DTN routing optimization method in unmanned cluster dynamic network

Abstract

The invention discloses a DTN routing optimization method in an unmanned cluster dynamic network, which belongs to the field of DTN routing adaptive optimization, and comprises the following steps: detecting and generating a network state, and predicting a future information delivery rate based on basic network historical information. And neighbor node state detection: generating the connectivity probability of the neighbor node and the destination node based on the connectivity relationship between the neighbor node and the destination node in the network. The method comprises the following steps of: determining a DTN routing decision, generating a routing decision based on an information delivery rate at a future moment and a communication relationship between a neighbor node and a destination node, sending DTN routing information, and quickly sending related service data information after the DTN routing decision is known, so that the service delivery rate of the information is improved. According to the invention, in a high-dynamic and complex unmanned cluster communication network environment, when a network topology cannot be stably and reliably established, an appropriate DTN routing sending strategy is actively adopted, the service delivery rate in an intermittent communication network is improved, and information sending of an unmanned cluster network in a high-dynamic scene is ensured.

Description

Technical Field

[0001] This invention relates to the field of DTN routing strategy optimization and generation for unmanned aerial vehicle (UAV) communication networks, and particularly to a DTN routing optimization method in a dynamic unmanned swarm network. Background Technology

[0002] With technological breakthroughs in unmanned aerial vehicles (UAVs) in areas such as flight endurance, intelligent flight control, and payload capacity, the deployment scale and application complexity of UAV swarms in civilian observation and collaborative scenarios continue to rise. UAV swarms are increasingly reliant on network communication and control. Due to the growing complexity of application scenarios, higher demands are placed on the reliable communication of the network in highly dynamic environments. UAV swarm networks typically exhibit characteristics such as high node mobility, frequent topology changes, and intermittent link connectivity. Especially in complex electromagnetic environments or areas obscured by obstacles, differences in flight speed, spatial distribution, and external signal interference among different nodes can easily lead to communication link interruptions or short-term difficulty in restoring the topology. This can result in data retransmission, a surge in control overhead, and even a decrease in mission execution efficiency or failure.

[0003] In existing UAV swarm network routing designs, most networks employ routing protocols based on prior topology assumptions (such as OLSR). These protocols rely on periodic neighbor discovery and routing table maintenance, which can easily lead to slow topology convergence and control signaling consuming effective bandwidth in highly dynamic scenarios, ultimately resulting in the failure to deliver service data in a timely manner. Especially when nodes move rapidly or link quality fluctuates drastically, traditional routing mechanisms struggle to adapt to changes in network status in real time, often resulting in routing loops, broadcast storms, and other phenomena that severely impact the timely delivery of critical task data.

[0004] In recent years, to improve network reliability, research has gradually shifted to Delay / Disruption-Tolerant Networking (DTN) architecture, which addresses link interruptions through a "store-carry-forward" mechanism. However, existing DTN routing strategies in UAV swarm networks still lack sufficient quantitative awareness of network dynamics during the design process, and fail to deeply integrate situational parameters such as historical network connectivity probability and delivery success rate into network routing and forwarding decisions. Furthermore, in bandwidth-constrained environments, how to adaptively adjust the number of data forwarding broadcasts based on real-time topology stability to reduce redundant replication has become a key challenge in optimizing swarm network performance. Therefore, there is an urgent need to construct a DTN routing strategy that can perceive network dynamics and adaptively adjust routes based on topology connectivity and transmission success rate to ensure efficient and reliable information transmission even under unreliable link conditions. Summary of the Invention

[0005] In view of this, the present invention proposes a DTN routing optimization method in unmanned dynamic networks. This method can select the forwarding strategy with the highest information delivery rate in large-scale, highly dynamic and complex unmanned networks, thereby improving the information delivery rate, reducing the network packet loss rate, and reducing control overhead.

[0006] The technical solution adopted in this invention is as follows:

[0007] A DTN routing optimization method in an unmanned clustered dynamic network includes a network state detection and generation process, a neighbor node state detection process, a DTN routing decision generation process, and a DTN routing information transmission process. The specific steps are as follows:

[0008] Step 1, Network State Detection and Generation Process:

[0009] (101) Collect the transmission and delivery rate of service data between any two adjacent nodes k and k+1 in the network at time t in history. , … ;

[0010] (102) Based on the historical data delivery rate of service data in the network at time t, predict the delivery rate between two adjacent nodes k and k+1 at time t+1 in the future. ;

[0011] Step 2, Neighbor Node Status Detection Process:

[0012] (201) The current node counts the historical data of all neighboring nodes m within the historical time T, and obtains the total historical connection time between neighboring node m and destination node D within the historical time T. ;

[0013] (202) Based on the total historical connectivity time Calculate the connectivity probability between neighbor node m and destination node D within historical time T. ;

[0014] Step 3, DTN routing decision generation process:

[0015] (301) Based on the connectivity probability between the current node's neighbor node m and the destination node D. And the predicted transmission delivery rate between the current node and its neighboring nodes, generating the DTN information delivery rate of neighboring node m. ,choose The largest neighbor node is used as the next-hop broadcast node;

[0016] (302) DTN message delivery rate based on next-hop broadcast node Calculate the number of times the DTN route broadcasts information during information delivery;

[0017] Step 4, DTN routing information sending process:

[0018] After encountering the next-hop broadcast node, the drone node sends business data according to the number of information broadcasts.

[0019] Step 5: Set the next hop broadcast node as the current node, return to step 2, and continue until the destination node is reached.

[0020] Furthermore, the specific process of step (102) is as follows:

[0021] Based on the historical data delivery rate of service data in the network at time t, the weighted moving average method is used to predict the delivery rate between two adjacent nodes k and k+1 at time t+1 in the future:

[0022]

[0023] in, ,1≤ ≤ .

[0024] Furthermore, the connectivity probability in step (202) The calculation method is as follows:

[0025] .

[0026] Furthermore, in step (301), the DTN information delivery rate The calculation method is as follows:

[0027] = .

[0028] Furthermore, the number of information broadcasts in step (302) The calculation method is as follows:

[0029]

[0030] in, and For setting value, < <1.

[0031] Compared with the prior art, the beneficial effects achieved by this invention are as follows:

[0032] 1. This invention improves the delivery rate of information in networks with interrupted topology connections, thus ensuring improved network performance.

[0033] 2. This invention reduces the data transmission latency in the entire network, ensuring the guaranteed transmission of information even in a network environment with delayed interruption tolerance.

[0034] 3. The limitation on the number of information broadcasts in this invention, using DTN routing, avoids unrestrained broadcast data, reduces broadcast overhead, prevents broadcast data from congesting the transmission of normal data throughout the network, and ensures the reliability of the drone swarm network.

[0035] 4. The information transmission based on probability selection of neighbor nodes in this invention can greatly improve the success rate of end-to-end information transmission and avoid the problem of node information loss in highly dynamic networks. Attached Figure Description

[0036] Figure 1 This is an overall flowchart of the method in an embodiment of the present invention.

[0037] Figure 2 This is a schematic diagram of the network state detection and generation process in an embodiment of the present invention.

[0038] Figure 3 This describes the neighbor node status detection process in an embodiment of the present invention.

[0039] Figure 4 This describes the DTN routing decision generation process in an embodiment of the present invention. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0041] A DTN routing optimization method in an unmanned clustered dynamic network mainly includes a network state detection and generation process, a neighbor node state detection process, a DTN routing decision process, and a DTN routing information transmission process, such as... Figure 1 As shown, the process first calculates the historical delivery rate of service data in the network and predicts the end-to-end delivery rate of service data in the future. Based on the connection time from neighboring nodes to the destination node, the connection probability between neighboring nodes and the destination node is calculated. Based on the future end-to-end delivery rate of service data and the connection probability between neighboring nodes and the destination node, an adaptive DTN routing decision strategy is generated, which generates DTN neighbor delivery nodes and the number of information broadcasts to improve the delivery rate. Finally, given the DTN routing strategy, upon encountering a neighboring node specified in the routing strategy, a message is sent to that neighboring node, and the number of broadcasts specified in the routing strategy is continuously broadcast.

[0042] like Figure 1 As shown, the method mainly includes the following steps:

[0043] Step 1, Network Status Detection and Generation Process: Based on the transmission and delivery rate of historical network service data, predict the transmission and delivery rate of service data between any two adjacent nodes at time t+1 in the future;

[0044] Step 2, Neighbor Node State Detection: Based on the total connection time from the neighbor node to the destination node D, generate the connection probability from the neighbor node m to the destination node D.

[0045] Step 3, DTN routing decision generation: Based on the transmission delivery rate between two adjacent nodes at time t+1 in the unmanned cluster network and the connectivity probability from the neighbor node to the destination node, calculate the next-hop broadcast node and the number of information broadcasts for the DTN route to obtain the DTN routing strategy.

[0046] Step 4, DTN routing information transmission: Based on the DTN routing policy obtained in Step 3, the drone node sends service data after encountering relevant neighbor nodes;

[0047] Step 5: Set the next hop broadcast node as the current node, return to step 2, and continue until the destination node is reached.

[0048] Here is a more specific example: a DTN routing optimization method in an unmanned cluster dynamic network, including a network state detection and generation process, a neighbor node state detection process, a DTN routing decision process, and a DTN routing information transmission process.

[0049] Network state detection and generation refers to the process of predicting future link quality levels based on historical network state data using a moving average method during network operation, thus supporting subsequent DTN routing strategies. For example... Figure 2 As shown, the specific steps are as follows:

[0050] (101) Based on the OLSR protocol, collect the historical transmission and delivery rates of service data between any two adjacent nodes k and k+1 in the network from time 1 to time t. , … ;

[0051] (1022) Use the weighted moving average method to measure the transmission delivery rate between two adjacent nodes k and k+1 at time t+1 in the future. :

[0052]

[0053] in, ,…, ,…, ,1≤ ≤ .

[0054] The neighbor node state detection process refers to the process of a node detecting the historical connectivity time between its neighbor nodes and the destination node within a time interval T during node operation. Based on this historical information, decision-making information is provided to support subsequent DTN routing strategies for the node. For example... Figure 3As shown, the specific steps are as follows:

[0055] (201) Based on the OLSR protocol, the current node collects the historical data information of all neighboring nodes m within the historical time T, and obtains the total historical connection time between neighboring node m and destination node D within the historical time T. ;

[0056] (202) Based on the total historical connectivity time Calculate the connectivity probability between neighbor node m and destination node D within historical time T. ;

[0057] .

[0058] The DTN routing decision-making process refers to the rapid generation of DTN routing policies after knowing the link and node states, such as... Figure 4 As shown, the steps to generate connectable neighbor nodes and the number of times information is broadcast are as follows:

[0059] (301) Based on the connectivity probability between the current node's neighbor node m and the destination node D. And the predicted delivery rate between the current node and its neighboring nodes. Generate the DTN information delivery rate of neighbor node m ,choose The largest neighbor node is used as the next-hop broadcast node;

[0060] =

[0061] (302) DTN message delivery rate based on next-hop broadcast node Calculate the number of information broadcasts during DTN route delivery. ;

[0062] .

[0063] The DTN routing information transmission process refers to the process of initiating information transmission when encountering relevant neighbor nodes after the DTN routing decision is completed. The specific steps are as follows:

[0064] After encountering the next-hop broadcast node, the drone node sends business data according to the number of information broadcasts.

Claims

1. A DTN routing optimization method in an unmanned clustered dynamic network, characterized in that, The process includes network state detection and generation, neighbor node state detection, DTN routing decision generation, and DTN routing information transmission. The specific steps are as follows: Step 1, Network State Detection and Generation Process: (101) Collect the transmission and delivery rate of service data between any two adjacent nodes k and k+1 in the network at time t in history. , … ; (102) Based on the historical data delivery rate of service data in the network at time t, predict the delivery rate between two adjacent nodes k and k+1 at time t+1 in the future. ; Step 2, Neighbor Node Status Detection Process: (201) The current node counts the historical data of all neighboring nodes m within the historical time T, and obtains the total historical connection time between neighboring node m and destination node D within the historical time T. ; (202) Based on the total historical connectivity time Calculate the connectivity probability between neighbor node m and destination node D within historical time T. ; Step 3, DTN routing decision generation process: (301) Based on the connectivity probability between the current node's neighbor node m and the destination node D. And the predicted transmission delivery rate between the current node and its neighboring nodes, generating the DTN information delivery rate of neighboring node m. ,choose The largest neighbor node is used as the next-hop broadcast node; (302) DTN message delivery rate based on next-hop broadcast node Calculate the number of times the DTN route broadcasts information during information delivery; Step 4, DTN routing information sending process: After encountering the next-hop broadcast node, the drone node sends business data according to the number of information broadcasts. Step 5: Set the next hop broadcast node as the current node, return to step 2, and continue until the destination node is reached.

2. The DTN routing optimization method in an unmanned cluster dynamic network according to claim 1, characterized in that, The specific process of step (102) is as follows: Based on the historical data delivery rate of service data in the network at time t, the weighted moving average method is used to predict the delivery rate between two adjacent nodes k and k+1 at time t+1 in the future: in, ,1≤ ≤ .

3. The method for generating the minimum latency routing path for a multi-channel UAV swarm according to claim 1, characterized in that, Connectivity probability in step (202) The calculation method is as follows: 。 4. The method for generating the minimum delay routing path for a multi-channel UAV swarm according to claim 1, characterized in that, DTN information delivery rate in step (301) The calculation method is as follows: = 。 5. The method for generating the minimum delay routing path for a multi-channel UAV swarm according to claim 1, characterized in that, Number of information broadcasts in step (302) The calculation method is as follows: in, and For setting value, < <1.

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