Networking method and system based on unmanned aerial vehicle cluster flow load evolution

Abstract

The invention belongs to the technical field of network communication, and particularly provides a networking method and system based on unmanned aerial vehicle cluster flow load evolution, and the method comprises the steps: constructing an unmanned aerial vehicle cluster through employing unmanned aerial vehicle nodes; a data request packet and a data packet are forwarded by using a data consumer node, a data provider node and an intermediate node in combination with an unmanned aerial vehicle routing table, and data transmission is completed; establishing a networking method evolution route under different service flow loads by using a networking experiment; calculating a data request packet forwarding rate, and obtaining an unmanned aerial vehicle node sensing cluster service flow load; and sensing a cluster service traffic load and a networking method evolution route by using the unmanned aerial vehicle nodes, constructing a routing failure time evolution strategy based on traffic load optimization, and completing dynamic networking of the unmanned aerial vehicle cluster evolved along with the traffic load. According to the networking method, the networking strategy can be automatically adjusted, and high data transmission efficiency is always kept in the scene of unmanned aerial vehicle cluster flow load time varying.

Description

Technical Field

[0001] The present invention belongs to the field of network communication technology, and specifically relates to a networking method and system based on the evolution of drone cluster traffic load. Background Art

[0002] In recent years, with the widespread application of drone swarms in low-altitude communications and large-scale data transmission, the impact of traffic load on drone networks has become increasingly prominent. The traffic load requirements of drone swarms vary depending on the application scenario. In particular, in missions such as emergency communications, environmental monitoring, and military reconnaissance, the demand for data transmission bandwidth and network reliability is increasing.

[0003] Drone swarms offer flexible communication capabilities across a wide range of scenarios, but their communication load exhibits significant time-varying characteristics across different missions and environments. In low-traffic scenarios, drone swarms have low communication requirements, primarily used for basic data transmission and simple task communications. However, in high-traffic scenarios, drone swarms must handle a large number of data transmission tasks, such as real-time video transmission and environmental data collection, placing higher demands on network bandwidth and routing capabilities. Traditional content-centric networking approaches rely on pre-maintained, stable transmission paths and instead discover data content through flooding with data request packets. In environments with high traffic loads, this approach can easily lead to bandwidth bottlenecks and significant packet loss. In contrast, host-centric networking approaches rely on the exchange of control information to maintain routing tables and are more adaptable to high traffic loads. However, in low-traffic environments, historical routing information in these approaches can become outdated due to node mobility, leading to incorrect packet forwarding and subsequent packet loss, reducing transmission efficiency.

[0004] In the drone self-organizing network scenario, when the drone cluster service traffic load varies with time, how to achieve the evolution and adaptation of the networking method to ensure the efficiency of data transmission is a technical problem that needs to be solved urgently in this field. Summary of the Invention

[0005] In order to enable drone clusters to maintain stable and efficient data transmission in an environment with time-varying traffic load, the present invention discloses a networking method and system based on the evolution of drone cluster traffic load. The method utilizes drones' observation of cluster service traffic load to dynamically adjust the route expiration time in the routing table, and selects a networking method with strong adaptability in the current environment based on the validity of the routing entries. This method has the ability to adapt to time-varying traffic loads and effectively improves the stability and efficiency of data transmission.

[0006] A networking method based on the evolution of UAV swarm traffic load, including:

[0007] Using drone nodes to build a drone cluster; wherein the drone nodes in the drone cluster are divided into data consumer nodes, data provider nodes and intermediate nodes according to different business functions;

[0008] Utilizing the data consumer node, the data provider node, and the intermediate node, combined with the drone routing table, to forward the data request packet and the data packet, thereby completing data transmission;

[0009] Use networking experiments to establish an evolutionary path for networking methods under different service traffic loads;

[0010] All drone nodes in the drone cluster monitor the messages sent by neighboring nodes in the wireless channel during data transmission in real time, calculate the data request packet forwarding rate, and obtain the drone node-perceived cluster service traffic load;

[0011] Using drone nodes to perceive cluster service traffic load and the evolution path of the networking method, a routing failure time evolution strategy based on traffic load optimization is constructed;

[0012] Through the routing failure time evolution strategy based on traffic load optimization, the dynamic networking of drone clusters as the traffic load evolves is completed.

[0013] Preferably, the data transmission process includes:

[0014] Using the data consumer node to send a data request packet to the drone cluster;

[0015] The intermediate node receives the data request packet and forwards it to the data provider node, and records the data request path;

[0016] The data provider node receives the data request packet and generates a data packet and returns it to the upstream intermediate node in the data request path;

[0017] The upstream intermediate node receives the returned data packet, updates the drone routing table according to the data content identifier carried in the data packet, and returns the data packet to the data consumer node along the data request path, completing the transmission of a single data content.

[0018] Preferably, the method for the data consumer node to send a data request packet to the drone cluster includes:

[0019] When the data content identifier in the drone routing table becomes invalid, the data consumer node initiates a data acquisition request in a broadcast manner;

[0020] When the data content identifier in the drone routing table is valid, the data consumer node initiates a data acquisition request in a unicast manner according to the next-hop host identifier in the routing entry.

[0021] Preferably, the data transmission process further includes:

[0022] All drone nodes monitor messages sent by neighboring nodes in the wireless channel in real time, and the messages include data request packets or data packets;

[0023] Determine whether the data request packet or the data packet is sent to the drone node, and if so, receive and process it accordingly;

[0024] If the message is a data request packet and is received, the information carried in the message is parsed to obtain the status of the network environment around the drone node.

[0025] Preferably, the networking method evolution route is designed based on the hybrid networking method IHCR framework.

[0026] Preferably, the method for constructing a routing failure time evolution strategy based on traffic load optimization includes:

[0027] The sliding window estimation algorithm is used to reduce the noise of the data request packet forwarding rate observed by the current UAV node in the current time slot, and the actual value of the data request packet forwarding rate after denoising is obtained;

[0028] Calculating a data request packet forwarding rate change rate based on actual values ​​of the data request packet forwarding rates of the current time slot and adjacent time slots;

[0029] Multiply the exponential iteration function of the data request packet forwarding rate change rate of the current drone node by the routing failure time of the current time slot to obtain the routing failure time of the next time slot;

[0030] Based on the hybrid networking method IHCR framework combined with the data request packet forwarding rate, the routing failure time is dynamically adjusted to complete the construction of the routing failure time evolution strategy based on traffic load optimization.

[0031] The present invention also provides a networking system based on the evolution of UAV cluster traffic load, which is used to implement the method, including:

[0032] A drone cluster establishment module is used to build a drone cluster using drone nodes; wherein the drone nodes in the drone cluster are divided into data consumer nodes, data provider nodes and intermediate nodes according to different business functions;

[0033] A data transmission module is used to utilize the data consumer node, the data provider node and the intermediate node, combined with the drone routing table, to forward the data request packet and the data packet to complete the data transmission;

[0034] The evolutionary route experiment module is used to establish the evolutionary route of networking methods under different business traffic loads through networking experiments;

[0035] A load calculation module is used to use all drone nodes in the drone cluster to monitor in real time the messages sent by neighboring nodes in the wireless channel during data transmission, calculate the data request packet forwarding rate, and obtain the drone node-perceived cluster service traffic load;

[0036] An evolutionary strategy building module is used to use drone nodes to perceive the cluster service traffic load and the evolution path of the networking method to build a routing failure time evolution strategy based on traffic load optimization;

[0037] The dynamic networking module is used to complete the dynamic networking of drone clusters as the traffic load evolves through a routing failure time evolution strategy based on traffic load optimization.

[0038] Preferably, the data transmission module includes:

[0039] a data request packet sending unit, configured to send a data request packet to the drone cluster using the data consumer node;

[0040] An intermediate node forwarding unit, configured to receive the data request packet at the intermediate node and forward it to the data provider node, and record the data request path;

[0041] A data packet generating unit, configured for the data provider node to receive the data request packet and generate a data packet to return to an upstream intermediate node in the data request path;

[0042] The data packet return unit is used for the upstream intermediate node to receive the returned data packet, update the drone routing table according to the data content identifier carried in the data packet, return the data packet to the data consumer node along the data request path, and complete the transmission of a single data content.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The networking method and system designed in the present invention based on the evolution of drone cluster traffic load can dynamically adapt to scenarios with different drone cluster traffic load business demands, ensuring the high efficiency of data transmission.

[0045] The routing failure time evolution strategy designed for traffic load optimization in the present invention can obtain the current cluster service traffic load from the node perspective, and calculate the routing failure time of the updated node based on the cluster service traffic load, so that the proposed networking method can evolve in the direction of environmental adaptation based on the cluster traffic load. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 This is a flow chart of a networking method based on the evolution of drone cluster traffic load according to an embodiment of the present invention;

[0048] Figure 2 Figures 1 and 2 show experimental results of packet transmission success rates for various networking methods using OMNeT++ under different cluster traffic loads, using an embodiment of the present invention. (a) shows experimental results of packet transmission success rates for various networking methods under different numbers of service transmissions. (b) shows experimental results of packet transmission success rates for various networking methods under different service request intervals.

[0049] Figure 3 Graphs showing the relationship between data request packet forwarding rate and cluster traffic load using OMNeT++ in an embodiment of the present invention; (a) shows the relationship between data request packet forwarding rate and number of service transmissions; (b) shows the relationship between data request packet forwarding rate and service request rate;

[0050] Figure 4 This is a scenario diagram of the time-varying traffic load of a drone cluster performed by OMNeT++ in an embodiment of the present invention;

[0051] Figure 5 This is a result diagram of the change of UAV node routing failure time in a cluster traffic load time-varying scenario using OMNeT++ in an embodiment of the present invention;

[0052] Figure 6 This is a graph showing the experimental results of the data packet transmission success rate corresponding to the networking method of the present invention in a scenario where the cluster traffic load varies with time, performed by OMNeT++ in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] Example 1

[0056] like Figure 1 As shown in FIG, a networking method based on the evolution of UAV cluster traffic load includes:

[0057] S1: Use drone nodes to build a drone cluster; the drone nodes in the drone cluster are divided into data consumer nodes, data provider nodes and intermediate nodes according to different business functions.

[0058] Specifically, the data consumer node is the node that requests to obtain data content and serves as the initiator of the transmission service; the data provider node is the node that provides data content and serves as the responder of the transmission service; the intermediate node is other nodes located on the service transmission path, responsible for data forwarding, and serves as the relay of the transmission service.

[0059] It is worth noting that the same drone node can simultaneously play multiple roles. For example, a node can act as a data consumer in transmission service A and as an intermediary node in transmission service B.

[0060] The networking methods of drone swarms can be divided into host-centric and content-centric methods according to different network architectures. Each method has the following characteristics:

[0061] ① Host-centric networking method: relies on routing tables to determine the data transmission path, usually routing and forwarding through host addresses (such as IP addresses).

[0062] ② Content-centric networking method: It does not rely on routing tables for data transmission, but discovers and obtains data based on data content identification, supporting dynamic content distribution.

[0063] S2: Utilize data consumer nodes, data provider nodes, and intermediate nodes, combined with the drone routing table, to forward data request packets and data packets to complete data transmission.

[0064] A further embodiment is that the data transmission process includes:

[0065] Utilize the data consumer node to send a data request packet to the drone cluster. In the present invention, the data consumer node initiates a data acquisition request to the drone cluster, and selects different networking methods to initiate the data request based on the validity of the data content identifier in the routing table. A further implementation method is that the method for the data consumer node to send a data request packet to the drone cluster includes: ① When the data content identifier in the drone routing table is invalid (that is, there is no routing entry corresponding to the content identifier), the data consumer node initiates a data acquisition request in a broadcast manner; in this case, the networking method of the present invention tends to be a content-centric networking method. ② When the data content identifier in the drone routing table is valid (that is, there is a routing entry corresponding to the content identifier), the data consumer node initiates a data acquisition request in a unicast manner based on the next-hop host identifier in the routing entry. In this case, the networking method of the present invention tends to be a host-centric networking method.

[0066] The intermediate node receives the data request packet and forwards it to the data provider node, recording the data request path. Specifically, in the present invention, after the intermediate node located on the transmission path receives the data request packet initiated by the data consumer node, it records the request path of the data (i.e., the previous hop host identifier) ​​to the pending request table for use in data packet return. Subsequently, the intermediate node extracts the data content identifier from the data request packet, and selects an applicable networking method to forward the request packet based on the validity of the content identifier in the routing table: ① The data content identifier in the routing table is invalid, and the intermediate node forwards the data request packet in a broadcast manner. ② The data content identifier in the routing table is valid, and the intermediate node forwards the data request packet in a unicast manner based on the next hop host identifier in the routing entry.

[0067] The data provider node receives a data request packet and generates a data packet, which it returns to an upstream intermediate node in the data request path. Specifically, in the present invention, after receiving a data acquisition request packet from the network, the data provider node extracts the data content identifier in the data request packet and generates a data packet corresponding to the data content identifier. The data provider node then returns the data packet to the previous hop node of the data request packet (i.e., the upstream intermediate node in the data request path) using unicast.

[0068] The upstream intermediate node receives the returned data packet, and updates the drone routing table according to the data content identifier carried in the data packet, and returns the data packet to the data consumer node along the data request path to complete the transmission of a single data content. Specifically, in the present invention, after receiving the returned data packet, the intermediate node located on the data request path retrieves the next hop node of the data return path in the pending request table according to the data content identifier carried in the data packet, and forwards the data packet to the node in a unicast manner. This process is repeated until the data packet is finally returned to the data consumer node, completing the transmission of a single data content. In the process of data packet return, after the intermediate node and the data consumer node receive the data packet forwarded by the previous hop, they map the data content identifier and the downstream node in the data request path (i.e., the previous hop node of the data packet) to the routing table according to the source of the data packet and update it for use in subsequent data requests.

[0069] A further implementation method is that the data transmission process further includes:

[0070] All drone nodes monitor the messages sent by neighboring nodes in the wireless channel in real time. The messages include data request packets or data packets.

[0071] Determine whether the data request packet or data packet is sent to this drone node. If so, receive it and process it accordingly;

[0072] If the message is a data request packet and is received, the information carried in the message is parsed to obtain the status of the network environment around the drone node.

[0073] S3: Using networking experiments, establish the evolution path of networking methods under different business traffic loads; a further implementation method is that the evolution path of networking methods is based on the IHCR framework design of the hybrid networking method. Specifically, in the present invention, the AODV protocol is used to represent the host-centric networking method, and the NDNF protocol is used to represent the content-centric networking method. In addition, the IHCR protocol is used to represent the hybrid networking method, where (ts) indicates that the protocol uses a fixed routing failure time of t seconds.

[0074] S31: Characterizes the traffic load of drone cluster services.

[0075] Traffic load reflects the communication resource requirements of a drone swarm. In low-traffic load scenarios, the swarm's communication needs are relatively stable, with low traffic volumes, primarily used for simple data exchanges. In high-traffic load scenarios, however, the swarm's communication needs increase dramatically, with high traffic volumes, typically involving real-time video transmission or complex data processing tasks.

[0076] In the present invention, the service traffic load is determined by the number of service transmissions and the service request interval of the drone node:

[0077] ① Number of transmission pairs: When the number of business transmissions of the drone cluster is small, the traffic load is small; when the number of business transmissions of the cluster is large, the traffic load is large.

[0078] ② Service request interval: When the service request interval of the drone cluster is large, the traffic load is small; when the service request interval of the cluster is small, the traffic load is large.

[0079] S32: Comparison of the performance of various networking methods for drone swarms under different traffic loads;

[0080] The present invention uses the OMNeT++ discrete event simulation platform to conduct experiments and compare the transmission performance of the host-centric networking method AODV, the content-centric networking method NDNF and the hybrid networking method IHCR.

[0081] Figure 2 The performance of four networking methods under different service traffic loads is shown. In the figure, the horizontal axis in the two sub-graphs represents the number of transmissions and request intervals of drone cluster services, and the vertical axis represents the network's packet transmission success rate. Figure 2 (a) The traffic load is affected by changing the number of service transmissions. In this case, the service request interval is fixed at 300 milliseconds. Figure 2 (b) Influencing the traffic load by changing the service request interval. In this case, the number of service transmissions is fixed to 12.

[0082] For the NDNF (content-centric) and IHCR (interpretation of zero-second routing failure times) networking methods, as the number of service transmissions increases, the traffic load increases, and the packet transmission success rate decreases significantly. Similarly, as the service request interval decreases, the traffic load increases, and the packet transmission success rate also decreases significantly.

[0083] For the AODV (host-centric) and IHCR (3-second routing failure time) networking methods, as the number of service transmissions increases, the traffic load increases, while the packet transmission success rate remains essentially unchanged. However, as the service request interval decreases, the traffic load increases, and the packet transmission success rate shows a trend of first slightly increasing and then significantly decreasing.

[0084] Based on the above experimental results, the present invention summarizes the following rules:

[0085] ① Content-centric networking is not suitable for high-traffic load environments. Compared with host-centric networking, it performs poorly in scenarios with high service traffic loads, and the transmission success rate drops significantly.

[0086] ② Host-centric networking is not suitable for low-traffic load environments. Compared with content-centric networking, it performs poorly in low-traffic load scenarios due to node mobility and has a lower transmission success rate.

[0087] ③ The performance of the hybrid networking approach is affected by the routing expiration time setting under different traffic load environments. When the routing expiration time is 0 seconds, its performance is close to that of the content-centric networking approach; when the routing expiration time is 3 seconds, its performance is close to that of the host-centric networking approach.

[0088] S33: Summarize the evolution of networking methods based on cluster traffic load.

[0089] In the present invention, the networking method based on the evolution of cluster traffic load relies on the hybrid networking method IHCR protocol to achieve. According to the rule summarized in step S32, the networking method evolution route of the present invention is as follows: in the low-traffic (traffic flow is less than or equal to 10 kilobits per second) load scenario with moderate cluster topology dynamics (mobile speed) (30m / s-60m / s), the networking method of the present invention tends to adopt the content-centric networking idea. At this time, each node in the drone cluster should maintain a smaller routing failure time. As the traffic load increases, the content-centric networking method will gradually lose applicability, so the networking method should evolve to a host-centric networking method. For this reason, each node in the drone cluster should gradually increase the routing failure time to adapt to the high-traffic (traffic flow is greater than or equal to 40 kilobits per second) load environment.

[0090] S4: Utilize all drone nodes in the drone cluster to monitor the messages sent by neighboring nodes in the wireless channel during data transmission in real time, calculate the data request packet forwarding rate, and obtain the drone node-perceived cluster service traffic load.

[0091] Specifically, S41: defining a data request packet forwarding rate observation indicator.

[0092] In the present invention, the drone node U monitors the messages sent by neighboring nodes in the wireless channel in real time. If the monitored message is a broadcast message or a unicast message targeted at the node itself, it is successfully received. The drone node U determines whether to forward the message based on the content identification information of the received message. Furthermore, the drone node U records the number of data request packets successfully received and forwarded in the tth time slot as N t At the end of the tth time slot, each UAV node calculates the data request packet forwarding rate based on the number of data request packets forwarded in this time slot, which is recorded as:

[0093] S42: Verify the correlation between the observed indicators and the cluster business traffic.

[0094] The present invention verifies the correlation between the data request packet forwarding rate observation index proposed in step S41 and the cluster traffic load through simulation experiments.

[0095] Figure 3 This figure shows the data request packet forwarding rates observed for different drone nodes under different cluster traffic loads. In the figure, nodes 1-6 correspond to six randomly selected drone nodes. Figure 3 (a) The traffic load is affected by changing the number of service transmissions. In this case, the service request interval is fixed at 300 milliseconds. Figure 3 (b) The traffic load is affected by changing the service request rate (1 / service request interval), where the number of service transmissions is fixed at 12. Regardless of whether the number of service transmissions or the request rate is changed, the data request packet forwarding rate of each node increases as the traffic load increases, indicating a significant positive correlation between the data request packet forwarding rate and the cluster service traffic load.

[0096] S5: Utilizing drone nodes to perceive cluster traffic load and the evolution of networking methods, a routing failure time evolution strategy based on traffic load optimization is constructed. In this invention, each drone node U dynamically adjusts the routing failure time RET based on the hybrid networking method IHCR framework and the observed indicators of cluster traffic load in each time slot, thereby achieving the evolution of drone cluster networking methods.

[0097] A further implementation method is to construct a method for evolving a routing failure time strategy based on traffic load optimization, including:

[0098] S51: Use the sliding window estimation algorithm (SlidingWindow Estimation) to reduce the noise of the data request packet forwarding rate observed by the current drone node in the current time slot (t-th time slot) to obtain the actual value of the data request packet forwarding rate after denoising Where W represents the sliding window size. The sliding window estimation algorithm is a classic data smoothing technique. Its basic principle is to dynamically estimate the system state by taking a weighted average of the current observation value and historical observation values ​​within a certain range. In this invention, a sliding window mechanism is introduced to correct the error between the predicted value and the actual observed data in real time, achieving accurate estimation of the data request packet forwarding rate and thus accurately sensing the cluster service traffic load.

[0099] S52: Based on the actual values ​​of the data request packet forwarding rates of the current time slot and the adjacent time slots, calculate the rate of change of the data request packet forwarding rate; specifically, calculate the rate of change of the data request packet forwarding rate of the two most recent adjacent time slots (the t-1th time slot and the tth time slot)

[0100] The rate of change of the data request packet forwarding rate is used to reflect the trend of cluster service traffic load changes in consecutive time slots, providing a basis for adjusting the networking method of UAV nodes.

[0101] S53: Multiply the exponential iteration function of the data request packet forwarding rate change rate of the current drone node by the routing failure time of the current time slot to obtain the routing failure time of the next time slot; specifically, update the routing failure time of this node in the next time slot (t+1 time slot)

[0102]

[0103] in, Indicates the interval The projection function, δ represents the evolution step of routing failure time, RET max Indicates the maximum value of the route invalidation time.

[0104] At this point, the UAV node multiplies the routing failure time of the tth time slot by the exponential iterative function of the data request packet forwarding rate change rate, thereby obtaining the routing failure time of the t+1th time slot.

[0105] S54: Based on the hybrid networking method IHCR framework combined with the data request packet forwarding rate, the routing failure time is dynamically adjusted to complete the construction of the routing failure time evolution strategy based on traffic load optimization.

[0106] S6: Through the routing failure time evolution strategy based on traffic load optimization, the dynamic networking of the drone cluster is completed as the traffic load evolves.

[0107] Example 2

[0108] The present invention further provides a networking system based on the evolution of drone cluster traffic load, which is used to implement the method described in Example 1, including:

[0109] The drone cluster establishment module is used to build a drone cluster using drone nodes. The drone nodes in the drone cluster are divided into data consumer nodes, data provider nodes, and intermediate nodes according to their different business functions.

[0110] The data transmission module is used to utilize data consumer nodes, data provider nodes and intermediate nodes, combined with the drone routing table, to forward data request packets and data packets to complete data transmission;

[0111] The evolutionary route experiment module is used to establish the evolutionary route of networking methods under different business traffic loads through networking experiments;

[0112] The load calculation module is used to use all drone nodes in the drone cluster to monitor the messages sent by neighboring nodes in the wireless channel during data transmission in real time, calculate the data request packet forwarding rate, and obtain the drone node-perceived cluster service traffic load;

[0113] An evolutionary strategy building module is used to use drone nodes to perceive the cluster service traffic load and the evolution path of the networking method, and to build a routing failure time evolution strategy based on traffic load optimization;

[0114] The dynamic networking module is used to complete the dynamic networking of drone clusters as the traffic load evolves through a routing failure time evolution strategy based on traffic load optimization.

[0115] In a further embodiment, the data transmission module includes:

[0116] A data request packet sending unit, configured to send a data request packet to a drone cluster using a data consumer node;

[0117] The intermediate node forwarding unit is used for the intermediate node to receive the data request packet and forward it to the data provider node, and record the data request path;

[0118] A data packet generating unit, configured for the data provider node to receive a data request packet and generate a data packet to return to an upstream intermediate node in the data request path;

[0119] The data packet return unit is used for the upstream intermediate node to receive the returned data packet, and update the drone routing table according to the data content identifier carried in the data packet, and return the data packet to the data consumer node along the data request path to complete the transmission of a single data content.

[0120] The networking method of the present invention combines the concepts of the host-centric networking method and the content-centric networking method, introducing the routing table design of the host-centric networking method into the content-centric networking method, and realizing the mapping of the host identifier and the data content identifier. The method selects the appropriate networking method for data transmission based on whether the routing entries corresponding to the host identifier and the content identifier in the routing table are valid, thereby improving the flexibility of the network. Furthermore, based on the drone node's perception of the business traffic load, the method can optimize the networking strategy based on the evolution of the current environment to cope with different business load changes.

[0121] Example 3

[0122] The networking method presented in this paper was tested using the OMNeT++ discrete event simulation platform. The experimental setup was an 800m x 800m field, with a network consisting of 64 randomly wandering drone nodes. The communication protocol used was 802.11ac. The experimentally validated networking protocols included AODV, NDNF, and the proposed eRET-TL protocol (a traffic load-optimized routing failure time evolution strategy).

[0123] Figure 4 This figure illustrates a time-varying scenario of the service traffic load of a drone swarm. The horizontal axis represents network simulation time, and the vertical axis represents service traffic load (= number of service transmissions / request interval * packet size). In this scenario, the service traffic load increases gradually from 10 kilobits / second to 40 kilobits / second over 1000 seconds, simulating the gradual increase in the swarm's service traffic load.

[0124] Figure 5 Shown in Figure 4 The evolution results of the routing failure time of different drone nodes in the networking method of the present invention under the time-varying scenario of cluster business traffic load. In the figure, the vertical axis represents the routing failure time of the drone, and nodes 1-6 correspond to 6 randomly selected drone nodes. Figure 4 It can be found that as the cluster service traffic load increases, the routing failure time of the drone nodes gradually evolves from 0.1 seconds to 3 seconds. This result shows that the networking method of the present invention can achieve the evolution from content-centric to host-centric based on the cluster service traffic load, and adapt to the dynamic adjustment requirements of the cluster traffic load evolution.

[0125] Figure 6 Shown in Figure 4 Comparison of the transmission success rates of the networking method eRET-TL of the present invention and the two static networking methods in the scenario of time-varying cluster service traffic load. In the figure, eRET-TL represents the networking method of the present invention, AODV represents the host-centric networking method, and NDNF represents the content-centric networking method. As can be seen from the figure, in the stage where the cluster service traffic load is relatively small (0-600 seconds), the performance of the networking method eRET-TL of the present invention is similar to that of the content-centric NDNF, and shows a downward trend as the traffic load increases; while in the stage where the cluster service traffic load is relatively large (600-1000 seconds), the performance of the networking method eRET-TL of the present invention is similar to that of the host-centric AODV. Overall, as the cluster service traffic load increases, the transmission success rate performance of eRET-TL always performs optimally, indicating that the networking method of the present invention can adapt based on the evolution of cluster traffic load to ensure efficient data transmission.

[0126] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A networking method based on the evolution of UAV cluster traffic load, characterized by: include: Using drone nodes to build a drone cluster; wherein the drone nodes in the drone cluster are divided into data consumer nodes, data provider nodes and intermediate nodes according to different business functions; Utilizing the data consumer node, the data provider node, and the intermediate node, combined with the drone routing table, to forward the data request packet and the data packet, thereby completing data transmission; Use networking experiments to establish an evolutionary path for networking methods under different service traffic loads; All drone nodes in the drone cluster monitor the messages sent by neighboring nodes in the wireless channel during data transmission in real time, calculate the data request packet forwarding rate, and obtain the drone node-perceived cluster service traffic load; Using drone nodes to perceive cluster service traffic load and the evolution path of the networking method, a routing failure time evolution strategy based on traffic load optimization is constructed; Through the routing failure time evolution strategy based on traffic load optimization, the dynamic networking of drone clusters as the traffic load evolves is completed.

2. The method according to claim 1, characterized in that The data transmission process includes: Using the data consumer node to send a data request packet to the drone cluster; The intermediate node receives the data request packet and forwards it to the data provider node, and records the data request path; The data provider node receives the data request packet and generates a data packet and returns it to the upstream intermediate node in the data request path; The upstream intermediate node receives the returned data packet, updates the drone routing table according to the data content identifier carried in the data packet, and returns the data packet to the data consumer node along the data request path, completing the transmission of a single data content.

3. The method according to claim 2, characterized in that The method for the data consumer node to send a data request packet to the drone cluster includes: When the data content identifier in the drone routing table becomes invalid, the data consumer node initiates a data acquisition request in a broadcast manner; When the data content identifier in the drone routing table is valid, the data consumer node initiates a data acquisition request in a unicast manner according to the next-hop host identifier in the routing entry.

4. The method according to claim 1, wherein The data transmission process also includes: All drone nodes monitor messages sent by neighboring nodes in the wireless channel in real time, and the messages include data request packets or data packets; Determine whether the data request packet or the data packet is sent to the drone node, and if so, receive and process it accordingly; If the message is a data request packet and is received, the information carried in the message is parsed to obtain the status of the network environment around the drone node.

5. The method according to claim 1, wherein The evolution route of the networking method is based on the hybrid networking method IHCR framework design.

6. The method according to claim 5, characterized in that Methods for constructing a routing failure time evolution strategy based on traffic load optimization include: The sliding window estimation algorithm is used to reduce the noise of the data request packet forwarding rate observed by the current UAV node in the current time slot, and the actual value of the data request packet forwarding rate after denoising is obtained; Calculating a data request packet forwarding rate change rate based on actual values ​​of the data request packet forwarding rates of the current time slot and adjacent time slots; Multiply the exponential iteration function of the data request packet forwarding rate change rate of the current drone node by the routing failure time of the current time slot to obtain the routing failure time of the next time slot; Based on the hybrid networking method IHCR framework combined with the data request packet forwarding rate, the routing failure time is dynamically adjusted to complete the construction of the routing failure time evolution strategy based on traffic load optimization.

7. A networking system based on the evolution of UAV cluster traffic load, used to implement the method according to any one of claims 1 to 6, characterized in that: include: A drone cluster establishment module is used to build a drone cluster using drone nodes; wherein the drone nodes in the drone cluster are divided into data consumer nodes, data provider nodes and intermediate nodes according to different business functions; A data transmission module is used to utilize the data consumer node, the data provider node and the intermediate node, combined with the drone routing table, to forward the data request packet and the data packet to complete the data transmission; The evolutionary route experiment module is used to establish the evolutionary route of networking methods under different business traffic loads through networking experiments; A load calculation module is used to use all drone nodes in the drone cluster to monitor in real time the messages sent by neighboring nodes in the wireless channel during data transmission, calculate the data request packet forwarding rate, and obtain the drone node-perceived cluster service traffic load; An evolutionary strategy building module is used to use drone nodes to perceive the cluster service traffic load and the evolution path of the networking method to build a routing failure time evolution strategy based on traffic load optimization; The dynamic networking module is used to complete the dynamic networking of drone clusters as the traffic load evolves through a routing failure time evolution strategy based on traffic load optimization.

8. The system according to claim 7, characterized in that The data transmission module includes: a data request packet sending unit, configured to send a data request packet to the drone cluster using the data consumer node; An intermediate node forwarding unit, configured to receive the data request packet at the intermediate node and forward it to the data provider node, and record the data request path; A data packet generating unit, configured for the data provider node to receive the data request packet and generate a data packet to return to an upstream intermediate node in the data request path; The data packet return unit is used for the upstream intermediate node to receive the returned data packet, update the drone routing table according to the data content identifier carried in the data packet, return the data packet to the data consumer node along the data request path, and complete the transmission of a single data content.

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