dynamic changes of underwater network nodes.

Description

Technical Field

[0001] The present invention relates to the field of underwater sensor networks, and in particular to a method and system for collecting underwater data. Background Art

[0002] Data collection is a very important research area in underwater sensor networks. For underwater monitoring systems, whether it is pollutant monitoring, water quality monitoring or underwater biological monitoring, underwater data must be collected as quickly as possible. In the data collection process of underwater networks, we not only pay attention to network energy consumption and delay issues, but also pay attention to the decay of the value of data information. Value of Information (VoI), as an important indicator reflecting the importance and time sensitivity of node perception data, is a factor that is considered. For time-sensitive data, its VoI decays faster over time. The decay of VoI over time requires collecting as many VoIs as possible with less energy consumption. In addition, node movement causes dynamic changes in network topology, making it difficult to form stable clusters. Unstable network clustering leads to the location and number of cluster head nodes being unfixed, which not only increases the difficulty of data collection, but also increases the delay of data collection, resulting in a reduction in the value of the data information collected in the end.

[0003] In the prior art, data is collected through a multi-hop data collection algorithm and an autonomous underwater vehicle (AUV)-assisted data collection algorithm; in the AUV-assisted data collection algorithm, the AUV needs to access all sensor nodes to collect data, resulting in a long delay in data collection and a serious loss of the value of the data information finally collected at the aggregation node. Although the multi-hop data collection algorithm has a small data collection delay and a small attenuation of information value, the node energy consumption is uneven, which can easily cause the energy of nodes with large amounts of forwarded data to be exhausted prematurely, affecting the life of the network. In addition, the above two prior arts are both aimed at the ideal state of a dynamic network, assuming that no matter how a node moves, it can communicate with other nodes and transmit data to the destination node, ignoring the dynamic changes in the network topology caused by the movement of nodes with the water flow.

[0004] In summary, how to collect data efficiently and with low energy consumption while taking into account the dynamic changes of actual network nodes is an important issue that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present invention provide a method and system for collecting underwater data, which can solve the problem in the prior art that it is impossible to collect data with high efficiency and low energy consumption while taking into account the dynamic changes of actual network nodes.

[0006] An embodiment of the present invention provides a method for collecting underwater data, comprising the following steps:

[0007] The multiple underwater sensors in the moving process are used as network nodes to construct the network topology structure, and the relevant data of each network node during communication is obtained, as well as the data information value VoI used to represent the value of the data collected by the underwater sensor when each network node moves with the water flow;

[0008] Dynamically cluster the network topology: In the network topology, multiple network nodes that can communicate with each other are regarded as a cluster, so that the network topology is divided into multiple clusters; in each cluster, according to the residual energy, data packet size and VoI of each network node during communication, the probability of each network node being selected as the cluster head node is obtained using the cluster head node selection probability model, and the network node corresponding to the maximum probability is selected from all the probabilities as the cluster head node;

[0009] In each cluster after clustering, the data of each network node is transmitted to the cluster head node to realize data transmission: according to the historical coordinates of each network node, the future coordinates of each network node are predicted by a multi-layer water flow model that simulates the movement of water flow; when each network node communicates with another network node, the transmission delay model is used to obtain the transmission delay of the data packet according to the channel capacity and data packet size of the network node during communication, as well as the distance between the coordinates of the two communicating network nodes; according to the transmission delay, the transmission probability model is used to obtain the data packet transmission probability between the two communicating network nodes; within the transmission delay, when the transmission probability is greater than the set threshold and the VoI is greater than zero, it is determined that the data packet is successfully transmitted; each network node can successfully transmit the data packet to multiple network nodes, and multiple network nodes are used as candidate forwarding node sets; when each network node transmits data, it selects a candidate forwarding node that meets the conditions from the candidate forwarding node set, and transmits it through a multi-hop transmission method until the cluster head node receives the transmission data of all network nodes;

[0010] An autonomous underwater vehicle (AUV) is used to collect data from the cluster head nodes of each cluster. The future coordinates of each cluster head node are predicted through a multi-layer water flow model based on the historical coordinates of each cluster head node. The AUV is used to regularly navigate to the predicted future coordinates of each cluster head node for data collection, and the collection path with the shortest navigation time is selected during collection.

[0011] Furthermore, the multi-layer water flow model that simulates water flow movement is used to predict the future coordinates of each network node, and the specific steps include:

[0012] Set the underwater monitoring area as a cuboid and establish a coordinate system for water flow movement;

[0013] The multi-layer water flow model is formulated as follows:

[0014]

[0015] Among them, v i represents the network node, t represents the time, u h represents the water velocity of the hth water layer, α h It represents the angular deviation of the underwater sensor on the x-axis when it moves with the water flow. (x vi (0),y vi (0),z vi (0)) represents the coordinates of the network node at the initial time t = 0, ξ is the inertia parameter of the system, (x vi (t),y vi (t),z vi (t)) represents the coordinates of the network node at time t.

[0016] Furthermore, the step of determining that the data packet is successfully transmitted specifically includes:

[0017] According to the channel capacity of the network node during communication, the size of the data packet and the predicted future coordinates of each network node, the transmission delay existing when two network nodes communicate is obtained through the transmission delay model;

[0018] The transmission delay model is formulated as follows:

[0019]

[0020] Among them, D c (i,j,t) represents the network node v at time t i With network node v j The transmission delay between b is the channel capacity, v uw represents the transmission speed of underwater acoustic signals, d(v i ,v j ,t) represents the network node v at time t i and are working with v i Communication network nodes v j The distance between them, and d(v i ,v j ,t)≤Rc, Rc is the communication radius of the network node. tran (i,t) is the node v i The data packet size that can be transmitted has a value not equal to 0;

[0021] When each network node transmits data to another network node, within the transmission delay, the transmission probability model is used to obtain the transmission probability;

[0022] The transmission probability model is formulated as follows:

[0023] P c (i,j,t)=(1-Pe (i,j,t)) b

[0024]

[0025] Among them, P c (i, j, t) represents the transmission probability, b represents the data packet transmitted by the node is b bits, P e (i,j,t) indicates that the distance between network nodes at time t is d(v i ,v j ,t) is the packet transmission bit error rate; Γ(i,j,t) is the error rate of network node v at time t. i To network node v j The signal-to-noise ratio of the sent data packet; node v i The transmission power and communication frequency are p t and f; Bw represents the noise bandwidth of the receiving end, N(f) is the underwater environmental noise; A(d(v i ,v j ,t),f) represents the sensor node v at time t i The frequency f is used to distance d(v i ,v j ,t) of the network node v j Path loss for transmitting packets;

[0026] When the transmission probability is greater than the threshold and the data information value is greater than zero, it is determined that the transmission conditions are met, and the network node v i Able to send network nodes v j Data transferred successfully.

[0027] Furthermore, each network node selects a candidate forwarding node that meets the conditions from the candidate forwarding node set when transmitting data, and the specific steps include:

[0028] Select a candidate forwarding node v from the candidate forwarding node set k ;

[0029] At time t, node v k The probability of being selected as a candidate forwarding node is:

[0030]

[0031] Among them, E res (k,t) is the network node v k The remaining energy at time t, k is the serial number of the network node, E0 is the network node v k The initial energy, λ1 is the system parameter, is the candidate forwarding node v at time t k Network node vi The matrix closer to the cluster head node set Q;

[0032]

[0033] in, represents the network node v at time t i The distance matrix to the cluster head node set Q, represents the candidate forwarding node v at time t k The matrix of distances to the cluster head node set Q;

[0034] And, the candidate forwarding node v k The remaining energy of the receiving node v i The energy consumed by the data packet and the transmission node v i Data packets and nodes v k The sum of the energy consumed by the data packets themselves;

[0035] The residual energy is expressed as follows:

[0036]

[0037] Among them, p t represents the transmission power of the network node, p r represents the received power, u t Indicates the data transmission rate, u r represents the data receiving rate, A(Rc,f) is the candidate forwarding node v k The path loss of sending data packets to network nodes within the communication radius Rc using signal frequency f.

[0038] Furthermore, the step of selecting the collection path with the shortest navigation time during collection includes:

[0039] The cluster head node sends a response packet containing cluster head node information to the AUV;

[0040] According to the cluster head node information in the response packet, the multi-layer water flow model is used to predict the subsequent moving position of each cluster head node;

[0041] According to the historical navigation state of the AUV, a state model capable of determining the future navigation state of the AUV is used to obtain data related to the future navigation state of the AUV; wherein the navigation state includes a navigation position and a navigation attitude;

[0042] The state model is formulated as follows:

[0043]

[0044] Among them, (x a (t),y a(t),z a (t)) is the coordinate of the AUV at time t, φa(t) is the heading angle of the AUV, φ'a(t) is the direction of the AUV on the xy plane, V auv Indicates the moving speed of the AUV;

[0045] When predicting the future coordinates of each cluster head node, the value of data information must be greater than zero;

[0046] According to the future navigation state of the AUV and the predicted future coordinates of each cluster head node, the encounter time between the AUV and the predicted future coordinates of each cluster head node is determined;

[0047] The shortest encounter time is selected from the encounter times, and the cluster head node corresponding to the shortest encounter time is the target node that the AUV visits first;

[0048] The AUV moves to the predicted future coordinates of the target node and collects data from all cluster head nodes and data from each underwater sensor received by each cluster head node.

[0049] An embodiment of the present invention provides an underwater data collection system, comprising:

[0050] The data acquisition module is used to construct a network topology structure by taking multiple underwater sensors in the moving process as network nodes, and to obtain the relevant data of each network node during communication, as well as the data information value VoI used to represent the value of the data collected by the underwater sensor when each network node moves with the water flow;

[0051] The cluster head node selection module is used to dynamically cluster the network topology structure: in the network topology structure, multiple network nodes that can communicate with each other are regarded as a cluster, so that the network topology structure is divided into multiple clusters; in each cluster, according to the residual energy, data packet size and VoI of each network node during communication, the probability of each network node being selected as the cluster head node is obtained using the cluster head node selection probability model, and the network node corresponding to the maximum probability is selected from all probabilities as the cluster head node;

[0052] The data transmission module is used to transmit the data of each network node to the cluster head node in each cluster after clustering to realize data transmission: according to the historical coordinates of each network node, the future coordinates of each network node are predicted by a multi-layer water flow model that simulates the movement of water flow; when each network node communicates with another network node, the transmission delay model is used to obtain the transmission delay of the data packet according to the channel capacity and data packet size of the network node during communication, and the distance between the coordinates of the two communicating network nodes; according to the transmission delay, the transmission probability model is used to obtain the data packet transmission probability between the two communicating network nodes; within the transmission delay, when the transmission probability is greater than the set threshold and the VoI is greater than zero, it is determined that the data packet is successfully transmitted; each network node can successfully transmit the data packet to multiple network nodes, and multiple network nodes are used as a candidate forwarding node set; when each network node transmits data, a candidate forwarding node that meets the conditions is selected from the candidate forwarding node set, and the data is transmitted through a multi-hop transmission method until the cluster head node receives the transmission data of all network nodes;

[0053] The data collection module is used to collect data from the cluster head nodes of each cluster using an autonomous underwater vehicle (AUV): based on the historical coordinates of each cluster head node, the future coordinates of each cluster head node are predicted through a multi-layer water flow model; the AUV is used to regularly navigate to the predicted future coordinates of each cluster head node for data collection, and the collection path with the shortest navigation time is selected during collection.

[0054] The embodiment of the present invention provides a method and system for collecting underwater data. Compared with the prior art, the beneficial effects thereof are as follows:

[0055] The multiple underwater sensors in the process of movement are used as network nodes to construct the network topology structure, and the network topology structure is dynamically clustered to obtain multiple clusters. A network node is selected in each cluster as the cluster head node.

[0056] According to the historical coordinates of each network node, the future coordinates of each network node are predicted by a multi-layer water flow model that simulates water flow movement; in each cluster, the communication between each network node and another network node is established, and the transmission delay and transmission probability during the communication process, as well as the VoI of each network node, are obtained; each network node can successfully transmit data packets to multiple network nodes, and select a network node that meets the conditions as a candidate forwarding node from multiple network nodes that can be transmitted, and transmit data through multi-hop transmission until the cluster head node receives the transmission data of all network nodes in the cluster where the cluster head node is located;

[0057] The future coordinates of each cluster head node are predicted by a multi-layer water flow model, and an AUV is used to regularly sail to the predicted future coordinates of each cluster head node for data collection, and the collection path with the shortest sailing time is selected during collection.

[0058] The multi-layer water flow model simulates the actual dynamic changes of underwater sensors as they move with the water flow. During the dynamic changes of underwater sensors, the multi-hop data collection algorithm has a small data collection delay and a small attenuation of information value. The AUV is used to collect data along the shortest path, which shortens the collection time. This allows underwater sensors to complete data collection in an efficient and low-energy manner under actual dynamic changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A system model diagram provided for an embodiment of the present invention;

[0060] Figure 2 A data collection algorithm framework based on dynamic network topology and VoI constraints provided by an embodiment of the present invention;

[0061] Figure 3 A data collection flow chart based on dynamic network topology and VoI constraints provided by an embodiment of the present invention;

[0062] Figure 4 A data transmission flow chart based on transmission delay provided by an embodiment of the present invention;

[0063] Figure 5 A data collection delay curve diagram under different numbers of nodes provided by an embodiment of the present invention;

[0064] Figure 6 A graph showing the changing trend of network lifespan under different numbers of nodes provided by an embodiment of the present invention;

[0065] Figure 7 A curve diagram of data packet collection rate under different numbers of nodes provided by an embodiment of the present invention;

[0066] Figure 8 A curve diagram showing the variation of the VoI of the final collected data with the number of sensor nodes provided by an embodiment of the present invention;

[0067] Fig. 9 A histogram showing the change in unit energy consumption with the number of nodes provided in an embodiment of the present invention;

[0068] Fig.10 A curve diagram showing the variation of data collection delay with AUV moving speed provided by an embodiment of the present invention;

[0069] Fig.11 A data collection delay curve diagram under different node communication radius provided by an embodiment of the present invention;

[0070] Fig.12A trend diagram of the influence of water flow velocity on data collection delay provided in an embodiment of the present invention, wherein (a) is the influence of water flow velocity and AUV moving speed on data collection delay, and (b) is the influence of water flow velocity and node communication radius on data collection delay. DETAILED DESCRIPTION

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0072] See also Figure 1 The embodiment of the present invention provides a method for collecting underwater data, comprising the following steps:

[0073] Step 1: Use multiple underwater sensors in the mobile process as network nodes to build a network topology structure, obtain relevant data of each network node during communication, and the data information value VoI used to represent the value of data collected by the underwater sensor when each network node moves with the water flow.

[0074] Step 2: Dynamically cluster the network topology: In the network topology, multiple network nodes that can communicate with each other are regarded as a cluster, so that the network topology is divided into multiple clusters; in each cluster, according to the residual energy, data packet size and VoI of each network node during communication, the cluster head node selection probability model is used to obtain the probability of each network node being selected as the cluster head node, and the network node corresponding to the maximum probability is selected from all probabilities as the cluster head node.

[0075] Step 3: In each cluster after clustering, the data of each network node is transmitted to the cluster head node to realize data transmission: according to the historical coordinates of each network node, the future coordinates of each network node are predicted by a multi-layer water flow model that simulates water flow movement; when each network node communicates with another network node, the transmission delay model is used to obtain the transmission delay of the data packet according to the channel capacity and data packet size of the network node during communication, as well as the distance between the coordinates of the two communicating network nodes; according to the transmission delay, the transmission probability model is used to obtain the data packet transmission probability between the two communicating network nodes; within the transmission delay, when the transmission probability is greater than the set threshold and the VoI is greater than zero, it is determined that the data packet is successfully transmitted; each network node can successfully transmit the data packet to multiple network nodes, and multiple network nodes are used as candidate forwarding node sets; when each network node transmits data, it selects a candidate forwarding node that meets the conditions from the candidate forwarding node set, and transmits it through a multi-hop transmission method until the cluster head node receives the transmission data of all network nodes.

[0076] Step 4: Use an autonomous underwater vehicle (AUV) to collect data from the cluster head nodes of each cluster: Based on the historical coordinates of each cluster head node, the future coordinates of each cluster head node are predicted through a multi-layer water flow model; the AUV is used to regularly navigate to the predicted future coordinates of each cluster head node for data collection, and the collection path with the shortest navigation time is selected during collection.

[0077] The underwater data collection algorithm designed by the present invention based on dynamic network topology and information value VoI constraint is divided into three stages, namely, dynamic clustering stage, data transmission stage and data collection stage:

[0078] (1) Dynamic clustering stage. The multi-layer water flow model is used to simulate the movement trajectory of sensor nodes. The network topology is obtained through two-way information exchange technology during the node movement, and the network is dynamically clustered. The process is as follows: First, the surface aggregation node sends a beacon data packet to the network node. After receiving the beacon data packet, the node broadcasts it to all neighboring nodes within its communication range. Nodes that can communicate with each other form a cluster, so as to obtain the network topology and the number of clusters of the cluster where the node is located. Then, a cluster head node is selected based on the network structure of the cluster, the remaining energy of the node, the size of the data packet that the node can transmit, and VoI. Node v i The probability of being selected as the cluster head node at time t is P CH (i,t):

[0079]

[0080] Among them, E res (i,t) is the node v i The residual energy at time t, E0 is the initial energy of the node, Cz (i) is the closeness centrality of the node, b tran (i,t) is the node v i The size of the data packet that can be transmitted with VoI not equal to 0, including node v i The generated data packets also include data packets received from other nodes whose VoI is not equal to 0, and the system parameter is 0<γ<1.

[0081] The proximity centrality of a sensor node is defined as i The reciprocal of the average length of the shortest path between nodes in the cluster:

[0082]

[0083] Among them, n m is node v i The number of nodes in the cluster, e num (i,k) is the slave node v i to v k The number of edges contained in the shortest path, C m is node v i The cluster.

[0084] The node with the largest probability value will be selected as the cluster head node. The mobility of nodes causes the network clustering and the selection of cluster head nodes to change dynamically.

[0085] (2) Data transmission phase. Sensor node v i During the movement, the topological structure of the cluster at the current moment, node location coordinates, remaining energy, data packet size, VoI, and neighbor node information are determined based on the multi-layer water flow model and two-way information exchange technology. There is a transmission delay when the mobile sensor node transmits data to the neighbor node, and the calculation formula is:

[0086]

[0087] Among them, c b is the channel capacity, v uw represents the transmission speed of underwater acoustic signals, d(v i ,v j ,t) represents the node v at time t i and v j The distance, and d(v i ,v j ,t)≤Rc, Rc is the communication radius of the sensor node. tran (i,t) is the node v i The size of the data packet that can be transmitted with VoI not equal to 0, including node v i The generated data packets also include data packets received from other nodes whose VoI is not equal to 0.

[0088] At node v i To node v j When transmitting a data packet, if the transmission probability of the data packet within the transmission delay is greater than the preset threshold p0, it can be considered that the data packet is successfully transmitted to the node v j . At time t, node v i To node v j The transmission probability of transmitting a bbit data packet can be expressed as:

[0089] P c (i,j,t)=(1-P e (i,j,t)) b

[0090] Among them, P e (i,j,t) indicates the distance between sensor nodes at time t is d(v i ,v j ,t), the data packet transmission bit error rate can be calculated using the following formula:

[0091]

[0092] Where Γ(i,j,t) represents the node v at time t i To node v j The signal-to-noise ratio of the data packet sent. i The transmission power and communication frequency are p t When and f, the signal-to-noise ratio Γ(i,j,t) can be expressed as:

[0093]

[0094] Where Bw is the noise bandwidth of the receiving end, and N(f) is the underwater environmental noise, which usually includes turbulence noise Nt(f), ship noise Ns(f), wind and wave noise Nw(f) and thermal noise Nth(f). i ,v j ,t),f) represents the sensor node v at time t i The frequency f is used to distance d(v i ,v j ,t) of sensor node v j Path loss for transmitting data packets.

[0095] When the mobile sensor node v i ∈V to neighboring sensor node v j When transmitting data (d(v i ,v j ,t)≤Rc), first calculate whether the packet transmission probability Pc(i,j,t) at the current time t is greater than the threshold p0. Sensor node vi Predict the neighboring mobile node v in the subsequent time based on the recorded historical location information of the neighbor node and the received historical location information of the neighbor node j The position and calculation of the transmission delay period If the packet transmission probability within the transmission delay period is greater than the predefined threshold p0 and VoI is greater than 0, then the mobile sensor node v j Can be used as mobile sensor node i The candidate forwarding node of the next hop node. The candidate forwarding node can be calculated by the following formula

[0096]

[0097] Among them, the sensor node v j is the sensor node v at time t i The neighbor node of sensor node v i The VoI of the packet at time t+Δt. All candidate forwarding nodes at time t The sensor node v i The set of candidate forwarding nodes

[0098] At time t, when the mobile sensor node v i When transmitting data to neighboring nodes, you can select from the candidate node set Select the next hop forwarding node Its remaining energy must be sufficient to forward node v i data, and compared to node v i , sensor node v k Closer to the cluster head node set Q. At time t, node v k The probability of being selected as a forwarding node can be expressed as:

[0099]

[0100] Among them, 0<λ1<1, is the candidate forwarding node v at time t k Than the current sensor node v i The 1-row M-column matrix closer to the cluster head node set Q, that is, the current node v i and candidate forwarding node v k The distance difference to all cluster head nodes is the same as the distance to node v i The matrix of the ratio of the distances to the cluster head nodes.

[0101]

[0102] Represents the node v at time t i The distance matrix to the cluster head node Q. Similarly, represents the candidate node v at time t k The matrix of Q distances to all cluster head nodes.

[0103] If at time t the node v k is selected as a forwarding node, then its remaining energy is not less than that of the receiving node v i The energy consumed by the data packet and the transmission node v i Data packets and nodes v k The sum of the energy consumed by the data packets themselves. That is, the candidate forwarding node v k The remaining energy must satisfy the following formula.

[0104]

[0105] Among them, p t represents the transmission power of the sensor node, p r represents the received power, u t Indicates the data transmission rate, u r represents the data receiving rate, A(Rc,f) is the node v k The path loss of sending data packets to neighboring nodes within the communication radius Rc using the signal frequency f. Select the node v with the largest probability that the remaining energy satisfies the energy formula. k As node v i The forwarding node at time t.

[0106] (3) Data collection phase. The AUV dives underwater regularly to collect data from dynamic cluster head nodes. Before starting data collection, the AUV sends a beacon data packet to the underwater node. All nodes storing data are recorded as cluster head nodes, and the cluster head node ID, location coordinates, data packet size, VoI and other information are recorded. After receiving the control packet, the cluster head node returns a response packet including cluster head node information and neighbor table to the AUV. The AUV determines which cluster head node data to collect first based on the received response packet. First, the trajectory of the cluster head node during movement and the location coordinates at future times are calculated based on the historical information of the cluster head node. Then, the state of the AUV at different times is determined, and the time when the AUV can meet the dynamic cluster head node is calculated. The cluster head node with the shortest time is selected as the target node to be visited first. The AUV moves directly to the predicted location of the cluster head node to collect cluster head node data. The relevant information of the cluster head node recorded in the AUV record table is updated based on the collected neighbor table information of the cluster head node, and then the location and movement trajectory of the next cluster head node to be visited that is closest to the AUV are predicted.

[0107] The AUV dives underwater at specific intervals to access and collect data from the mobile cluster head node, and then returns to the surface aggregation node. Subsequent sensed data will be collected when the AUV dives underwater again.

[0108] The performance of the DCADTV solution of the present invention is analyzed below.

[0109] 1. Experimental results analysis

[0110] The DCADTV scheme is evaluated using MATLAB and compared with the performance of existing schemes. Nv underwater sensor nodes are randomly deployed in a 103m×103m×500m three-dimensional underwater monitoring area. The coordinates of the surface aggregation node are assumed to be (500,500,500). The initial coordinates of the AUV are assumed to be (500,500,500). The moving speed of the AUV is v auv The present invention records the water flow velocity of u=1m / s as the general velocity, the water flow velocity of u=2m / s as the faster velocity, and the water flow velocity of u=0.5m / s as the water flow approaching a stationary state during simulation.

[0111] The present invention compares the performance of LP-DDCA, AEEDCO, HAMA algorithms with the DCADTV algorithm in a dynamic network proposed by the present invention. LP-DDCA is a dynamic AUV-assisted data collection algorithm based on position prediction, in which the AUV plans its moving trajectory according to the predicted sensor node position, reducing the path length of the AUV as the node moves continuously. In the AEEDCO algorithm, the cluster head node is first selected according to the energy, and then the TSP algorithm is used to plan the path for the AUV to access the cluster head node, and the AUV only collects data from the cluster head node. The HAMA algorithm designs the moving trajectory of the AUV into a spiral shape, and the nodes close to the moving trajectory are cluster head nodes. The cluster head node can directly send data to the AUV, and other nodes forward the data to the AUV through multi-hop transmission.

[0112] The network performance compared in the present invention mainly includes network life, average delay in data collection, data packet collection rate, VoI of collected data and unit energy consumption. Network life is defined as the number of rounds run when the energy of the first sensor node in the network is exhausted. The average delay in data collection is defined as the average time taken by the AUV to complete data collection and return to the surface aggregation node from the surface aggregation node when performing each round of data collection tasks. The data packet collection rate (PCR) is defined as the ratio of the data packets successfully collected by the AUV to the data packets sent by all nodes. The VoI of collected data is defined as the total data VoI finally collected by the surface aggregation node during the operation of the network when the VoI decays over time. The unit energy consumption is defined as the ratio of the total energy consumed by the network to the total data VoI finally collected by the aggregation node.

[0113] Figure 5 The data collection latency for different numbers of nodes is compared. Figure 5 It can be seen that the data collection delay of the LP-DDCA algorithm, the AEEDCO algorithm and the DCADTV algorithm proposed in the present invention increases with the increase in the number of nodes. As the number of nodes increases, the data collection delay of the AUV accessing all nodes in the LP-DDCA algorithm becomes larger, and is greater than the data collection delay of the AEEDCO algorithm and the DCADTV algorithm. Compared with the DCADTV algorithm, the AEEDCO algorithm clusters the network nodes, and the AUV only collects the data of the cluster head nodes, ignoring the data collection of isolated mobile nodes in the network. Therefore, the data collection delay of the AEEDCO algorithm is smaller than that of the DCADTV algorithm of the present invention. In the HAMA algorithm, the AUV periodically dives underwater to collect node data and returns to the surface at a fixed time, so the data collection delay is fixed. When the number of sensor nodes is 100, compared with the LP-DDCA algorithm and the HAMA algorithm, the data collection delay of the present invention (DCADTV algorithm) is reduced by 34.1% and 21.7%, respectively.

[0114] Figure 6 Indicates the changing trend of network lifetime with the number of nodes. Figure 6 It can be seen that the network life based on the AEEDCO algorithm, the HAMA algorithm and the DCADTV algorithm of the present invention decreases as the number of nodes increases. As the number of nodes increases, the nodes in the network are more densely distributed, the cluster head node collects more data packets from member nodes, and the energy consumption is faster. Compared with the AEEDCO algorithm, the AUV in the present invention also needs to collect data from isolated nodes, that is, there are more clusters. Although the data collection delay is increased, the network life is longer than the AEEDCO algorithm. The denser the node distribution in the HAMA algorithm, the more energy the forwarding node consumes, and the network life is also reduced. Since the AUV in the LP-DDCA algorithm accesses all nodes to collect data, that is, the energy consumed by each round of nodes is the same, the network life has nothing to do with the number of nodes, but only with the initial energy and the size of the data packet.

[0115] Figure 7Indicates the changing trend of data packet collection rate with the number of nodes. Since the AUV in the LP-DDCA algorithm accesses all nodes to collect data, the average PCR of each round can reach 100%. In the present invention, the AUV not only accesses the dynamic cluster head node to collect data, but also accesses the isolated nodes or sub-network nodes that store data formed by the movement of the nodes, so the average PCR of each round can also reach 100%. In the AEEDCO algorithm, the AUV only accesses the cluster head node to collect data, ignoring the data of the isolated nodes or sub-network data formed by the movement. In addition, due to the mobility of the nodes and the delay of data transmission, when the node forwards data to the neighboring node using the dynamic path, there is a situation where the data cannot be forwarded successfully, so the PCR is lower than 100%. As the number of nodes increases, the number of nodes that can still communicate after the node moves will also increase, which makes the PCR larger as the number of nodes increases. In the HAMA algorithm, nodes forward data to nodes within the AUV's moving trajectory. However, due to the mobility of nodes and the delay of data transmission, data cannot be fully transmitted when forwarding data between nodes using dynamic paths. Moreover, when the AUV moves along the moving trajectory, the nodes originally located in the trajectory have moved to other locations and cannot be successfully collected by the AUV, so their PCR is low.

[0116] Figure 8 It is a curve showing the change of VoI of the data finally collected by the surface aggregation node with the number of sensor nodes. During the operation of the network, since the VoI of the data collected by the AUV still decays with time, the VoI of the data collected by the surface aggregation node decreases with the increase of the data collection delay. The data collection delay of the present invention is relatively small and the network life is relatively long. In addition, the AUV also collects data from all dynamic cluster head nodes, which leads to an increase in the VoI of the data finally collected by the aggregation node during the operation of the network. In the LP-DDCA algorithm, the AUV accesses all mobile sensor nodes and collects sensor node data. As the number of sensor nodes increases, the data collection delay increases, and the VoI collected by the aggregation node decreases. During the operation of the network, the VoI of the data collected by the aggregation node based on the LP-DDCA algorithm is smaller than the VoI of the data collected based on the present invention. From Figure 8 It can be seen that the more underwater sensor nodes there are, the greater the difference between the VoI of the data collected by the LP-DDCA algorithm and the present invention. Since the AEEDCO algorithm and the HAMA algorithm cannot normally collect data from isolated nodes, the VoI of the data finally collected by the surface aggregation node is significantly smaller than the VoI of the data collected by the present invention.

[0117] Fig. 9 is a histogram of unit energy consumption changing with the number of nodes. Fig. 9It can be seen from the figure that as the number of sensor nodes increases, the energy consumed by collecting unit VoI gradually decreases. Because in the LP-DDCA algorithm, the AUV visits each node and collects its data, the data collection delay is large, resulting in a decrease in the VoI of the data collected by the surface aggregation node, so the unit energy consumption of the LP-DDCA algorithm is greater than that of the present invention. Figure 7 It can be seen that the data packet collection rate of the present invention is much greater than that of the AEEDCO algorithm, that is, the VoI of the data collected in each round is much greater than that of the AEEDCO algorithm, so the unit energy consumption of the present invention is lower than that of the AEEDCO algorithm.

[0118] Fig.10 represents the variation of data collection delay with AUV moving speed. Fig.10 It can be seen that the data collection delay of the LP-DDCA algorithm, the AEEDCO algorithm and the present invention decreases as the AUV's moving speed increases. In the LP-DDCA algorithm, the AUV needs to access all nodes to collect data, so the data collection delay of the LP-DDCA algorithm is significantly greater than that of the AEEDCO algorithm and the algorithm proposed in the present invention. In the AEEDCO algorithm, the network nodes are divided into several clusters. Due to the mobility of the nodes, there may be isolated nodes or disconnected sub-networks in the network, and the AUV only collects data from the cluster head nodes. The present invention also needs to collect data from isolated nodes or sub-networks formed by movement, so its collection delay is greater than that of AEEDCO when the AUV's moving speed is relatively low. When the AUV's moving speed increases, the time it takes for the AUV to catch up with the mobile node to collect data becomes shorter, and the difference in data collection delays between the two algorithms also becomes smaller. In the HAMA algorithm, the AUV periodically dives underwater to collect node data and returns to the surface at a fixed time, so the data collection delay is fixed.

[0119] Fig.11 is the curve of data collection delay changing with node communication radius. Fig.11It can be seen that the data collection delay of the AEEDCO algorithm and the algorithm of the present invention decreases with the increase of the communication radius. As the communication radius increases, the number of nodes in the network cluster in the AEEDCO algorithm increases, the range of the cluster increases, and there is a closer AUV path, so the data collection delay will decrease. When the communication radius of the node is small (Rc = 150m), the number of dynamic clusters in the present invention may be more than the number of fixed clusters in the AEEDCO algorithm, and the number of isolated nodes or subnetworks formed by node movement increases. The delay for AUV to access nodes with data and collect data will be greater than that of the AEEDCO algorithm. As the communication radius of the node increases, the number of dynamic clusters gradually decreases, and the delay for AUV to collect data also decreases. When the communication radius Rc = 400m, all nodes in the network are located in the same cluster, so there is only one cluster head node. The AUV only needs to access this one cluster head node to collect data from all nodes, and the delay in data collection is lower than that of the AEEDCO algorithm. After that, the data collection delay will not change significantly when the communication radius is increased, but it is still lower than that of the AEEDCO algorithm. Since the AUV in the LP-DDCA algorithm has to visit all sensor nodes and move to a fixed reliable distance to collect data, its collection delay remains unchanged regardless of how the communication radius changes. In the HAMA algorithm, the AUV periodically dives underwater to collect node data and returns to the surface aggregation node at a fixed time, so the data collection delay is fixed.

[0120] Fig.12 Represents the effect of water velocity on data collection delay. Fig.12 (a) shows the effect of water velocity and AUV moving speed on data collection delay, where the x-axis represents the AUV moving speed v auv , the y-axis represents the water velocity u, and the z-axis represents the data collection delay. In the HAMA algorithm, the AUV periodically dives underwater to collect node data and returns to the surface at a fixed time. Therefore, this algorithm is ignored in subsequent performance comparisons. Fig.12 As shown in (a), when the water flow speed is slow, the nodes move slightly, and the data collection delay decreases as the AUV moves faster. When the AUV moves fast enough, the data collection delay of the present invention is close to that of the AEEDCO algorithm. When the AUV moves slowly, even if the nodes move slowly, the AUV still needs more time to complete data collection, and the delay is longer. Since the AUV needs to access all nodes to collect data in the LP-DDCA algorithm, its collection delay is higher than other algorithms. Fig.12 (b) shows the influence of water flow speed and node communication radius on data collection delay, where the x-axis represents the communication radius Rc, the y-axis represents the water flow speed u, and the z-axis represents the data collection delay. Fig.11It can be seen that the node communication radius has little effect on the LP-DDCA algorithm and the HAMA algorithm. Therefore, the two algorithms are no longer compared in the analysis of the impact of the communication radius and water flow speed on the data collection delay. Fig.12 As shown in (b), when the water flow speed remains unchanged, as the node communication radius increases, the number of dynamic clusters in the present invention gradually decreases, and the number of cluster heads accessed by the AUV also gradually decreases. Compared with the AEEDCO algorithm, in which the AUV needs to access a fixed number of clusters, the data collection delay of the present invention is gradually lower than that of the AEEDCO algorithm. When the node communication radius remains unchanged, as the water flow speed increases, the speed at which the node moves with the water flow becomes faster, the range of movement also becomes larger, the time it takes for the AUV to access the mobile node to collect data becomes longer, and the collection delay increases.

[0121] In a mobile underwater sensor network, the DCADTV algorithm of the present invention can effectively collect data from mobile nodes, shorten data collection delay, effectively guarantee the data packet collection rate by calculating the data packet transmission probability based on transmission delay, and increase the VoI of the data finally collected by the aggregation node. Compared with the AEEDCO algorithm, the DCADTV algorithm of the present invention improves the data packet collection rate and network life, and increases the VoI of the data collected by the aggregation node. Compared with the LP-DDCA algorithm, the DCADTV algorithm of the present invention effectively reduces the data collection delay and increases the VoI of the data collected by the aggregation node.

[0122] In order to solve the problems of high network energy consumption and long data collection delay caused by VoI attenuation and dynamic changes in network topology in dynamic underwater network scenarios, the present invention proposes an underwater data collection algorithm DCADTV based on dynamic network topology and information value constraints. The dynamic network topology is formed by dynamic clustering of mobile nodes, the data transmission probability of mobile nodes based on transmission delay is calculated to select the next hop forwarding node, and AUV is used as an edge device to collect data of dynamic cluster head nodes, ensuring that the VoI of the collected data is increased while collecting all mobile node data, reducing the energy consumption of cluster head nodes that consume too much energy, and reducing data collection delay.

[0123] Through simulation experiments and comparison with dynamic data collection algorithms LP-DDCA algorithm, AEEDCO algorithm, and HAMA algorithm, it is concluded that the present invention has higher data collection efficiency and data VoI, and significantly reduces network energy consumption and data collection delay.

[0124] The underwater data collection algorithm DCADTV based on dynamic network topology and information value constraints provided by the present invention combines the advantages of multi-hop transmission and AUV-assisted data collection algorithms, can effectively collect data from mobile sensor nodes, reduce network energy consumption, improve data collection efficiency, and ensure data collection integrity.

[0125] An embodiment of the present invention provides an underwater data collection system, comprising:

[0126] The data acquisition module is used to use multiple underwater sensors in the mobile process as network nodes to build a network topology structure, obtain the relevant data of each network node during communication, and the data information value VoI used to represent the value of data collected by the underwater sensor when each network node moves with the water flow.

[0127] The cluster head node selection module is used to dynamically cluster the network topology structure: in the network topology structure, multiple network nodes that can communicate with each other are regarded as a cluster, so that the network topology structure is divided into multiple clusters; in each cluster, according to the residual energy, data packet size and VoI of each network node during communication, the cluster head node selection probability model is used to obtain the probability of each network node being selected as the cluster head node, and the network node corresponding to the maximum probability is selected from all probabilities as the cluster head node.

[0128] The data transmission module is used to transmit the data of each network node to the cluster head node in each cluster after clustering to realize data transmission: according to the historical coordinates of each network node, the future coordinates of each network node are predicted by a multi-layer water flow model that simulates water flow movement; when each network node communicates with another network node, the transmission delay model is used to obtain the transmission delay of the data packet according to the channel capacity and data packet size of the network node during communication, and the distance between the coordinates of the two communicating network nodes; according to the transmission delay, the transmission probability model is used to obtain the data packet transmission probability between the two communicating network nodes; within the transmission delay, when the transmission probability is greater than the set threshold and the VoI is greater than zero, it is determined that the data packet is successfully transmitted; each network node can successfully transmit the data packet to multiple network nodes, and the multiple network nodes are used as a candidate forwarding node set; when each network node transmits data, a candidate forwarding node that meets the conditions is selected from the candidate forwarding node set, and the data is transmitted through a multi-hop transmission method until the cluster head node receives the transmission data of all network nodes.

[0129] The data collection module is used to collect data from the cluster head nodes of each cluster using an autonomous underwater vehicle (AUV): based on the historical coordinates of each cluster head node, the future coordinates of each cluster head node are predicted through a multi-layer water flow model; the AUV is used to regularly navigate to the predicted future coordinates of each cluster head node for data collection, and the collection path with the shortest navigation time is selected during collection.

[0130] A specific embodiment is as follows:

[0131] like Figure 1 As shown, the present invention (DCADTV) discusses the data collection problem in an AUV-assisted multi-layer network architecture described by a three-dimensional coordinate system.

[0132] The network deploys 1 surface aggregation node, 1 AUV and N v Mobile underwater sensor nodes. The node set is recorded as Sensor Node v i The coordinates of The initial positions of all sensor nodes are known, and their initial energy and communication radius are the same, denoted as E0 and R respectively. c As an edge device, AUV is used to access and collect data from cluster head nodes. It is stipulated that AUV starts from the surface aggregation node, and returns to the surface aggregation node to unload the collected data after visiting the cluster head node. The moving speed of AUV is v auv There are no obstacles in the underwater monitoring area that hinder the movement of the AUV, and the AUV can stay at any position in the underwater area. It is assumed that the VoI of the data collected by the AUV still decays over time.

[0133] For different underwater applications, the importance and time sensitivity of data are different, and the attenuation of its VoI is also different. In most cases, the VoI is the largest when the information is sensed by the sensor node, and then the VoI decreases over time until the data information is successfully transmitted to the surface sink node. If the data information is not transmitted to the sink node within the predefined deadline, then the data information loses its value and meaning of being collected, and the VoI of the data is assigned to 0. In other words, the later the data is transmitted to the surface sink node, the smaller the VoI of the data collected by the sink node.

[0134] In a dynamic underwater environment, nodes move with the water flow, resulting in dynamic changes in the network topology. It is difficult for the network to form a stable cluster. At the same time, the selected candidate cluster head nodes will also change frequently with the dynamic changes in the topology. The present invention divides the data collection algorithm based on dynamic network topology and VoI constraints into three stages, namely, dynamic clustering stage, data transmission stage and data collection stage. Figure 2 shown.

[0135] Combination Figure 3 , the node information includes the node position coordinates and VoI. The present invention uses a multi-layer water flow model that can simulate deep-sea currents to represent the water flow movement in the underwater monitoring area. In the multi-layer water flow model, the hth water flow layer is modeled as a water flow velocity u h and the angle deviation from the x-axis α h A piecewise function of. At time t, the sensor node v in the hth water flow layer i The position coordinates can be expressed by the formula

[0136]

[0137] Among them, (x vi (0),yvi (0),z vi (0)) represents the sensor node v i The coordinate at the initial time t = 0, ξ is the inertial parameter of the system, which is related to the physical parameters such as density and volume of the sensor node. This invention mainly considers the horizontal flow movement and ignores the vertical movement of the water flow. Therefore, z vi (t) = z vi (0). Sensor node v i The data VoII at time t i (t) can be calculated by the formula

[0138] I i (t) = βF i +(1-β)f i (t-t0),t≥t0

[0139] Among them, F i ∈R+ is the importance of data information related to the monitoring requirements of the sensor node, f i (t-t0) is used to reflect time sensitivity and is defined in the form of e -ε (t-t0), ε∈[0,1] is its attenuation coefficient. When ε=0, function f i (t-t0) does not decay. In addition, β∈[0,1] is a weight coefficient that balances importance and time sensitivity. At the initial time t=t0, the VoI of the sensor node sensing data is the largest, and then the VoI decays with time. Each sensor node has a predefined deadline, and data that exceeds the deadline will lose the meaning and value of collection, that is, its VoI value is 0.

[0140] Combination Figure 2 and Figure 3 ,The present invention is divided into three stages, namely the dynamic clustering stage, the data transmission stage and the data collection stage.

[0141] 1. Dynamic clustering stage

[0142] The dynamic changes of network topology make it difficult for the network to form a stable cluster, and the movement of nodes affects the transmission probability of data packets and the success rate of AUV collecting node data.

[0143] The present invention uses a multi-layer water flow model to simulate the movement trajectory of sensor nodes, obtains the network topology through two-way information exchange technology during the node movement, and dynamically clusters the network. The process is as follows: first, the surface aggregation node sends a beacon data packet to the network node, and after receiving the beacon data packet, the node broadcasts it to all neighboring nodes within its communication range. Nodes that can communicate with each other form a cluster, thereby obtaining the network topology and number of clusters of the cluster where the node is located. Then, a cluster head node is selected based on the network structure of the cluster, the remaining energy of the node, the size of the data packet that the node can transmit, and VoI. Node v i The probability of being selected as the cluster head node at time t is P CH (i,t):

[0144]

[0145] where Eres(i,t) is the node v i The residual energy at time t, E0 is the initial energy of the node, C z (i) is the closeness centrality of the node, b tran (i,t) is the node v i The size of the data packet that can be transmitted with VoI not equal to 0, including node v i The generated data packets also include the data packets received from other nodes whose VoI is not equal to 0, and the system parameter is 0<γ<1. The node with the largest probability value will be selected as the cluster head node. The mobility of nodes causes the network clustering and the selection of cluster head nodes to change dynamically.

[0146] 2. Data transmission stage

[0147] like Figure 4 The flow chart is shown in Figure 2. Sensor node v i During the movement, the topological structure of the cluster at the current moment, node location coordinates, remaining energy, data packet size, VoI, and neighbor node information are determined based on the multi-layer water flow model and two-way information exchange technology. There is a transmission delay when the mobile sensor node transmits data to the neighbor node, and the calculation formula is:

[0148]

[0149] Among them, c b is the channel capacity, v uw represents the transmission speed of underwater acoustic signals, d(v i ,v j ,t) represents the node v at time t i and v j The distance, and d(v i ,v j ,t)≤Rc, Rc is the communication radius of the sensor node. tran(i,t) is the node v i The size of the data packet that can be transmitted with VoI not equal to 0, including node v i The generated data packets also include data packets received from other nodes whose VoI is not equal to 0.

[0150] At node v i To node v j When transmitting a data packet, if the transmission probability of the data packet within the transmission delay is greater than the preset threshold p0, it can be considered that the data packet is successfully transmitted to the node v j . At time t, node v i To node v j The transmission probability of transmitting a bbit data packet can be expressed as

[0151] P c (i,j,t)=(1-P e (i,j,t)) b (3)

[0153] Among them, P e (i,j,t) indicates the distance between sensor nodes at time t is d(v i ,v j ,t), the data packet transmission bit error rate can be calculated using the following formula:

[0154]

[0155] Where Γ(i,j,t) represents the node v at time t i To node v j The signal-to-noise ratio of the data packet sent. i The transmission power and communication frequency are p t When and f, the signal-to-noise ratio Γ(i,j,t) can be expressed as

[0156]

[0157] Among them, B w represents the noise bandwidth of the receiving end, N(f) is the underwater environmental noise, which usually includes turbulence noise Nt(f), ship noise Ns(f), wind and wave noise Nw(f) and thermal noise Nth(f). i ,v j ,t),f) represents the sensor node v at time t i The frequency f is used to distance d(v i ,v j ,t) of sensor node v j Path loss for transmitting data packets.

[0158] When the mobile sensor node vi ∈V to neighboring sensor node v j When transmitting data (d(v i ,v j ,t)≤Rc), first calculate whether the packet transmission probability Pc(i,j,t) at the current time t is greater than the threshold p0. Sensor node v i Predict the neighboring mobile node v in the subsequent time based on the recorded historical location information of the neighbor node and the received historical location information of the neighbor node j The position and calculation of the transmission delay period If the packet transmission probability within the transmission delay period is greater than the predefined threshold p0 and VoI is greater than 0, then the mobile sensor node v j Can be used as mobile sensor node i The candidate forwarding node of the next hop node. The candidate forwarding node can be calculated by the following formula

[0159]

[0160]

[0161] Among them, the sensor node v j is the sensor node v at time t i Neighbor nodes of i (t+Δt) is the sensor node v i The VoI of the packet at time t+Δt. All candidate forwarding nodes at time t The sensor node v i The set of candidate forwarding nodes

[0162] At time t, when the mobile sensor node v i When transmitting data to neighboring nodes, you can select from the candidate node set Select the next hop forwarding node Its remaining energy must be sufficient to forward node v i data, and compared to node v i , sensor node v k Closer to the cluster head node set Q. At time t, node v k The probability of being selected as a forwarding node can be expressed as

[0163]

[0164] Among them, 0<λ1<1, is the candidate forwarding node v at time t k Than the current sensor node v iThe 1-row M-column matrix closer to the cluster head node set Q, that is, the current node v i and candidate forwarding node v k The distance difference to all cluster head nodes is the same as the distance to node v i The matrix of the ratio of the distance to the cluster head node, Represents the node v at time t i The distance matrix to the cluster head node Q. Similarly, represents the candidate node v at time t k The matrix of Q distances to all cluster head nodes.

[0165] If at time t the node v k is selected as a forwarding node, then its remaining energy is not less than that of the receiving node v i The energy consumed by the data packet and the transmission node v i Data packets and nodes v k The sum of the energy consumed by the data packets themselves. That is, the candidate forwarding node v k The remaining energy must satisfy the following formula.

[0166]

[0167] Among them, p t represents the transmission power of the sensor node, p r represents the received power, u t Indicates the data transmission rate, u r represents the data receiving rate, A(Rc,f) is the node v k The path loss of sending data packets to neighboring nodes within the communication radius Rc using the signal frequency f. Select the node v with the largest probability that the remaining energy satisfies the energy formula. k As node v i The forwarding node at time t.

[0168] 3. Data Collection Phase

[0169] The AUV dives underwater regularly to collect data from dynamic cluster head nodes. Before starting data collection, the AUV sends a beacon data packet to the underwater node. All nodes storing data are recorded as cluster head nodes, and the cluster head node ID, location coordinates, data packet size, VoI and other information are recorded. After receiving the control packet, the cluster head node returns a response packet including cluster head node information and neighbor table to the AUV. The AUV determines which cluster head node data to collect first based on the received response packet. First, the trajectory of the cluster head node during movement and the location coordinates at future times are calculated based on the historical information of the cluster head node. Then, the state of the AUV at different times is determined according to formula (10), and the time when the AUV can meet the dynamic cluster head node is calculated. The cluster head node with the shortest time is selected as the target node to be visited first. The AUV moves directly to the predicted position of the cluster head node to collect cluster head node data. According to the collected neighbor table information in the cluster head node, the relevant information of the cluster head node recorded in the AUV record table is updated, and then the location and movement trajectory of the next cluster head node to be visited closest to the AUV are predicted.

[0170]

[0171] Where (x a (t),y a (t),z a (t)) is the coordinate of the AUV at time t, φ a (t) is the heading angle of the AUV, φ' a (t) is the orientation of the AUV in the xy plane.

[0172] The AUV dives underwater at specific intervals to access and collect data from the mobile cluster head node, and then returns to the surface aggregation node. Subsequent sensed data will be collected when the AUV dives underwater again.

[0173] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for collecting underwater data, characterized in that: The following steps are involved: The multiple underwater sensors in the moving process are used as network nodes to construct the network topology structure, and the relevant data of each network node during communication is obtained, as well as the data information value VoI used to represent the value of the data collected by the underwater sensor when each network node moves with the water flow; Dynamically cluster the network topology: In the network topology, multiple network nodes that can communicate with each other are regarded as a cluster, so that the network topology is divided into multiple clusters; in each cluster, according to the residual energy, data packet size and VoI of each network node during communication, the probability of each network node being selected as the cluster head node is obtained using the cluster head node selection probability model, and the network node corresponding to the maximum probability is selected from all the probabilities as the cluster head node; In each cluster after clustering, the data of each network node is transmitted to the cluster head node to realize data transmission: according to the historical coordinates of each network node, the future coordinates of each network node are predicted by a multi-layer water flow model that simulates the movement of water flow; when each network node communicates with another network node, the transmission delay model is used to obtain the transmission delay of the data packet according to the channel capacity and data packet size of the network node during communication, as well as the distance between the coordinates of the two communicating network nodes; According to the transmission delay, the transmission probability model is used to obtain the data packet transmission probability between two communicating network nodes; within the transmission delay, when the transmission probability is greater than the set threshold and the VoI is greater than zero, the data packet is determined to be successfully transmitted; each network node can successfully transmit the data packet to multiple network nodes, and multiple network nodes are used as candidate forwarding node sets; when each network node transmits data, it selects a candidate forwarding node that meets the conditions from the candidate forwarding node set, and transmits it through a multi-hop transmission method until the cluster head node receives the transmission data of all network nodes; An autonomous underwater vehicle (AUV) is used to collect data from the cluster head nodes of each cluster. The future coordinates of each cluster head node are predicted through a multi-layer water flow model based on the historical coordinates of each cluster head node. The AUV is used to regularly navigate to the predicted future coordinates of each cluster head node for data collection, and the collection path with the shortest navigation time is selected during collection.

2. The underwater data collection method according to claim 1, characterized in that: The multi-layer water flow model simulating water flow movement is used to predict the future coordinates of each network node, and the specific steps include: Set the underwater monitoring area as a cuboid and establish a coordinate system for water flow movement; The multi-layer water flow model is formulated as follows: Among them, v i represents the network node, t represents the time, u h represents the water velocity of the hth water layer, α h It represents the angular deviation of the underwater sensor on the x-axis when it moves with the water flow. (x vi (0),y vi (0),z vi (0)) represents the coordinates of the network node at the initial time t = 0, ξ is the inertia parameter of the system, (x vi (t),y vi (t),z vi (t)) represents the coordinates of the network node at time t.

3. The underwater data collection method according to claim 1, characterized in that: The specific steps of determining that the data packet is successfully transmitted include: According to the channel capacity of the network node during communication, the size of the data packet and the predicted future coordinates of each network node, the transmission delay existing when two network nodes communicate is obtained through the transmission delay model; The transmission delay model is formulated as follows: Among them, D c (i,j,t) represents the network node v at time t i With network node v j The transmission delay between b is the channel capacity, v uw represents the transmission speed of underwater acoustic signals, d(v i ,v j ,t) represents the network node v at time t i and are working with v i Network nodes for communication j The distance between them, and d(v i ,v j ,t)≤Rc, Rc is the communication radius of the network node. tran (i,t) is the node v i The data packet size that can be transmitted has a value not equal to 0; When each network node transmits data to another network node, within the transmission delay, the transmission probability model is used to obtain the transmission probability; The transmission probability model is formulated as follows: P c (i,j,t)=(1-P e (i,j,t)) b Among them, P c (i, j, t) represents the transmission probability, b represents the data packet transmitted by the node is b bits, P e (i,j,t) indicates that the distance between network nodes at time t is d(v i ,v j ,t) is the packet transmission bit error rate; Γ(i,j,t) is the error rate of network node v at time t. i To network node v j The signal-to-noise ratio of the sent data packet; node v i The transmission power and communication frequency are p t and f; Bw represents the noise bandwidth of the receiving end, N(f) is the underwater environmental noise; A(d(v i ,v j ,t),f) represents the sensor node v at time t i The frequency f is used to distance d(v i ,v j ,t) of the network node v j Path loss for transmitting packets; When the transmission probability is greater than the threshold and the data information value is greater than zero, it is determined that the transmission conditions are met, and the network node v i Able to send data to network node v j Data transferred successfully.

4. The underwater data collection method according to claim 1, characterized in that: When each network node transmits data, it selects a candidate forwarding node that meets the conditions from the candidate forwarding node set. The specific steps include: Select a candidate forwarding node v from the candidate forwarding node set k ; At time t, node v k The probability of being selected as a candidate forwarding node is: Among them, E res (k,t) is the network node v k The remaining energy at time t, k is the serial number of the network node, E0 is the network node v k The initial energy, λ1 is the system parameter, is the candidate forwarding node v at time t k Network node v i The matrix closer to the cluster head node set Q; in, represents the network node v at time t i The distance matrix to the cluster head node set Q, represents the candidate forwarding node v at time t k The matrix of distances to the cluster head node set Q; And, the candidate forwarding node v k The remaining energy of the receiving node v i The energy consumed by the data packet and the transmission node v i Data packets and nodes v k The sum of the energy consumed by the data packets themselves; The residual energy is expressed as follows: Among them, p t represents the transmission power of the network node, p r represents the received power, u t Indicates the data transmission rate, u r represents the data receiving rate, A(Rc,f) is the candidate forwarding node v k The path loss of sending data packets to network nodes within the communication radius Rc using signal frequency f.

5. The underwater data collection method according to claim 1, characterized in that: The specific steps of selecting the collection path with the shortest navigation time during collection include: The cluster head node sends a response packet containing cluster head node information to the AUV; According to the cluster head node information in the response packet, the future coordinates of each cluster head node are predicted through the multi-layer water flow model; According to the historical navigation state of the AUV, a state model capable of determining the future navigation state of the AUV is used to obtain data related to the future navigation state of the AUV; wherein the navigation state includes a navigation position and a navigation attitude; The state model is formulated as follows: Among them, (x a (t),y a (t),z a (t)) is the coordinate of the AUV at time t, φa(t) is the heading angle of the AUV, φ'a(t) is the direction of the AUV on the xy plane, V auv Indicates the moving speed of the AUV; When predicting the future coordinates of each cluster head node, the value of data information must be greater than zero; According to the future navigation state of the AUV and the predicted future coordinates of each cluster head node, the encounter time between the AUV and the predicted future coordinates of each cluster head node is determined; The shortest encounter time is selected from the encounter times, and the cluster head node corresponding to the shortest encounter time is the target node that the AUV visits first; The AUV moves to the predicted future coordinates of the target node and collects data from all cluster head nodes and data from each underwater sensor received by each cluster head node.

6. An underwater data collection system, characterized in that: include: The data acquisition module is used to construct a network topology structure by taking multiple underwater sensors in the moving process as network nodes, and to obtain the relevant data of each network node during communication, as well as the data information value VoI used to represent the value of the data collected by the underwater sensor when each network node moves with the water flow; The cluster head node selection module is used to dynamically cluster the network topology structure: in the network topology structure, multiple network nodes that can communicate with each other are regarded as a cluster, so that the network topology structure is divided into multiple clusters; in each cluster, according to the residual energy, data packet size and VoI of each network node during communication, the probability of each network node being selected as the cluster head node is obtained using the cluster head node selection probability model, and the network node corresponding to the maximum probability is selected from all probabilities as the cluster head node; The data transmission module is used to transmit the data of each network node to the cluster head node in each cluster after clustering to realize data transmission: according to the historical coordinates of each network node, the future coordinates of each network node are predicted by a multi-layer water flow model that simulates the movement of water flow; when each network node communicates with another network node, the transmission delay model is used to obtain the transmission delay of the data packet according to the channel capacity and data packet size of the network node during communication, as well as the distance between the coordinates of the two communicating network nodes; According to the transmission delay, the transmission probability model is used to obtain the data packet transmission probability between two communicating network nodes; within the transmission delay, when the transmission probability is greater than the set threshold and the VoI is greater than zero, the data packet is determined to be successfully transmitted; each network node can successfully transmit the data packet to multiple network nodes, and multiple network nodes are used as candidate forwarding node sets; when each network node transmits data, it selects a candidate forwarding node that meets the conditions from the candidate forwarding node set, and transmits it through a multi-hop transmission method until the cluster head node receives the transmission data of all network nodes; The data collection module is used to collect data from the cluster head nodes of each cluster using an autonomous underwater vehicle (AUV): based on the historical coordinates of each cluster head node, the future coordinates of each cluster head node are predicted through a multi-layer water flow model; the AUV is used to regularly navigate to the predicted future coordinates of each cluster head node for data collection, and the collection path with the shortest navigation time is selected during collection.

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