A networking method and system based on evolution of unmanned aerial vehicle cluster traffic load

Abstract

This invention belongs to the field of network communication technology, specifically providing a networking method and system based on the evolution of UAV swarm traffic load. The method includes: constructing a UAV swarm using UAV nodes; using data consumer nodes, data provider nodes, and intermediate nodes, combined with the UAV routing table, to forward data request packets and data packets to complete data transmission; establishing network evolution paths under different service traffic loads through networking experiments; calculating the data request packet forwarding rate to obtain the service traffic load perceived by the UAV nodes; and constructing a routing failure time evolution strategy based on traffic load optimization using the service traffic load perceived by the UAV nodes and the network evolution path to complete the dynamic networking of the UAV swarm as traffic load evolves. The networking method of this invention can automatically adjust the networking strategy, maintaining high data transmission efficiency in scenarios where the traffic load of the UAV swarm changes over time.

Description

Technical Field

[0001] This invention belongs to the field of network communication technology, specifically relating to a networking method and system based on the evolution of traffic load in UAV swarms. Background Technology

[0002] In recent years, with the widespread application of drone swarms in low-altitude communication 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, especially in missions such as emergency communication, environmental monitoring, and military reconnaissance, where the demand for data transmission bandwidth and network reliability is growing.

[0003] Drone swarms offer flexible communication capabilities across a wide range, but their communication load exhibits significant time-varying characteristics depending on the task and environment. In low-traffic-load scenarios, drone swarms have lower communication needs, primarily for basic data transmission and simple task communication. However, in high-traffic-load scenarios, drone swarms need to handle a large number of data transmission tasks, such as real-time video transmission and environmental data acquisition, which places higher demands on network bandwidth and routing capabilities. Traditional content-centric networking methods do not rely on pre-maintained stable transmission paths, discovering data content through data request packet flooding. In high-traffic-load environments, this method easily creates bandwidth bottlenecks, leading to the loss of numerous data packets. In contrast, host-centric networking methods rely on control information exchange to maintain routing tables, making them more adaptable to high-traffic loads. However, in low-traffic-load environments, the historical routing information of such methods becomes outdated due to node mobility, leading to incorrect packet forwarding, packet loss, and reduced transmission efficiency.

[0004] In the context of unmanned aerial vehicle (UAV) self-organizing networks, how to evolve and adapt networking methods to ensure efficient data transmission when the traffic load of UAV swarm services changes over time is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] To enable drone swarms to maintain stable and efficient data transmission in environments with varying traffic loads, this invention discloses a networking method and system based on the evolution of drone swarm traffic load. By utilizing drones to observe the traffic load of swarm services, the system dynamically adjusts the route failure time in the routing table and selects a highly adaptable networking method for the current environment based on the validity of the routing entries. This provides the system with adaptive capabilities to varying traffic loads, effectively improving the stability and efficiency of data transmission.

[0006] A networking method based on the traffic load evolution of UAV swarms includes:

[0007] A drone cluster is constructed 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 their different business functions.

[0008] By utilizing the data consumer node, the data provider node, and the intermediate node, and in conjunction with the UAV routing table, data request packets and data packets are forwarded to complete data transmission;

[0009] Using network experiments, we established the evolution path of network topology methods under different service traffic loads.

[0010] By utilizing all drone nodes in the drone cluster to monitor the messages sent by neighboring nodes on the wireless channel during data transmission in real time, the data request packet forwarding rate is calculated to obtain the service traffic load of the drone node sensing cluster.

[0011] By utilizing the traffic load of the drone node sensing cluster and the evolution path of the networking method, a routing failure time evolution strategy based on traffic load optimization is constructed.

[0012] By employing a routing failure time evolution strategy based on traffic load optimization, dynamic networking of drone swarms is achieved as traffic load evolves.

[0013] Preferably, the data transmission process includes:

[0014] The data consumer node sends 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 to return to the upstream intermediate node in the data request path;

[0017] The upstream intermediate node receives the returned data packet, updates the UAV 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, thus completing the transmission of a single data content.

[0018] Preferably, the method by which the data consumer node sends 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 via broadcast.

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

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

[0022] All drone nodes are listening in real time to messages sent by neighboring nodes in the wireless channel, including data request packets or data packets;

[0023] Determine whether the data request packet or the data packet has been sent to this UAV node; if yes, receive and process 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 UAV node.

[0025] Preferably, the evolution path of the networking method is based on the design of 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 in the current time slot of the current UAV node, and the actual value of the data request packet forwarding rate after noise reduction is obtained.

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

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

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

[0031] The present invention also provides a networking system based on UAV swarm traffic load evolution for implementing the method, comprising:

[0032] The 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] The data transmission module is used to utilize the data consumer node, the data provider node, and the intermediate node, in conjunction with the UAV routing table, to forward data request packets and data packets to complete data transmission;

[0034] The evolution path experiment module is used to establish the evolution path of networking methods under different service traffic loads through networking experiments.

[0035] The load calculation module is used to monitor the messages sent by neighboring nodes on the wireless channel during data transmission in real time by all drone nodes in the drone cluster, calculate the data request packet forwarding rate, and obtain the service traffic load of the drone node sensing cluster.

[0036] An evolution strategy construction module is used to construct a route failure time evolution strategy based on traffic load optimization by utilizing the traffic load of the drone node sensing cluster and the evolution path of the networking method.

[0037] The dynamic networking module is used to complete the dynamic networking of the drone swarm 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 is used to send a data request packet to the drone cluster using the data consumer node;

[0040] An 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 to record the data request path;

[0041] A data packet generation unit is used for the data provider node to receive the data request packet and generate a data packet to return to the upstream intermediate node in the data request path;

[0042] The data packet return unit is used by the upstream intermediate node to receive the returned data packet, update the UAV 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.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] The networking method and system based on the evolution of UAV swarm traffic load designed in this invention can dynamically adapt to different scenarios of UAV swarm traffic load service demand, ensuring high efficiency of data transmission.

[0045] The routing failure time evolution strategy for traffic load optimization designed in this invention can obtain the current cluster service traffic load from the node perspective and calculate and update the routing failure time of the node based on the cluster service traffic load, so that the proposed networking method can evolve in a direction that adapts to the environment based on the cluster traffic load. Attached Figure Description

[0046] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart of a networking method based on the traffic load evolution of a drone swarm, according to an embodiment of the present invention.

[0048] Figure 2 These are experimental results of data packet transmission success rates under different cluster traffic loads for various networking methods using OMNeT++ in this embodiment of the invention; wherein, (a) is an experimental result of data packet transmission success rates under different service transmission numbers for various networking methods; and (b) is an experimental result of data packet transmission success rates under different service request intervals for various networking methods.

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

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

[0051] Figure 5 This is a graph showing the result of the change in the routing failure time of UAV nodes under a time-varying cluster traffic load scenario using OMNeT++ in an embodiment of the present invention.

[0052] Figure 6 This is an experimental result diagram of the data packet transmission success rate of the networking method of the present invention under a time-varying cluster traffic load scenario using OMNeT++, as demonstrated in this embodiment of the invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] Example 1

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

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

[0058] Specifically, data consumer nodes are nodes that request data content and act as initiators of transmission services; data provider nodes are nodes that provide data content and act as responders of transmission services; and intermediate nodes are other nodes located on the service transmission path, responsible for data forwarding and acting as relays of transmission services.

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

[0060] Depending on the network architecture, drone swarm networking methods can be divided into host-centric networking methods and content-centric networking methods, each with the following characteristics:

[0061] ① Host-centric networking method: Relies on routing tables to determine data transmission paths, and typically routes and forwards data through host addresses (such as IP addresses).

[0062] ② Content-centric networking approach: Data transmission does not rely on routing tables, but rather on data content identifiers for data discovery and retrieval, supporting dynamic content distribution.

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

[0064] A further implementation method involves the data transmission process including:

[0065] This invention utilizes data consumer nodes to send data request packets to a drone swarm. In this invention, the data consumer node initiates a data acquisition request to the drone swarm 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 involves the following methods for the data consumer node to send data request packets to the drone swarm: ① When the data content identifier in the drone routing table is invalid (i.e., there is no routing entry corresponding to the content identifier), the data consumer node initiates the data acquisition request via broadcast; in this case, the networking method of this invention tends towards a content-centric networking method. ② When the data content identifier in the drone routing table is valid (i.e., there is a routing entry corresponding to the content identifier), the data consumer node initiates the data acquisition request via unicast based on the next-hop host identifier in the routing entry. In this case, the networking method of this invention tends towards a host-centric networking method.

[0066] Intermediate nodes receive data request packets and forward them to data provider nodes, recording the data request path. Specifically, in this invention, after receiving a data request packet initiated by a data consumer node, the intermediate node located on the transmission path records the request path of the data (i.e., the identifier of the previous hop host) in a pending request table for use in data packet return. Subsequently, the intermediate node extracts the data content identifier from the data request packet and, based on the validity of the content identifier in the routing table, selects an appropriate networking method to forward the request packet: ① If the data content identifier in the routing table is invalid, the intermediate node forwards the data request packet using a broadcast method. ② If the data content identifier in the routing table is valid, the intermediate node forwards the data request packet using a unicast method based on the next-hop host identifier in the routing entry.

[0067] The data provider node receives the data request packet and generates a data packet to return to the upstream intermediate node in the data request path. Specifically, in this invention, after receiving the data acquisition request packet from the network, the data provider node extracts the data content identifier from the data request packet and generates a data packet corresponding to the data content identifier. Subsequently, the data provider node uses unicast to return the data packet to the previous hop node of the data request packet (i.e., the upstream intermediate node in the data request path).

[0068] The upstream intermediate node receives the returned data packet and updates the UAV routing table according to the data content identifier carried in the data packet. It then returns the data packet to the data consumer node along the data request path, completing a single data transmission. Specifically, in this invention, after receiving the returned data packet, the intermediate node located on the data request path retrieves the next-hop node for the data return path from the pending request table based on the data content identifier carried in the data packet, and forwards the data packet to that node using unicast. This process is repeated until the data packet finally returns to the data consumer node, completing a single data transmission. During the data packet return process, after receiving the data packet forwarded from the previous hop, the intermediate node and the data consumer node map the data content identifier to the downstream node (i.e., the previous hop node of the data packet) in the data request path according to the data packet's origin and update the routing table for use in subsequent data requests.

[0069] A further implementation method includes, during data transmission:

[0070] All drone nodes are listening in real time to messages sent by neighboring nodes in the wireless channel, including data request packets or data packets.

[0071] Determine whether a data request packet or data packet has been 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 this UAV node.

[0073] S3: Utilize networking experiments to establish an evolution path for networking methods under different service traffic loads; a further implementation method is that the evolution path is based on the hybrid networking method IHCR framework design. Specifically, in this 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. Furthermore, the IHCR protocol is used to represent the hybrid networking method, where (ts) represents the fixed routing failure time of t seconds used by this protocol.

[0074] S31: Characterizes the traffic load of drone swarm 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, mainly used for simple data exchange; while in high traffic load scenarios, the swarm's communication needs increase sharply with high traffic, typically involving real-time video transmission or complex data processing tasks.

[0076] In this 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 service transmissions of the drone cluster is small, the traffic load is small; when the number of service 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: Compare the performance of various networking methods for drone swarms under different traffic loads;

[0080] This invention uses the OMNeT++ discrete event simulation platform to conduct experiments and compare the transmission performance of host-centric networking method AODV, content-centric networking method NDNF, and 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 subgraphs represents the number of transmissions and the request interval of the drone swarm service, respectively, and the vertical axis represents the network's data packet transmission success rate. Figure 2 (a) The traffic load is affected by changing the number of service transmissions, with the service request interval fixed at 300 milliseconds. Figure 2 (b) Affect traffic load by changing the service request interval, where the number of service transmissions is fixed at 12.

[0082] For NDNF (Content-Centric Networking) and IHCR (Interruption-Based Routing) networking methods, as the number of service transmissions increases, the traffic load rises, and the packet transmission success rate decreases significantly. Similarly, as the interval between service requests shortens, the traffic load increases, and the packet transmission success rate also decreases significantly.

[0083] For AODV (host-centric) and IHCR (interruptible routing timeout) networking methods, as the number of service transmissions increases, the traffic load rises, but the packet transmission success rate remains basically unchanged. However, as the interval between service requests shortens, 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 patterns:

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

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

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

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

[0089] In this invention, the networking method based on cluster traffic load evolution is implemented using the hybrid networking method IHCR protocol. According to the pattern summarized in step S32, the evolution path of the networking method in this invention is as follows: In low-traffic (service traffic less than or equal to 10 kbps) load scenarios with moderate cluster topology dynamics (movement speed) (30m / s-60m / s), the networking method of this invention tends to adopt a content-centric networking approach. At this time, each node in the UAV cluster should maintain a short route failure time. As service traffic load increases, the content-centric networking method will gradually lose its applicability; therefore, the networking method should evolve towards a host-centric networking method. To this end, each node in the UAV cluster should gradually increase its route failure time to adapt to high-traffic (service traffic greater than or equal to 40 kbps) load environments.

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

[0091] Specifically, S41: Defines the data request packet forwarding rate observation metric.

[0092] In this invention, UAV node U monitors 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 this node, it is successfully received. UAV node U determines whether to forward the message based on the content identifier information of the received message. Furthermore, UAV node U records the number of data request packets successfully received and forwarded in the t-th time slot as N. t At the end of the t-th time slot, each drone node calculates its data request packet forwarding rate based on the number of data request packets forwarded in that time slot, denoted as:

[0093] S42: Verify the correlation between observed metrics and cluster service traffic.

[0094] This 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 The figure shows the data request packet forwarding rate observed by different drone nodes under different cluster service traffic loads. In the figure, nodes 1-6 correspond to 6 randomly selected drone nodes. Figure 3 (a) The traffic load is affected by changing the number of service transmissions, with the service request interval fixed at 300 milliseconds. Figure 3 (b) The traffic load was affected by changing the service request rate (1 / service request interval), with the number of service transmissions fixed at 12. Regardless of whether the change was made by the number of service transmissions or the request rate, the data request packet forwarding rate of each node increased with the increase in traffic load, indicating a significant positive correlation between the data request packet forwarding rate and the cluster service traffic load.

[0096] S5: Utilizing the cluster traffic load perception of UAV nodes and the evolution path of the networking method, a route failure time evolution strategy based on traffic load optimization is constructed. In this invention, each UAV node U, based on the hybrid networking method IHCR framework, dynamically adjusts the route failure time RET by combining the observed indicators of cluster traffic load in each time slot, thereby realizing the evolution of the UAV cluster networking method.

[0097] A further implementation method involves constructing a routing failure time evolution strategy based on traffic load optimization, which includes:

[0098] S51: Use the sliding window estimation algorithm to denoise the data request packet forwarding rate observed by the current UAV node in the current time slot (the t-th time slot), and obtain the actual value of the denoised data request packet forwarding rate. 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 and historical observations within a certain range. In this invention, the 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 data request packet forwarding rates of the current time slot and 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 rates of the two most recent adjacent time slots (time slot t-1 and time slot t).

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

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

[0102]

[0103] in, Indicates to interval The projection function, δ, represents the evolution step size of the route failure time, RET max This represents the maximum possible route failure time.

[0104] At this point, the drone node obtains the route failure time of the (t+1)th time slot by multiplying the exponential iteration function of the rate of change of the data request packet forwarding rate with the route failure time of the t-th time slot.

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

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

[0107] Example 2

[0108] This invention also provides a networking system based on the traffic load evolution of UAV swarms, used to implement the method described in Embodiment 1, including:

[0109] The drone swarm establishment module is used to build a drone swarm using drone nodes; the drone nodes in the drone swarm 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 forward data request packets and data packets by utilizing data consumer nodes, data provider nodes, and intermediate nodes, in conjunction with the UAV routing table, to complete data transmission;

[0111] The evolution path experiment module is used to establish the evolution path of networking methods under different service traffic loads through networking experiments.

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

[0113] The evolution strategy construction module is used to build a routing failure time evolution strategy based on traffic load optimization by utilizing the service traffic load of the drone node perception cluster and the evolution path of the networking method.

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

[0115] A further embodiment of the invention includes a data transmission module comprising:

[0116] The data request packet sending unit is used to send data request packets to the drone cluster using the data consumer node;

[0117] The intermediate node forwarding unit is used to receive data request packets from intermediate nodes and forward them to data provider nodes, and to record the data request path.

[0118] The data packet generation unit is used by the data provider node to receive the data request packet and generate a data packet to return to the upstream intermediate node in the data request path;

[0119] The data packet return unit is used by the upstream intermediate node to receive the returned data packet, update the UAV 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] This invention combines the ideas of host-centric and content-centric networking methods. It introduces the routing table design of the host-centric networking method into the content-centric networking method, achieving a mapping between host identifiers and data content identifiers. This method selects an appropriate networking method for data transmission based on the validity of the routing entries corresponding to the host identifier and content identifier in the routing table, thereby improving network flexibility. Furthermore, based on the drone node's perception of service traffic load, this method can optimize the networking strategy based on the current environmental evolution to cope with different service load changes.

[0121] Example 3

[0122] The networking method of this invention was tested using the OMNeT++ discrete event simulation platform. The experimental setup consisted of an 800m x 800m field, a network of 64 randomly walking UAV nodes, and the 802.11ac protocol. The networking protocols verified in the experiment included AODV, NDNF, and the eRET-TL protocol proposed in this invention (a routing failure time evolution strategy based on traffic load optimization).

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

[0124] Figure 5 Shown in Figure 4 In a time-varying cluster service traffic load scenario, the evolution of routing failure time for different UAV nodes in the networking method of this invention is shown. In the figure, the vertical axis represents the routing failure time of the UAVs, and nodes 1-6 correspond to six randomly selected UAV nodes. Combined with... Figure 4 It can be observed 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 demonstrates that the networking method of the present invention can achieve an evolution from content-centric to host-centric based on the cluster service traffic load, adapting to the dynamic adjustment requirements of the cluster traffic load evolution.

[0125] Figure 6 Shown in Figure 4 This paper compares the transmission success rate of the eRET-TL networking method of this invention with two static networking methods under time-varying cluster service traffic load scenarios. In the figure, eRET-TL represents the networking method of this invention, AODV represents the host-centric networking method, and NDNF represents the content-centric networking method. As can be seen from the figure, during the low cluster service traffic load phase (0-600 seconds), the performance of the eRET-TL networking method is similar to that of the content-centric NDNF, and decreases with increasing traffic load. However, during the high cluster service traffic load phase (600-1000 seconds), the performance of the eRET-TL networking method is similar to that of the host-centric AODV. Overall, as the cluster service traffic load increases, eRET-TL consistently demonstrates the best transmission success rate, indicating that the networking method of this invention can adaptively adapt to the evolution of cluster traffic load, ensuring efficient data transmission.

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

Claims

1. A networking method based on UAV swarm traffic load evolution, characterized in that, include: A drone cluster is constructed 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 their different business functions. By utilizing the data consumer node, the data provider node, and the intermediate node, and in conjunction with the UAV routing table, data request packets and data packets are forwarded to complete data transmission; Using network experiments, we established the evolution path of network topology methods under different service traffic loads. By utilizing all drone nodes in the drone cluster to monitor the messages sent by neighboring nodes on the wireless channel during data transmission in real time, the data request packet forwarding rate is calculated to obtain the service traffic load of the drone node sensing cluster. By utilizing the traffic load of the drone node sensing cluster and the evolution path of the networking method, a routing failure time evolution strategy based on traffic load optimization is constructed. By employing a routing failure time evolution strategy based on traffic load optimization, dynamic networking of drone swarms is achieved as traffic load evolves. The evolution path of the networking method is based on the design of the hybrid networking method IHCR framework; Methods for constructing routing failure time evolution strategies 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 in the current time slot of the current UAV node, and the actual value of the data request packet forwarding rate after noise reduction is obtained. Calculate the rate of change of data request packet forwarding rate based on the actual values ​​of the data request packet forwarding rate in the current time slot and adjacent time slots; Multiply the exponential iteration function of the rate of change of the data request packet forwarding rate of the current drone node by the route failure time of the current time slot to obtain the route failure time of the next time slot; Based on the hybrid networking method IHCR framework and combined with the data request packet forwarding rate, the route failure time is dynamically adjusted to complete the construction of a route failure time evolution strategy based on traffic load optimization.

2. The method according to claim 1, characterized in that, The data transmission process includes: The data consumer node sends 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 to return to the upstream intermediate node in the data request path; The upstream intermediate node receives the returned data packet, updates the UAV 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, thus completing the transmission of a single data content.

3. The method according to claim 2, characterized in that, The method by which the data consumer node sends 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 via broadcast. When the data content identifier in the drone routing table is valid, the data consumer node initiates a data acquisition request using unicast based on the next-hop host identifier in the routing entry.

4. The method according to claim 1, characterized in that, The data transmission process also includes: All drone nodes are listening in real time to messages sent by neighboring nodes in the wireless channel, including data request packets or data packets; Determine whether the data request packet or the data packet has been sent to this UAV node; if yes, receive and process 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 UAV node.

5. A networking system based on UAV swarm traffic load evolution, used to implement the method described in any one of claims 1-4, characterized in that, include: The 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. The data transmission module is used to utilize the data consumer node, the data provider node, and the intermediate node, in conjunction with the UAV routing table, to forward data request packets and data packets to complete data transmission; The evolution path experiment module is used to establish the evolution path of networking methods under different service traffic loads through networking experiments. The load calculation module is used to monitor the messages sent by neighboring nodes on the wireless channel during data transmission in real time by all drone nodes in the drone cluster, calculate the data request packet forwarding rate, and obtain the service traffic load of the drone node sensing cluster. An evolution strategy construction module is used to construct a route failure time evolution strategy based on traffic load optimization by utilizing the traffic load of the drone node sensing cluster and the evolution path of the networking method. The dynamic networking module is used to complete the dynamic networking of the drone swarm as the traffic load evolves through a routing failure time evolution strategy based on traffic load optimization.

6. The system according to claim 5, characterized in that, The data transmission module includes: A data request packet sending unit is used to send a data request packet to the drone cluster using the data consumer node; An 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 to record the data request path; A data packet generation unit is used for the data provider node to receive the data request packet and generate a data packet to return to the upstream intermediate node in the data request path; The data packet return unit is used by the upstream intermediate node to receive the returned data packet, update the UAV 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.

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