A multi-factor dynamic clustering method for unmanned aerial vehicle networking

Abstract

The application relates to a multi-factor dynamic clustering method for unmanned aerial vehicle networking, and belongs to the technical field of unmanned aerial vehicle networking clustering. The method introduces a plurality of factors such as a safety distance factor and a transmission performance factor between unmanned aerial vehicle nodes to calculate a clustering election index, avoids the risk of excessively dense distribution of nodes around a cluster head of unmanned aerial vehicle networking, makes the cluster head node be in a low-risk state of a safety distance as much as possible, and elects the cluster head according to the transmission performance of the node. Meanwhile, based on the clustering election index, an efficient method for clustering and cluster maintenance is designed. In addition, in the preferred embodiment of the application, the weight coefficient can be dynamically adjusted based on the link health degree between the member nodes in the cluster maintained by the cluster head node, so as to realize the optimization and update of the cluster structure. The application effectively improves the rationality of the clustering structure and the business transmission performance, and realizes an efficient and reliable cluster maintenance mechanism.

Description

Technical Field

[0001] This invention relates to a multi-factor dynamic clustering method for UAV networking that integrates security distance and transmission performance, belonging to the field of UAV networking clustering technology. Background Technology

[0002] Unmanned aerial vehicle (UAV) networks are temporary networks autonomously formed by multiple UAVs through wireless communication, and they have broad application prospects in fields such as low-altitude safety, disaster relief, environmental monitoring, and logistics delivery. As the scale of UAV swarms expands and the complexity of tasks increases, constructing an efficient and stable network topology has become a key technological challenge. In this process, clustering technology, by dividing the network into multiple clusters and electing cluster heads to manage intra-cluster operations and inter-cluster communication, can effectively reduce network control overhead, improve routing efficiency, and enhance network scalability. Therefore, clustering technology is one of the core methods for building large-scale UAV networks.

[0003] Existing UAV networking clustering algorithms can be broadly categorized into several types based on their election strategies, including identifier-based clustering, connectivity-based clustering, and weight-based clustering. Among these, weighted clustering algorithms, which comprehensively consider multiple factors such as node degree, mobility, and energy to calculate weight values ​​for electing cluster heads, are widely used. However, existing weighted clustering algorithms still have significant drawbacks: (1) In terms of weight parameters, existing methods usually focus on the relative distance between UAVs, but fail to effectively avoid the risk of collision or signal interference caused by excessively close physical distances, and lack protection methods for nodes within the cluster, especially the cluster head node; at the same time, few algorithms incorporate the service transmission characteristics (such as service volume and latency requirements) between UAVs into the weight calculation, which cannot effectively ensure the service quality of service transmission between the cluster head node and each node within the cluster.

[0004] (2) During the cluster maintenance phase, there is a lack of efficient cluster head switching mechanism, making it difficult to achieve smooth and rapid handover of cluster heads when the topology changes rapidly, which can easily lead to communication interruption.

[0005] (3) The weight coefficients in the existing algorithms are mostly fixed values, which cannot be dynamically adjusted according to changes in network status, business load, etc., resulting in poor adaptability of the algorithm environment and difficulty in maintaining optimal performance in complex real-world application scenarios.

[0006] In summary, existing weighted clustering methods have certain shortcomings in terms of cluster head protection, cluster head switching agility, and the adaptability of weight coefficients. It is urgent to study ways to improve these three deficiencies in weighted clustering algorithms. Summary of the Invention

[0007] The technical problem solved by this invention is to address the aforementioned shortcomings of weighted clustering algorithms in UAV networking. It proposes a multi-factor dynamic clustering method for UAV networking, introducing multiple factors such as the safety distance factor and transmission performance factor between UAV nodes to calculate cluster election indicators. This avoids the risk of excessively dense node distribution around the cluster head in UAV networking, ensuring that the cluster head node is in a low-risk state with a safe distance. Furthermore, cluster heads are elected based on node transmission performance, and clustering and cluster maintenance are achieved based on the cluster election indicators. This effectively improves the rationality of the cluster structure and service transmission performance, realizing an efficient and reliable cluster maintenance mechanism.

[0008] The present invention adopts the following technical solution: A multi-factor dynamic clustering method for UAV networking includes: S1. Within the network, UAV nodes periodically broadcast their own HELLO detection packets and receive HELLO detection packets broadcast by other UAV nodes. The HELLO detection packets include location coordinates, speed information, service transmission information, cluster control information, UAV node status, receiving identifier address, and sending identifier address. S2. Based on the HELLO detection packet, calculate the safe distance factor, speed similarity factor, energy factor and transmission performance factor of each UAV node, and calculate the cluster head election index of each UAV node based on the safe distance factor, speed similarity factor, energy factor and transmission performance factor. S3. Sort the cluster head election index values ​​of each UAV node, and elect a cluster head and a backup cluster head according to the sorting results to complete the cluster assembly. S4. Notify the surrounding drone nodes of the clustering results. Each drone node takes corresponding actions based on the notification results and its own status, including joining the cluster, confirming joining the cluster, not taking any action, or returning to step S3, and re-electing a cluster head and a backup cluster head. S5. Return to step S1 until all drone nodes have been traversed; S6. Complete the removal of nodes within the cluster, the addition of new nodes to the cluster, and / or the switching of the cluster head.

[0009] In the above-mentioned multi-factor dynamic clustering method for UAV networking, the service transmission information in step S1 includes the number of data packets successfully received by the current UAV from other UAVs within a single hop range, and the transmission performance of other UAV nodes is represented by the UAV node address and the number of data packets.

[0010] In the above-mentioned multi-factor dynamic clustering method for UAV networking, the safety distance factor in step S2 is calculated based on its own position coordinates and the coordinates of other UAV nodes; The speed similarity factor is calculated based on its own speed information and the speed information of other UAV nodes; The energy factor is calculated based on its own energy usage. The transmission performance factor is calculated based on the service transmission information from the HELLO packets of other drone nodes, matching the number of data packets successfully transmitted by the drone itself to the HELLO packet, and combining it with the total number of data packets sent by the drone to the HELLO packet as recorded by itself.

[0011] In the above-mentioned multi-factor dynamic clustering method for UAV networking, the safety distance factor The calculation formula is as follows:

[0012] in, The distance factor, which represents the normalized distance between drones, is expressed as:

[0013] The actual spatial distance between drone i and drone j is calculated using the following drone coordinate information:

[0014] The distance between two communicating drones i and j is calculated using the following formula:

[0015] This indicates the minimum safe distance between drone nodes. This indicates the communication range of the drone.

[0016] In the above-mentioned multi-factor dynamic clustering method for UAV networking, the speed similarity factor It is obtained through the following method: Taking UAV i as a reference, the velocity differences of UAV j relative to it on the x, y, z coordinate axes are as follows:

[0017]

[0018]

[0019] , These represent the angles between the velocity vector of UAV i and the x, y, and z coordinate axes, respectively.

[0020] The drone i is within communication range with The average velocity difference of adjacent UAV nodes on the x, y, z coordinate axes is as follows:

[0021]

[0022]

[0023] Then the drone node i and The average speed difference between adjacent drone nodes is as follows:

[0024] The variances of the velocity differences between node i and its neighboring UAV nodes along the x, y, and z axes are then calculated as follows:

[0025]

[0026]

[0027] The average of the variances of the velocity differences along the x, y, and z axes is obtained as follows:

[0028] Then the velocity similarity factor Represented as: .

[0029] In the above-mentioned multi-factor dynamic clustering method for UAV networking, the energy factor The calculation formula is as follows:

[0030] in, This indicates the initial energy of the drone. The total energy consumption of the drone is calculated using the following formula:

[0031] in, The energy consumed by the drone to transmit data The energy consumed by the drone to receive data The energy consumed by the drone to fly The energy consumed by the drone to perform other tasks.

[0032] In the above-mentioned multi-factor dynamic clustering method for UAV networking, the transmission performance factor The calculation is performed using the following formula:

[0033] in, This indicates the specific time period for observing data packet transmission. Indicates that drone node i is at time The total number of data packets transmitted to node j within its communication coverage area. Indicates time The number of data packets that meet the relevant business indicators among the data packets received by UAV node j from node i.

[0034] In the above-mentioned multi-factor dynamic clustering method for UAV networking, the cluster head election index of UAV nodes in step S2 is used as a basis. The calculation formula is as follows:

[0035] in, and This indicates the drone's serial number (ID) in the network. Indicates drone node Safety distance factor, Indicates drone node The velocity similarity factor with its neighboring UAV node j Indicates drone node energy factors, Indicates drone node The transmission performance factor of its neighboring nodes; , , , The weight coefficients of the corresponding clustering factors satisfy the following conditions: .

[0036] In the above-mentioned multi-factor dynamic clustering method for UAV networking, in step S3, each UAV node sorts the cluster head election index values ​​of other UAV nodes received through the HELLO detection packet, as well as its own cluster head election index value, and elects a cluster head and a backup cluster head according to the sorting result to complete the clustering formation, including: If the cluster head election index is used Sort and get itself If the value is the largest, then it will become the head of the primary cluster, and the subsequent clusters will be sorted accordingly. Each node serves as a member of its own cluster. The second largest cluster head is used as a backup cluster. The value satisfies the condition of being less than the upper limit. .

[0037] In the above-mentioned multi-factor dynamic clustering method for UAV networking, in step S4, the clustering results are notified to surrounding UAV nodes. Each UAV node takes corresponding actions based on the notification results and its own status, including joining a cluster, confirming joining a cluster, doing nothing, or returning to step S3, and re-electing a cluster head and a backup cluster head, including: When a drone node is in an undecided state and receives a HELLO probe packet message from a cluster head node, it will check if its own node address exists in the cluster node list. If its own node address exists, it will join the cluster of the cluster head node and reply to the cluster head node with a cluster entry confirmation message; if its own node address does not exist, it will ignore the cluster entry control information of the HELLO probe packet message. For other pending nodes waiting to join a cluster, upon receiving a cluster joining confirmation message from another node, the node in the newly created cluster will be removed from the cluster head election value index of all drone nodes stored in the node itself, and the weights of the remaining un-clustered nodes will be reordered. Then, the process returns to step S3.

[0038] In the above-mentioned multi-factor dynamic clustering method for UAV networking, step S6, which involves removing nodes within a cluster, includes: If the primary cluster head node does not receive a HELLO probe packet message from a node in the cluster within a certain period of time, the node is removed from the cluster list; for nodes in the cluster that actively leave the cluster, the cluster control information of the current cluster is directly removed when sending the HELLO probe packet message, so that the cluster head node can remove the node from the cluster. Adding a new node to a cluster includes: When a new node needs to join a cluster, it will obtain the status of surrounding clusters through the HELLO probe packet message of the nearest cluster head node, select a cluster that has not reached the maximum number of members in the cluster, and interact with the cluster head node of that cluster to confirm before joining the cluster.

[0039] In the above-mentioned multi-factor dynamic clustering method for UAV networking, step S6, completing the cluster head switching includes: When the primary cluster head runs out of energy or is damaged, the backup cluster head is replaced. The primary cluster head transmits its inter-cluster routing information to the backup cluster head and notifies the neighboring cluster head nodes. For distance vector routing protocols that only maintain the next-hop node, the neighboring cluster head nodes are notified to replace the routes related to their primary cluster head with the newly replaced backup cluster head, thus achieving seamless cluster head switching. When the backup cluster head is notified to be replaced as the primary cluster head, it will notify the member nodes in the current cluster via a HELLO probe packet message. The member nodes will update the cluster head information and reply with a cluster entry confirmation message. Based on the receipt of the confirmation message, the new cluster head will remove the drone nodes that have not replied with a cluster entry confirmation message from the list of nodes in the cluster.

[0040] In the above-mentioned multi-factor dynamic clustering method for UAV networking, when the link health... Below the health threshold Or the link health rate decline rate exceeds the deterioration threshold. When necessary, the weight coefficients of the corresponding clustering factors are changed: The weighting coefficient is calculated when any one or two of the following conditions are met. , , , Replacement: or

[0041] in, This indicates the current link health status. Indicates observation time The health of the preceding link; Link health The calculation formula is as follows:

[0042] in, This represents the total number of links within the current cluster. This represents the number of normal links within the current cluster.

[0043] In the aforementioned multi-factor dynamic clustering method for UAV networking, the weight coefficients , , , The replacement method is as follows: (1) The cluster head node simultaneously receives HELLO messages from member nodes within the cluster and records the average safety distance factor among the cluster members. Average speed similarity factor and average transmission performance factor The calculation formula is as follows:

[0044]

[0045] (2) Compare the three types of factors of the cluster head node and the cluster member nodes with the three types of factors among the cluster member nodes, and adjust the corresponding weights according to the comparison results: like This indicates that the members of the cluster are too dispersed. Set the safety distance factor adjustment weight flag D_Indicator to 1, otherwise set it to 0. like This indicates that the relative motion between members of the cluster is relatively intense. Set the speed similarity factor adjustment weight flag V_Indicator to 1, otherwise set it to 0. like This indicates that the transmission performance of each member in the cluster is better than that of the cluster head node. Set the transmission performance factor adjustment weight flag S_Indicator to 1, otherwise set it to 0. If D_Indicator is 1, V_Indicator is 0, and S_Indicator is 0, then the safety distance factor weights are adjusted. Increase 'a' to adjust the weight of the velocity similarity factor. and transmission performance factor weight Decrease b respectively; If D_Indicator is 1, V_Indicator is 0, and S_Indicator is 1, then the safety distance factor weights are adjusted. and transmission performance factor weight Increase b respectively, and the weight of the velocity similarity factor. Reduce a; If D_Indicator is 1, V_Indicator is 1, and S_Indicator is 0, then the safety distance factor weights will still be applied. Weights of speed similarity factors Increase b respectively, the weight of the transmission performance factor Reduce a; If D_Indicator is 1, V_Indicator is 1, and S_Indicator is 1, then the safety distance factor weights are adjusted. and transmission performance factor weight Increase b respectively, and the weight of the velocity similarity factor. Reduce a; If D_Indicator is 0, V_Indicator is 1, and S_Indicator is 1, then the speed similarity factor weights are adjusted. and transmission performance factor weight Increase b respectively to adjust the weight of the safety distance factor. Decrease a; If D_Indicator is 0, V_Indicator is 1 and S_Indicator is 0, then no adjustment is needed; If D_Indicator is 0, V_Indicator is 0 and S_Indicator is 1, then the transmission performance factor weights are adjusted. Increase 'a' to adjust the weight of the safety distance factor. Weights of speed similarity factors Decrease b respectively; If D_Indicator is 0, V_Indicator is 0, and S_Indicator is 0, then no adjustment is made.

[0046] An electronic device, including a memory and a processor: The memory is used to store one or more computer instructions; The processor is used to execute one or more computer instructions to implement the steps of the above method.

[0047] Compared with the prior art, the present invention has at least the following beneficial effects: This invention provides a multi-factor dynamic clustering method for UAV networking that integrates safety distance and transmission performance. It introduces multiple factors, such as safety distance factors and transmission performance factors among UAV nodes, to calculate cluster election indicators. This avoids the risk of excessively dense node distribution around the cluster head in UAV networking, ensuring that the cluster head node is in a low-risk state with a safe distance. Furthermore, cluster heads are elected based on node transmission performance. Simultaneously, based on the cluster election indicators, an efficient method for clustering and cluster maintenance is designed. In addition, in a preferred embodiment of this invention, the weight coefficients can be dynamically adjusted based on the link health of intra-cluster member nodes maintained by the cluster head node to achieve optimized updates to the cluster structure. Attached Figure Description

[0048] Figure 1 This is a flowchart of the multi-factor dynamic clustering method for UAV networking in an embodiment of the present invention; Figure 2 This is a schematic diagram of the safe distance in an embodiment of the present invention; Figure 3 This is a schematic diagram of the HELLO probe packet format in an embodiment of the present invention; Figure 4 This is a schematic diagram of replacing the cluster head in an embodiment of the present invention; Figure 5 This is a schematic diagram of the link statistics process in an embodiment of the present invention. Detailed Implementation

[0049] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments: like Figure 1 As shown, the multi-factor dynamic clustering method for UAV networking provided in this embodiment of the invention includes cluster head election index calculation, networking clustering, cluster maintenance process, and adaptive adjustment of the weight factor of the cluster head election index. Specifically, it includes the following steps: I. Calculation of Cluster Head Election Indicators In this embodiment of the invention, the drone node is equipped with a navigation system by default, which can obtain its own coordinates and speed information. For drones Its position is represented as Its speed is expressed as .

[0050] The formula for calculating the cluster head election index is as follows: (1) In the formula, and This is the serial number ID of the drone in the network, which can uniquely identify a drone node. Indicates drone node Cluster head election indicators Indicates drone node Safety distance factor, Indicates drone node The velocity similarity factor with its neighboring UAV node j Indicates drone node energy factors, Indicates drone node The transmission performance factor of its neighboring nodes. , , , The weight coefficients of the corresponding clustering factors satisfy the following conditions: .

[0051] That is, drone nodes The cluster head election index is determined by its own energy factor, as well as its safe distance factor, velocity similarity factor, and transmission performance factor with neighboring nodes. The specific explanations of each cluster index are as follows: (1) Safety distance factor

[0052] A safe distance factor is used to measure the spatial proximity of unmanned aerial vehicles (UAVs). Closer distances between UAVs help maintain stable communication links, but excessively close distances can also cause a series of potential risks, such as aerodynamic interference leading to flight instability and interference with communication and navigation signals. Therefore, this invention introduces the concept of a safe distance when characterizing the proximity of UAVs, such as... Figure 2 As shown, This indicates the minimum safe distance between drone nodes. This indicates the communication range of the drone, and typically includes... When the spatial distance between the two drones is less than At this time, there will be risks such as flight instability or signal interference, which should be avoided. Therefore, a "penalty" factor is applied to the safety distance factor between such close-knit drone nodes, making its value greater than the normal transmission distance of the drone nodes, thereby reducing the cluster head election index value.

[0053] The distance between two UAVs i and j that can communicate with each other. The calculation is performed using the following formula: (2) In the formula, This represents the actual spatial distance between drone i and drone j, which can be calculated using drone coordinate information.

[0054] (3) The distance factor after normalizing the distance between drones is shown in the following formula.

[0055] (4) The average distance factor of drone node i can be obtained by calculating the distance factor of other drone nodes j within the communication coverage area of ​​drone node i and taking the average value.

[0056] (5) (2) Velocity similarity factor

[0057] The speed similarity factor is used to measure the speed consistency between drones. When drones maintain similar directions of movement and speeds, it helps to maintain stable communication links and topology.

[0058] Taking UAV i as a reference, the velocity differences of UAV j relative to it on the x, y, z coordinate axes are as follows: (6) (7) (8) , These represent the angles between the velocity vector of UAV i and the x, y, and z coordinate axes, respectively. The coordinate system can be selected as follows: the origin o can be selected at the ground control center, with the east and north directions on the horizontal plane of the control center as the x and y axes, and the direction perpendicular to the ground and upward from the control center as the z axis.

[0059] The drone i is within communication range with The average velocity difference between adjacent UAV nodes on the x, y, z coordinate axes is: (9) (10) (11) Then the drone node i and The average speed difference between adjacent drone nodes is (12) Then, the variance of the velocity difference between node i and its neighboring UAV nodes along the x, y, and z axes is calculated. (13) (14) (15) The average of the variances of the velocity differences along the x, y, and z axes can be obtained. (16) The velocity similarity factor is then expressed as (17) (3) Energy factors

[0060] The energy factor is used to measure the remaining energy of a drone node and is calculated using the following formula.

[0061] (18) in, This indicates the initial energy of the drone. This indicates the total energy consumption of the drone, which can be calculated using the following formula: (19) in, The energy consumed by the drone to transmit data The energy consumed by the drone to receive data The energy consumed by the drone to fly The energy consumed by the drone to perform other tasks, such as the energy consumed by reconnaissance using camera devices.

[0062] (4) Transmission performance factor

[0063] The transmission performance factor is used to measure the service data transmission between UAV i and other UAV nodes within its transmission coverage area, and is specifically calculated using the following formula.

[0064] (20) in, This indicates the specific time period for observing data packet transmission. Indicates that drone node i is at time The total number of data packets transmitted to node j within its communication coverage area. Indicates time The number of data packets that meet relevant business metrics (e.g., data packets that meet business latency requirements) received by UAV node j from node i.

[0065] II. Networking and Clustering Process Based on the cluster head election index calculation method proposed in this invention, UAV nodes combine four parameters—safety distance factor, speed similarity factor, energy factor, and transmission performance factor—to calculate their own cluster head election index through a weighted summation method and compare them with each other. The UAV node with the highest weight is elected as the cluster head. The specific network clustering process is as follows: (1) The UAV nodes in the network periodically broadcast their own HELLO detection packets and receive HELLO detection packets broadcast by other UAV nodes. The HELLO detection packets include location coordinates, speed information, service transmission information, cluster control information, UAV node status, receiving identifier address and sending identifier address.

[0066] In the initial stage of network establishment, the ground control center assigns unified weight calculation coefficients to all UAVs within the network. Each UAV, based on its own position coordinates, speed, and service transmission status, periodically broadcasts HELLO detection packets to inform nearby UAVs of relevant information and collects HELLO detection packets from other nodes. The information that should be included in the HELLO packet is as follows: Figure 3 As shown, there are no restrictions on the arrangement format of each information field in the data packet: The "Receive Identifier Address" and "Send Identifier Address" fields represent the receiving node address (here, the broadcast address) and sending node address of the HELLO packet, respectively.

[0067] The "Location Coordinates" field displays the drone node's location information. The "Speed ​​Information" field indicates the drone's flight speed and direction. The "Service Transmission Information" field contains the number of data packets successfully received from other drones within a single hop range. The transmission performance from relevant drone nodes is represented by the "Drone Node Address" and the "Number of Data Packets".

[0068] "Clustering Control Information" indicates the relevant clustering control messages. "UAV Node Status" indicates the status of the UAV within the cluster. When it is "0", it means that it is a "pending node", which is the default status when the node has not joined the cluster; when it is "1", it means that it is a "joined cluster node"; when it is "2", it means that it is the "primary cluster head"; when it is "3", it means that it is the "standby cluster head". The processing flow for different node statuses will be given in the following process. "Cluster Head Election Index" is the cluster head election index calculated according to formula (1). "Cluster ID" indicates the identifier of the cluster to which the node belongs. "Standby Cluster Head Address" indicates the standby cluster head. "List of Nodes in the Cluster" identifies the addresses of each node in the current cluster. "Cluster Weight Adjustment Indicator" means that when it is "0", it means that the cluster weight coefficient does not need to be changed; when it is "1", it means that the cluster weight coefficient needs to be changed. The changed weight coefficient is placed in the "Cluster Weight Change Information" field. When the cluster weight coefficient is 1, the re-clustering process will be started.

[0069] Each drone node in the network will update the required information fields in the HELLO probe packet every time it broadcasts a HELLO packet.

[0070] (2) Based on the HELLO detection packet, calculate the safe distance factor, speed similarity factor, energy factor and transmission performance factor of each UAV node, and calculate the cluster head election index of each UAV node based on the safe distance factor, speed similarity factor, energy factor and transmission performance factor.

[0071] Calculate the safety distance factor based on its own position coordinates and the coordinates of other UAV nodes (see formulas (2) to (5) above). Calculate the speed similarity factor based on its own speed information and the speed information of other UAV nodes (see formulas (6) to (17) above). Based on the "service transmission information" in the HELLO packets from other UAV nodes, match the number of data packets successfully transmitted by the UAV sending the HELLO packet to that packet, and combine this with the total number of data packets sent by the UAV sending the HELLO packet as recorded by itself, to calculate the transmission performance factor (see formula (20) above). Calculate the energy factor based on its own energy usage (see formulas (18) to (19) above). Combine the four clustering weight factors and the weight coefficients of the clustering index to calculate its own cluster head election index (see formula (1) above), and update it in the next broadcast HELLO probe packet.

[0072] (3) Each UAV node sorts the cluster head election index values ​​of other UAV nodes received through the HELLO detection packet, as well as its own cluster head election index value, and elects a cluster head and a backup cluster head according to the sorting result to complete the clustering, specifically including: Store all cluster head election index values ​​for the drone nodes corresponding to the received HELLO packets. And perform a descending sort comparison: If we compare ourselves If the value is the largest, then it will become the head of the primary cluster, and the subsequent clusters will be sorted accordingly. Each node is designated as a cluster member, and the cluster head with the second-largest W value is designated as a backup cluster. The value has an upper limit. This is used to avoid excessive cluster members affecting clustering performance.

[0073] In the next broadcast HELLO packet, the drone node status updated to 2 (i.e., the primary cluster head node) in the "cluster control information" will be updated. The "cluster ID" will be its own (cluster head) address, the "backup cluster head" will be the selected backup cluster head address, and the list of nodes within the cluster will include the primary cluster head address and other nodes within the cluster. The address of each node.

[0074] After the primary cluster head node sends the HELLO packet for clustering, it will wait for clustering confirmation messages from the member nodes within each current cluster.

[0075] If it is not the largest cluster head, it waits for clustering information from the HELLO packet of the cluster head node. If the "alternate cluster head" is its own address, it sets its own node status to "3".

[0076] (4) Notify the surrounding UAV nodes of the clustering results. Each UAV node takes corresponding actions based on the notification results and its own status, including joining the cluster, confirming joining the cluster, not taking any action, or returning to step (3) and re-electing a cluster head and a backup cluster head. Specifically, this includes: When a node is in a "pending" state and receives a HELLO message from the cluster head node, it will check if its own node address is in the "cluster node list". If its own node address is there, it will join the cluster of the "cluster head node" and reply to the cluster head node with a cluster entry confirmation message; if its own node address is not there, it will ignore the cluster entry control information in the HELLO message.

[0077] For other "pending nodes" waiting to join a cluster, when they receive a cluster entry confirmation message from other nodes, they will remove the nodes in the newly created cluster from the cluster head election value index of all the drone nodes they have stored, and re-sort the weights of the remaining un-clustered nodes. Then they will re-execute step (3).

[0078] (5) Return to step (1) until all UAV nodes have been traversed; that is, all nodes repeat the above steps (1)-(4) until all nodes are clustered. At this time, each UAV node confirms the clustering status with each other through HELLO messages.

[0079] This invention effectively improves the rationality of clustering structure and service transmission performance. By integrating equal weights of a safety distance factor and a transmission performance factor, the cluster head election mechanism is systematically optimized. The safety distance factor serves as a protection mechanism, constraining the distribution density of nodes around the cluster head to avoid signal interference and collision risks caused by excessive node density, thereby establishing a safe operating environment for the cluster head. The transmission performance factor, through real-time evaluation of link quality, prioritizes nodes with strong data transmission capabilities as cluster heads, ensuring the service transmission quality between the cluster head node and all nodes within the cluster. By introducing the synergistic effect of these two weights, along with the speed similarity factor and energy factor, the overall performance and reliability of network-wide service transmission can be improved while ensuring a stable and rational clustering structure.

[0080] III. Cluster Maintenance Process (1) The process of nodes leaving and entering the cluster, namely, removing nodes from the cluster and adding new nodes to the cluster.

[0081] If the primary cluster head node does not receive a HELLO message from a node within the cluster within a certain period of time, that node will be removed from the cluster list. For nodes within the cluster that actively leave the cluster, the "cluster control information" of the current cluster can be directly removed when sending the HELLO message. This method allows the cluster head node to remove the node that leaves the cluster.

[0082] When a new node needs to join a cluster, it will obtain the status of surrounding clusters through the HELLO message of the nearest cluster head node, select a cluster that has not reached the maximum number of members in the cluster, and interact with the cluster head node of that cluster to confirm before joining the cluster.

[0083] (2) Cluster head switching process When the primary cluster head runs out of power or fails, a backup cluster head is replaced. The primary cluster head transmits its inter-cluster routing information to the backup cluster head and notifies neighboring cluster head nodes. For distance-vector routing protocols that only maintain next-hop nodes, this can be achieved by notifying neighboring cluster head nodes, enabling them to replace routes related to their primary cluster head with those of the newly replaced backup cluster head. This allows for seamless cluster head switching without requiring the backup cluster head to re-establish routes with other cluster heads using routing protocols. Figure 4 As shown.

[0084] When the standby cluster head is notified to be replaced as the primary cluster head, it will notify the member nodes in the current cluster via a HELLO message. The member nodes will then update their cluster head information and reply with a cluster entry confirmation message. Based on the receipt of confirmation messages, the new cluster head will remove any drone nodes that have not replied with a cluster entry confirmation message from the cluster node list.

[0085] This invention provides an efficient and reliable cluster maintenance mechanism. Through HELLO message status awareness and a backup cluster head design, it supports dynamic node joining / leaving of clusters and rapid, seamless switching in case of primary cluster head failure. The backup cluster head inherits the routing information of the original cluster head and promptly notifies neighboring cluster head nodes to update their routing tables. This mechanism effectively avoids the network-wide rerouting process triggered by cluster head changes in traditional methods, reduces routing control overhead, and ensures the continuity and real-time performance of data transmission.

[0086] IV. Adaptive Adjustment of Cluster Head Election Index Weighting Factors When the cluster head node transmits service data to member nodes within the cluster, it can observe transmission performance indicators such as latency, bandwidth, and packet loss rate of the service data through methods such as flow-based testing, and then perform statistical analysis on the link health between the cluster head node and each node within the cluster.

[0087] like Figure 5 As shown, if the service data packets transmitted between the cluster head node and the member nodes within the cluster meet the service performance requirements for a period of time, then the link is considered a normal link. If the number of data packets that fail to meet the service performance requirements exceeds a certain threshold, then the link is considered normal. If the link is abnormal, then the link is considered an abnormal link. Therefore, the link health status... The specific calculation method is as follows: (twenty one) in, This represents the total number of links within the current cluster. This represents the number of healthy links within the current cluster. When the link health level falls below the health threshold... Or the link health rate decline rate exceeds the deterioration threshold. , means as follows: or (twenty two) in This indicates the current link health status. Indicates observation time The health of the preceding link.

[0088] That is, the weighting factor is changed when one or two of the conditions in formula (22) occur.

[0089] At this point, the weighting coefficient adjustment process for the cluster head election index will be initiated, prompting the drone nodes to change the index calculation results. The specific process is as follows: (1) Maintain the safety distance factor for the cluster head and each cluster member for the cluster head UAV node. Speed ​​similarity factor and transmission performance factor Weighting coefficients , and Adjustments are made. The cluster head node simultaneously records the average safety distance factor among cluster members by receiving HELLO messages from member nodes within the cluster. Average speed similarity factor and average transmission performance factor The calculation formula is as follows: (twenty three) (twenty four) (25) (2) Compare the three types of factors of the cluster head node and the cluster member nodes with the three types of factors among the cluster member nodes, and adjust the corresponding weights according to the comparison results: like This indicates that the members of the cluster are too dispersed. Set the safety distance factor adjustment weight flag D_Indicator to 1, otherwise set it to 0.

[0090] like This indicates that the relative motion between members of the cluster is relatively intense. Set the velocity similarity factor adjustment weight flag V_Indicator to 1, otherwise set it to 0.

[0091] like This indicates that the transmission performance of each member in the cluster is better than that of the cluster head node. Set the transmission performance factor adjustment weight flag S_Indicator to 1, otherwise set it to 0.

[0092] Based on the situation of each flag, a relevant weighting coefficient adjustment strategy is adopted. The following weighting factor adjustments must ensure that the sum of the weighting coefficients is still 1 after the adjustment, i.e. Since this algorithm only involves adjusting three weight coefficients, if the increase in one weight coefficient is 'a', the sum of the decreases required to adjust the other two weight coefficients is also 'a'. The following scheme will use the changes a and b, satisfying a = 2b, as an example to illustrate the adaptive adjustment principle of the weight coefficients in this scheme.

[0093] If D_Indicator is 1, V_Indicator is 0, and S_Indicator is 0, then the safety distance factor weights are adjusted. Increase 'a' to adjust the weight of the velocity similarity factor. and transmission performance factor weight Decrease b respectively.

[0094] If D_Indicator is 1, V_Indicator is 0, and S_Indicator is 1, then the safety distance factor weights are adjusted. and transmission performance factor weight Increase b respectively, and the weight of the velocity similarity factor. Reduce 'a'.

[0095] If D_Indicator is 1, V_Indicator is 1, and S_Indicator is 0, then the safety distance factor weights will still be applied. Weights of speed similarity factors Increase b respectively, the weight of the transmission performance factor Reduce 'a'.

[0096] If D_Indicator is 1, V_Indicator is 1, and S_Indicator is 1, then the safety distance factor weights are adjusted. and transmission performance factor weight Increase b respectively, and the weight of the velocity similarity factor. Reduce 'a'.

[0097] If D_Indicator is 0, V_Indicator is 1, and S_Indicator is 1, then the speed similarity factor weights are adjusted. and transmission performance factor weight Increase b respectively to adjust the weight of the safety distance factor. Decrease a.

[0098] If D_Indicator is 0, V_Indicator is 1, and S_Indicator is 0, then no adjustment is needed.

[0099] If D_Indicator is 0, V_Indicator is 0 and S_Indicator is 1, then the transmission performance factor weights are adjusted. Increase 'a' to adjust the weight of the safety distance factor. Weights of speed similarity factors Decrease b respectively.

[0100] If D_Indicator is 0, V_Indicator is 0, and S_Indicator is 0, then no adjustment is made.

[0101] (3) The cluster head node includes the above weight adjustment strategy in the next round of broadcast HELLO message to notify the nodes in the cluster. The nodes in the cluster then re-execute the cluster head election process within the current cluster based on the weight adjustment results.

[0102] This invention supports dynamic adaptive adjustment of weighting factors. The cluster head node evaluates and dynamically adjusts the weight coefficients of each clustering factor in real time based on the health of links within the cluster and the status of members, enabling the clustering strategy to self-optimize as the network status changes, effectively improving the adaptability of UAV networking to complex flight environments and changing service requirements.

[0103] The technical solution of this invention mainly includes the following core components: (1) A weighted calculation method for cluster head election index based on multiple factors such as security distance and transmission performance.

[0104] A comprehensive cluster head election index is constructed by introducing multiple key factors, including safe distance factors, speed similarity factors, energy factors, and transmission performance factors among UAV nodes. The adaptability of each node is quantified through weighted summation, providing a scientific basis for cluster head election. The selected cluster head should, as far as possible, ensure the following effects: The safety distance factor avoids overcrowding of nodes and improves cluster head security; the speed similarity factor maintains motion consistency and enhances cluster structure stability; the energy factor ensures service forwarding capabilities and extends network lifetime; and the transmission performance factor optimizes link quality and ensures data transmission reliability. This enables efficient and stable cluster construction and maintenance in dynamic environments.

[0105] (2) Cluster head election and switching scheme.

[0106] Based on the aforementioned election metrics, an efficient cluster initialization and dynamic maintenance mechanism was designed. Nodes exchange status information by periodically broadcasting HELLO probe packets and autonomously elect cluster heads and backup cluster heads according to the metric values, achieving rapid and stable cluster formation. Simultaneously, it supports dynamic node entry / exit from clusters and seamless failover in case of primary cluster head failure, allowing backup cluster heads to effectively inherit the relevant routing information of the original cluster head, ensuring the reliability and continuity of the network topology.

[0107] (3) Dynamic adjustment scheme for weighting factors.

[0108] The cluster head node dynamically evaluates and adjusts the weight coefficients of each cluster factor based on the real-time health status of the links within the cluster. By comparing the differences between the cluster head and cluster members in terms of distance, speed, and transmission performance, the weight allocation is adaptively optimized, thereby maintaining the optimal cluster structure even when the network state changes, and improving the overall network adaptability and robustness.

[0109] The above description is only the best specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the protection scope of the present invention.

[0110] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A multi-factor dynamic clustering method for unmanned aerial vehicle (UAV) networking, characterized in that, include: S1. Within the network, UAV nodes periodically broadcast their own HELLO detection packets and receive HELLO detection packets broadcast by other UAV nodes. The HELLO detection packets include location coordinates, speed information, service transmission information, cluster control information, UAV node status, receiving identifier address, and sending identifier address. S2. Based on the HELLO detection packet, calculate the safe distance factor, speed similarity factor, energy factor and transmission performance factor of each UAV node, and calculate the cluster head election index of each UAV node based on the safe distance factor, speed similarity factor, energy factor and transmission performance factor. S3. Sort the cluster head election index values ​​of each UAV node, and elect a cluster head and a backup cluster head according to the sorting results to complete the cluster assembly. S4. Notify the surrounding drone nodes of the clustering results. Each drone node takes corresponding actions based on the notification results and its own status, including joining the cluster, confirming joining the cluster, not taking any action, or returning to step S3, and re-electing a cluster head and a backup cluster head. S5. Return to step S1 until all drone nodes have been traversed; S6. Complete the removal of nodes within the cluster, the addition of new nodes to the cluster, and / or the switching of the cluster head.

2. The multi-factor dynamic clustering method for UAV networking according to claim 1, characterized in that, In step S1, the service transmission information includes the number of data packets that the current UAV successfully receives from other UAVs within a single hop range. The transmission performance of other UAV nodes is represented by the UAV node address and the number of data packets.

3. The multi-factor dynamic clustering method for UAV networking according to claim 1, characterized in that, In step S2, the safety distance factor is calculated based on its own position coordinates and the coordinates of other UAV nodes. The speed similarity factor is calculated based on its own speed information and the speed information of other UAV nodes; The energy factor is calculated based on its own energy usage. The transmission performance factor is calculated based on the service transmission information from the HELLO packets of other drone nodes, matching the number of data packets successfully transmitted by the drone itself to the HELLO packet, and combining it with the total number of data packets sent by the drone to the HELLO packet as recorded by itself.

4. The multi-factor dynamic clustering method for UAV networking according to claim 3, characterized in that, The safety distance factor The calculation formula is as follows: in, The distance factor, which represents the normalized distance between drones, is expressed as: The actual spatial distance between drone i and drone j is calculated using the following drone coordinate information: The distance between two communicating drones i and j is calculated using the following formula: This indicates the minimum safe distance between drone nodes. This indicates the communication range of the drone.

5. The multi-factor dynamic clustering method for UAV networking according to claim 3, characterized in that, The speed similarity factor It is obtained through the following method: Taking UAV i as a reference, the velocity differences of UAV j relative to it on the x, y, z coordinate axes are as follows: , These represent the angles between the velocity vector of UAV i and the x, y, and z coordinate axes, respectively. The drone i is within communication range with The average velocity difference of adjacent UAV nodes on the x, y, z coordinate axes is as follows: Then the drone node i and The average speed difference between adjacent drone nodes is as follows: The variances of the velocity differences between node i and its neighboring UAV nodes along the x, y, and z axes are then calculated as follows: The average of the variances of the velocity differences along the x, y, and z axes is obtained as follows: Then the velocity similarity factor Represented as: 。 6. The multi-factor dynamic clustering method for UAV networking according to claim 3, characterized in that, The energy factor The calculation formula is as follows: in, This indicates the initial energy of the drone. The total energy consumption of the drone is calculated using the following formula: in, The energy consumed by the drone to transmit data The energy consumed by the drone to receive data The energy consumed by the drone to fly The energy consumed by the drone to perform other tasks.

7. The multi-factor dynamic clustering method for UAV networking according to claim 3, characterized in that, The transmission performance factor The calculation is performed using the following formula: in, This indicates the specific time period for observing data packet transmission. Indicates that drone node i is at time The total number of data packets transmitted to node j within its communication coverage area. Indicates time The number of data packets that meet the relevant business indicators among the data packets received by UAV node j from node i.

8. The multi-factor dynamic clustering method for UAV networking according to claim 1, characterized in that, Based on the cluster head election index of the drone nodes in step S2 The calculation formula is as follows: in, and This indicates the drone's serial number (ID) in the network. Indicates drone node Safety distance factor, Indicates drone node The velocity similarity factor with its neighboring UAV node j Indicates drone node energy factors, Indicates drone node The transmission performance factor of its neighboring nodes; , , , The weight coefficients of the corresponding clustering factors satisfy the following conditions: .

9. The multi-factor dynamic clustering method for UAV networking according to claim 1, characterized in that, In step S3, each UAV node sorts the cluster head election index values ​​of other UAV nodes received through the HELLO detection packet, as well as its own cluster head election index value, and elects a cluster head and a backup cluster head according to the sorting result to complete the clustering, including: If the cluster head election index is used Sort and get itself If the value is the largest, then it will become the head of the primary cluster, and the subsequent clusters will be sorted accordingly. Each node serves as a member of its own cluster. The second largest cluster head is used as a backup cluster. The value satisfies the condition of being less than the upper limit. .

10. The multi-factor dynamic clustering method for UAV networking according to claim 1, characterized in that, In step S4, the clustering results are notified to surrounding UAV nodes. Each UAV node takes corresponding actions based on the notification results and its own status, including joining a cluster, confirming joining a cluster, doing nothing, or returning to step S3, and re-electing a cluster head and a backup cluster head, including: When a drone node is in an undecided state and receives a HELLO probe packet message from a cluster head node, it will check if its own node address exists in the cluster node list. If its own node address exists, it will join the cluster of the cluster head node and reply to the cluster head node with a cluster entry confirmation message; if its own node address does not exist, it will ignore the cluster entry control information of the HELLO probe packet message. For other pending nodes waiting to join a cluster, upon receiving a cluster joining confirmation message from another node, the node in the newly created cluster will be removed from the cluster head election value index of all drone nodes stored in the node itself, and the weights of the remaining un-clustered nodes will be reordered. Then, the process returns to step S3.

11. The multi-factor dynamic clustering method for UAV networking according to claim 1, characterized in that, In step S6, completing the removal of nodes within the cluster includes: If the primary cluster head node does not receive a HELLO probe packet message from a node in the cluster within a certain period of time, the node is removed from the cluster list; for nodes in the cluster that actively leave the cluster, the cluster control information of the current cluster is directly removed when sending the HELLO probe packet message, so that the cluster head node can remove the node from the cluster. Adding a new node to a cluster includes: When a new node needs to join a cluster, it will obtain the status of surrounding clusters through the HELLO probe packet message of the nearest cluster head node, select a cluster that has not reached the maximum number of members in the cluster, and interact with the cluster head node of that cluster to confirm before joining the cluster.

12. The multi-factor dynamic clustering method for UAV networking according to claim 1, characterized in that, In step S6, completing the cluster head switching includes: When the primary cluster head runs out of energy or is damaged, the backup cluster head is replaced. The primary cluster head transmits its inter-cluster routing information to the backup cluster head and notifies the neighboring cluster head nodes. For distance vector routing protocols that only maintain the next-hop node, the neighboring cluster head nodes are notified to replace the routes related to their primary cluster head with the newly replaced backup cluster head, thus achieving seamless cluster head switching. When the backup cluster head is notified to be replaced as the primary cluster head, it will notify the member nodes in the current cluster via a HELLO probe packet message. The member nodes will update the cluster head information and reply with a cluster entry confirmation message. Based on the receipt of the confirmation message, the new cluster head will remove the drone nodes that have not replied with a cluster entry confirmation message from the list of nodes in the cluster.

13. The multi-factor dynamic clustering method for UAV networking according to claim 8, characterized in that, When link health Below the health threshold Or the link health rate decline rate exceeds the deterioration threshold. When necessary, the weight coefficients of the corresponding clustering factors are changed: The weighting coefficient is calculated when any one or two of the following conditions are met. , , , Replacement: or in, This indicates the current link health status. Indicates observation time The health of the preceding link; Link health The calculation formula is as follows: in, This represents the total number of links within the current cluster. This represents the number of normal links within the current cluster.

14. The multi-factor dynamic clustering method for UAV networking according to claim 13, characterized in that, Weighting coefficient , , , The replacement method is as follows: (1) The cluster head node simultaneously receives HELLO messages from member nodes within the cluster and records the average safety distance factor among the cluster members. Average speed similarity factor and average transmission performance factor The calculation formula is as follows: (2) Compare the three types of factors of the cluster head node and the cluster member nodes with the three types of factors among the cluster member nodes, and adjust the corresponding weights according to the comparison results: like This indicates that the members of the cluster are too dispersed. Set the safety distance factor adjustment weight flag D_Indicator to 1, otherwise set it to 0. like This indicates that the relative motion between members of the cluster is relatively intense. Set the speed similarity factor adjustment weight flag V_Indicator to 1, otherwise set it to 0. like This indicates that the transmission performance of each member in the cluster is better than that of the cluster head node. Set the transmission performance factor adjustment weight flag S_Indicator to 1, otherwise set it to 0. If D_Indicator is 1, V_Indicator is 0, and S_Indicator is 0, then the safety distance factor weights are adjusted. Increase 'a' to adjust the weight of the velocity similarity factor. and transmission performance factor weight Decrease b respectively; If D_Indicator is 1, V_Indicator is 0, and S_Indicator is 1, then the safety distance factor weights are adjusted. and transmission performance factor weight Increase b respectively, and the weight of the velocity similarity factor. Reduce a; If D_Indicator is 1, V_Indicator is 1, and S_Indicator is 0, then the safety distance factor weights will still be applied. Weights of speed similarity factors Increase b respectively, the weight of the transmission performance factor Reduce a; If D_Indicator is 1, V_Indicator is 1, and S_Indicator is 1, then the safety distance factor weights are adjusted. and transmission performance factor weight Increase b respectively, and the weight of the velocity similarity factor. Reduce a; If D_Indicator is 0, V_Indicator is 1, and S_Indicator is 1, then the speed similarity factor weights are adjusted. and transmission performance factor weight Increase b respectively to adjust the weight of the safety distance factor. Decrease a; If D_Indicator is 0, V_Indicator is 1 and S_Indicator is 0, then no adjustment is needed; If D_Indicator is 0, V_Indicator is 0 and S_Indicator is 1, then the transmission performance factor weights are adjusted. Increase 'a' to adjust the weight of the safety distance factor. Weights of speed similarity factors Decrease b respectively; If D_Indicator is 0, V_Indicator is 0, and S_Indicator is 0, then no adjustment is made.

15. An electronic device, characterized in that, Including memory and processor: The memory is used to store one or more computer instructions; The processor is used to execute one or more computer instructions to implement the steps of the method of claim 1.

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