An unmanned cluster self-organizing network dynamic topology and path optimization method

Abstract

This invention provides a dynamic topology and path optimization method for unmanned cluster ad hoc networks. It constructs a communication bandwidth requirement matrix and a distance matrix; designs a cost function based on distance, bandwidth requirement, and path hop count; establishes a fully connected communication topology matrix; and uses a shortest path algorithm, based on the cost function, to find the minimum cost path for each pair of nodes with communication needs. Based on all the obtained minimum cost paths, it constructs the optimal ad hoc network topology. By designing a precise cost function and combining it with Dijkstra's algorithm, it achieves efficient optimization of communication paths while simultaneously constructing the optimal ad hoc network topology, minimizing the communication cost function. The multi-factor coupling design of the cost function makes the path optimization results more accurate and adaptable to dynamically changing cluster environments. The combination with Dijkstra's algorithm enables efficient path search, making it suitable for medium- to large-scale unmanned clusters.

Description

Technical Field

[0001] This invention relates to the field of unmanned cluster communication networking technology, specifically to a method for dynamic topology and path optimization of unmanned cluster self-organizing networks. Background Technology

[0002] With the development of unmanned technology, unmanned swarms (such as drone swarms and unmanned vehicle swarms) have been widely used in various fields. The efficient communication of unmanned swarms relies on a reasonable network topology and optimized communication paths, directly affecting the swarm's collaborative efficiency. Network communication status is affected by distance, hop count, and bandwidth requirements. Increased distance leads to increased signal attenuation and link transmission latency; increased hop count accumulates forwarding latency and packet loss rate, increasing node processing overhead. Especially when bandwidth requirements are excessive, increasing hop count can cause link congestion, compromising the real-time performance and stability of communication. Summary of the Invention

[0003] To address the problems in existing technologies, this invention provides a dynamic topology and path optimization method for unmanned cluster self-organizing networks. This method can balance the quantitative communication costs of distance, hop count, and bandwidth requirements and achieve collaborative optimization of topology and path, with the aim of reducing the total cost of cluster communication.

[0004] A method for dynamic topology and path optimization in an unmanned self-organizing network, characterized by the following steps: Step 1: Construct a communication bandwidth requirement matrix and a distance matrix. The communication bandwidth requirement matrix is ​​used to describe the communication bandwidth requirements between nodes in the unmanned cluster. The distance matrix is ​​used to describe the physical distance between nodes. ;in, and All are symmetric matrices with zero diagonal elements; Step 2: Design a cost function based on distance, bandwidth requirements, and path hop count to quantify the communication cost of the communication topology. The cost function includes the cost of each hop on the path. The cost of each hop is calculated based on the distance of the hop, the total number of hops on the path containing the hop, and the maximum communication distance. The maximum communication distance is determined based on the transmit power, receive sensitivity, and signal frequency. Step 3: Establish a fully connected communication topology matrix. Using the shortest path algorithm, based on the cost function in Step 2, find the minimum cost path for each pair of nodes with communication needs. Step 4: Based on all the minimum cost paths obtained in Step 3, construct the optimal self-organizing network topology, that is, retain the links contained in all minimum cost paths and disconnect the rest of the links.

[0005] Furthermore, the cost function mentioned in step 2 is specifically: in, It is the cost of a specific communication path for a specific communication pair. in, This represents the distance between the communication node pairs; It is a constant, defined as the inherent overhead of establishing a connection for a communication pair; The speed of light; This represents the total number of hops in the current transmission path. This refers to the current jump order; This represents the distance between the two nodes in the current skip order; It refers to the maximum stable communication distance under specific transmit power, receive sensitivity, and signal frequency. The core innovation of this invention lies in the design of the communication cost function, specifically reflected in: (1) Distance segmentation penalty mechanism: Quadratic penalties are applied according to the distance threshold to more accurately reflect the high cost characteristics of long-distance communication; (2) Multi-factor coupling model: Physical distance, communication bandwidth requirement and path hop count are coupled through a product to achieve nonlinear quantification of "the greater the distance, the greater the bandwidth requirement, and the more hops, the higher the communication cost"; (3) Dynamic adaptability: The cost function can be adjusted according to the actual cluster scenario (such as distance threshold, bandwidth requirement weight) to adapt to unmanned clusters of different sizes and tasks.

[0006] Further details include: maximum communication distance. Confirmed using the following formula: Transmit power, in dBm; Receiver sensitivity at a specific transmission rate, expressed in dBm; The signal frequency is expressed in MHz.

[0007] Furthermore, the shortest path algorithm mentioned in step 3 is Dijkstra's algorithm, specifically including: Step 3.1: Initialize the node cost array, setting the starting node cost to 0 and the costs of the remaining nodes to infinity; Step 3.2: Use a priority queue to store the nodes to be processed. The queue elements include the current cost, the current node, and the current hop count. Step 3.3: Traverse the adjacent nodes of the current node, calculate the link cost and update the path cost. If a better path is found, update the cost array and the predecessor node. Step 3.4: Terminate the search after reaching the target node and reconstruct the optimal path using the predecessor node.

[0008] Further, the communication bandwidth demand matrix mentioned in step 1 Represents communication node With communication nodes Communication bandwidth requirements between them; the distance matrix Represents communication node With communication nodes The straight-line distance between them.

[0009] Furthermore, after constructing the optimal self-organizing network topology in step 4, steps 1 to 4 are repeated according to dynamically changing communication needs or node locations to achieve dynamic optimization of the topology.

[0010] The beneficial effects of this invention are as follows: By designing a precise cost function and combining it with Dijkstra's algorithm, efficient optimization of communication paths is achieved, while constructing an optimal self-organizing network topology to minimize the communication cost function. The multi-factor coupling design of the cost function (comprehensively considering distance, hop count, and bandwidth requirements) makes the path optimization results more accurate and adaptable to dynamically changing cluster environments. Combined with Dijkstra's algorithm, efficient path search is achieved, which is suitable for medium-to-large-scale unmanned clusters (100 nodes can be computed in just 0.6 milliseconds on an AMD R7 7745H CPU). Attached Figure Description

[0011] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the optimal network topology and optimal transmission path in scenario 1; Figure 3 This is a schematic diagram of the optimal network topology and optimal transmission path in scenario 2. Detailed Implementation

[0012] The present invention will now be described in detail with reference to the accompanying drawings. Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The directional terms such as left, center, right, top, and bottom in the embodiments of the present invention are only relative concepts or referenced to the normal use state of the product, and should not be considered restrictive.

[0013] First embodiment, based on scenario 1: The communication requirements of a cluster of 6 drones are as follows: The communication distance is as follows: A method for dynamic topology and path optimization in unmanned self-organizing networks, such as Figure 1 As shown, it includes the following steps: Step 1: Construct the communication bandwidth requirement matrix and distance matrix; Communication bandwidth demand matrix : Distance matrix : Step 2: Establish the communication cost function The communication cost described above: in, It is the cost of a specific communication path for a specific communication pair: Here, we ignore the inherent overhead of communication in establishing a connection, that is... =0; =30dBm (the upper limit of the typical transmit power range (20~30dBm) for UAV communication equipment); when When less than 10Mbps, When the value is -95, the maximum single-hop communication distance L = 28km; when When it is greater than 10Mbps, Taking -80, the maximum single-hop communication distance L = 5.28km; frequency =1500MHz; Step 3: Optimize the path using Dijkstra's algorithm. The optimization result is as follows: Figure 2 As shown; Step 4: Construct the optimal ad hoc network topology matrix, as follows: Optimization results show that communication pairs (A,D) and (A,F) have high data bandwidth requirements and long single-hop transmission distances. and The current transmission bandwidth exceeds the maximum transmission distance, so Dijkstra's algorithm selects relay mode for transmission and uses only one relay node; the data bandwidth requirement for communication pair (A,E) is relatively small, and the single-hop transmission distance is ( If the distance is less than the maximum transmission distance required for the current needs, the algorithm will choose the single-hop direct connection mode.

[0014] The second embodiment, based on scenario 2: The communication requirements of a cluster of 5 drones are as follows: The communication distance is as follows: Step 1: Construct the communication bandwidth requirement matrix and distance matrix.

[0015] Communication bandwidth demand matrix : Distance matrix : Establish communication cost function The communication cost described above: in, It is the cost of a specific communication path for a specific communication pair: in, This represents the distance between the communication node pairs; It is a constant, defined as the inherent overhead of establishing a connection for a communication pair; The speed of light; This represents the total number of hops in the current transmission path. This refers to the current jump order; This represents the distance between the two nodes in the current skip order; It refers to the maximum stable communication distance under specific transmit power, receive sensitivity, and signal frequency. Maximum communication distance Confirmed using the following formula: Transmit power, in dBm; Receiver sensitivity at a specific transmission rate, expressed in dBm; The signal frequency is expressed in MHz. Here, we ignore the inherent overhead of communication in establishing a connection, i.e., a=0; =30dBm (the upper limit of the typical transmit power range (20~30dBm) for UAV communication equipment); when When less than 10Mbps, Taking -95, the maximum single-hop transmission distance L = 28km; when When it is greater than 10Mbps, Taking -80, the maximum single-hop transmission distance L = 5.28km; frequency =1500MHz; Step 3: Optimize the path using Dijkstra's algorithm. The cost of all paths for the communication pair (A,E) is shown in the table below. Optimization results are as follows Figure 3 As shown; Step 4: Construct the optimal ad hoc network topology matrix, as follows: Regarding path selection, Dijkstra's algorithm prioritizes the path with the fewest hops. For example, between the shortest path (ABDE, cost 85.36) and the path with the fewest hops (ACE, cost 18.04), Dijkstra's algorithm chooses the latter. Reducing the number of hops can effectively reduce the data forwarding load of ad hoc network communication. Similarly, for two-hop transmission paths (ABE, cost 22.0) and (ACE, cost 18.04), the fundamental reason why Dijkstra's algorithm chooses the latter is that under the ABE path, the single-hop distance of BE is close to the limit distance at that rate. Dijkstra's algorithm calculates that its cost is higher than that of ACE, so it chooses the latter, further proving the rationality of Dijkstra's algorithm.

[0016] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic topology and path optimization in an unmanned self-organizing network, characterized in that, Includes the following steps: Step 1: Construct a communication bandwidth requirement matrix and a distance matrix. The communication bandwidth requirement matrix is ​​used to describe the communication bandwidth requirements between nodes in the unmanned cluster. The distance matrix is ​​used to describe the physical distance between nodes. ;in, and All are symmetric matrices with zero diagonal elements; Step 2: Design a cost function based on distance, bandwidth requirements, and path hop count to quantify the communication cost of the communication topology. The cost function includes the cost of each hop on the path. The cost of each hop is calculated based on the distance of the hop, the total number of hops on the path containing the hop, and the maximum communication distance. The maximum communication distance is determined based on the transmit power, receive sensitivity, and signal frequency. Step 3: Establish a fully connected communication topology matrix. Using the shortest path algorithm, based on the cost function in Step 2, find the minimum cost path for each pair of nodes with communication needs. Step 4: Based on all the minimum cost paths obtained in Step 3, construct the optimal self-organizing network topology, that is, retain the links contained in all minimum cost paths and disconnect the rest of the links.

2. The method for dynamic topology and path optimization of unmanned cluster self-organizing networks according to claim 1, characterized in that: The cost function mentioned in step 2 is specifically as follows: in, It is the cost of a specific communication path for a specific communication pair. in, This represents the distance between the communication node pairs. It is a constant, defined as the inherent overhead of establishing a connection for a communication pair; The speed of light; This represents the total number of hops in the current transmission path. This refers to the current jump order; This represents the distance between the two nodes in the current skip order; It refers to the maximum communication distance that can be stably received under specific transmit power, receive sensitivity, and signal frequency.

3. The method for dynamic topology and path optimization of unmanned cluster self-organizing networks according to claim 2, characterized in that: Maximum communication distance Confirmed using the following formula: Transmit power, in dBm; Receiver sensitivity at a specific transmission rate, expressed in dBm; The signal frequency is expressed in MHz.

4. The method for dynamic topology and path optimization of unmanned cluster self-organizing networks according to claim 1, characterized in that: The shortest path algorithm mentioned in step 3 is Dijkstra's algorithm, which specifically includes: Step 3.1: Initialize the node cost array, setting the starting node cost to 0 and the costs of the remaining nodes to infinity; Step 3.2: Use a priority queue to store the nodes to be processed. The queue elements include the current cost, the current node, and the current hop count. Step 3.3: Traverse the adjacent nodes of the current node, calculate the link cost and update the path cost. If a better path is found, update the cost array and the predecessor node. Step 3.4: Terminate the search after reaching the target node and reconstruct the optimal path using the predecessor node.

5. The method for dynamic topology and path optimization of unmanned cluster self-organizing networks according to claim 1, characterized in that: The communication bandwidth demand matrix mentioned in step 1 Represents communication node With communication nodes The communication bandwidth requirements between them; the distance matrix Represents communication node With communication nodes The straight-line distance between them.

6. The method for dynamic topology and path optimization of unmanned cluster self-organizing networks according to claim 1, characterized in that: After constructing the optimal self-organizing network topology in step 4, steps 1 to 4 are repeated according to dynamically changing communication needs or node locations to achieve dynamic optimization of the topology.

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