A distributed routing protocol method suitable for large-scale unmanned aerial vehicle cluster networks

Abstract

The present application relates to the technical field of unmanned aerial vehicle communication, in particular to a distributed routing protocol method suitable for large-scale unmanned aerial vehicle cluster network; the method introduces a greedy forwarding mode assisted by geographical position in each node in the unmanned aerial vehicle cluster, acquires the latest local topology structure information, quickly constructs an autonomous network, processes the received hello message based on the distributed routing of the local topology information, establishes a two-hop neighbor table for acquiring the local topology information, dynamically allocates resources through dynamic routing relay, and quickly allocates appropriate channel resources to each node; the ground base station selects the minimum distance path from the source node to the destination node based on the routing decision of the local information, and evaluates the performance of the distributed routing protocol under different node quantity and node moving speed scenarios, processes the received information, makes autonomous decisions, and realizes the intelligentization of the distributed routing execution protocol task.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and more specifically, to a distributed routing protocol method suitable for large-scale UAV swarm networks. Background Technology

[0002] In a communication scenario, there exists a swarm of drones and a fixed-location ground base station. When a node in the drone swarm needs to communicate with the ground base station, a fast and stable route needs to be established to transmit data. Each node in the drone swarm can obtain its own flight position information and the location of the fixed-location ground base station. Because node-dense FANETs are characterized by a high density of drone nodes compared to traditional drone ad hoc networks, the limited resources of the drone nodes can easily lead to network congestion when the number of packets transmitted in a node-dense FANET is too large.

[0003] Currently, the so-called drone swarm communication networking methods on the market include narrowband data radios, 4G, Wi-Fi, Zigbee, NB-IoT, LoRa, and wireless self-organizing networks, etc. 4G, 5G, and Wi-Fi, as broadband communication networking methods, cannot cover the airspace of drones at a certain altitude, resulting in poor actual communication performance. Furthermore, communication between drone nodes requires relaying through 4G base stations, leading to significant latency. The combination of 4G base stations and CPEs cannot meet the communication distance requirements of many drone swarms. Since the base station acts as the network center, channel resource allocation is entirely dependent on the base station, making it very vulnerable to damage and unsuitable for drone swarms. Commonly used wireless local area networks (WLANs) lack anti-interference technology and cannot cope with various complex electromagnetic environments. Wi-Fi standards are open, offering virtually no resistance to interception or hacking, making it susceptible to intrusion and compromising communication security. Clearly, these methods also fail to meet the requirements for drone swarm communication networking described above.

[0004] Narrowband data radios such as Zigbee, NB-IoT, LoRa, P900, and Xtend 900 have too low transmission rates, and their point-to-point and point-to-multipoint operation modes do not support the business logic of drone swarms. Clearly, except for ad hoc wireless networks, none of the above methods can meet the communication networking requirements of the drone swarm described above. Considering the high speed and limited battery capacity of drone nodes, how should the routing protocol of the drone communication network be designed to reduce the overhead of drone node route discovery and maintenance, while achieving good end-to-end latency and packet loss rate, and meeting the communication requirements of drone swarm networks in high-speed environments? In the process of optimizing the MAC and routing protocols in multi-hop ad hoc networks, ensuring that various services meet different requirements for transmission latency, transmission rate, and false alarm rate, and dynamically allocating channel resources and optimizing transmission routes under multi-parameter, multi-objective optimization conditions, is a challenging task. Especially in electronic warfare environments with deliberate interference, the degradation of communication link quality has a significant negative impact on the overall performance of UAV ad-hoc networks. This necessitates that the physical layer communication waveforms and the data link layer MAC protocols be able to withstand electromagnetic interference. Communication waveforms typically employ anti-interference techniques such as spread spectrum frequency hopping, direct-sequence spread spectrum, or intelligent frequency selection, along with robust error correction coding capabilities to ensure communication link quality. For ad-hoc network systems, broadband high-speed frequency hopping not only needs to address carrier synchronization, bit synchronization, and frame synchronization in traditional fully connected networks, but also needs to achieve network-wide time synchronization and frequency hopping pattern synchronization under multi-hop conditions, making technical implementation extremely challenging. Since frequent link failures incur substantial computational costs and data forwarding delays, severely impacting the communication performance of UAV swarms, the design of high-performance UAV swarm ad-hoc network routing protocols is crucial, with routing being considered as a key factor in stability. Summary of the Invention

[0005] This invention addresses the problems of insufficient transmission link stability, frequent routing protocol failures, and high route discovery overhead in existing routing protocols. It proposes a distributed routing protocol method suitable for large-scale UAV swarm networks. By introducing a geolocation-assisted greedy forwarding method at each node in the UAV swarm, the latest local topology information is obtained, enabling rapid construction of an autonomous network. Distributed routing based on local topology information processes received hello messages, establishes a two-hop neighbor table for acquiring local topology information, and dynamically allocates resources through dynamic routing relays to quickly assign suitable channel resources to each node. Ground base stations make routing decisions based on local information, selecting the shortest path from the source node to the destination node. The performance of the distributed routing protocol is evaluated under different node numbers and movement speeds. The received information is processed, and autonomous decision-making is achieved, realizing intelligent execution of the distributed routing protocol task.

[0006] The specific implementation details of this invention are as follows: A distributed routing protocol method suitable for large-scale UAV swarm networks specifically includes the following steps: Step 1: Introduce the geolocation-assisted greedy forwarding method to each node of the UAV swarm to obtain local topology information and establish a UAV self-organizing network communication model; Step 2: Transmit the local topology information to a fixed ground station outside the maximum communication distance via a line-of-sight link or satellite link to establish a multi-hop end-to-end route; Step 3: Periodically connect each node in the drone cluster to the channel and send the drone source node's hello message transmission frame to all neighbors within the communication range; based on the distributed routing of the local topology information, poll each node in a cyclic manner to establish the MAC protocol module; Step 4: Process the received hello messages according to the set hello message format, establish a two-hop neighbor table of local topology information, and add the node's own position information and one-hop neighbor table received from other nodes to the hello message to establish a node movement model. Step 5: Calculate the next-hop route and establish a communication protocol model based on the location information in the one-hop neighbor table and the two-hop neighbor table; Step 6: Update the one-hop neighbor table and the two-hop neighbor table containing the geographical location information of neighboring nodes, and compare them with the routing table of the current node to calculate the shortest path; Step 7: Using the stability of the link state between nodes as the routing parameter, establish the data transmission path data packet from the source node to the destination node, use the hop count as the path metric, adjust the channel resources in the network, and allocate the channel resources of each node through dynamic routing relay.

[0007] To better implement this invention, further, when the UAV node network layer transmits some or all of the video collected by the UAV photoelectric ball back to the ground control station via data link, step 2 specifically includes: Step 21: Transmit the platform parameters obtained from the UAV to the ground control station via data link; Step 22: Transmit the UAV control commands received from the ground control station to the UAV via data link; Step 23: Share platform parameters between drones via data link; Step 24: Based on the channel collision principle, when the transmission success rate drops to a set limit, obtain the sum of the number of pulses sent and received on the channel within the statistical time window at this time; Step 25: Data collected and processed from sensors between drones will be transmitted and shared via a data link.

[0008] To better implement this invention, furthermore, when the current operating frequency is interfered with, before periodically connecting each node in the UAV cluster to the channel as described in step 3, the following operations are performed: The channel status is determined based on the number of transmitted and received pulses monitored in the channel; the channel occupancy rate is compared with a data packet threshold; and the channel access is determined based on the comparison result. If the channel occupancy rate is lower than the data packet threshold, a data packet is transmitted; if the channel occupancy rate is higher than the data packet threshold, a backoff time is set according to the backoff algorithm to wait; after the backoff time is reached, the channel occupancy rate is detected again and compared with the data packet threshold to determine whether to connect to the channel; a channel occupancy statistic associated with the channel occupancy status is obtained through numerical calculation; the channel occupancy statistic is compared with the data packet threshold to determine whether to transmit a data packet; if the channel occupancy statistic is lower than the data packet threshold, the channel is connected and a data packet is transmitted.

[0009] To better implement the present invention, further, after establishing the node mobility model in step 5, the node mobility model restricts the parameters based on the link lifecycle to route selection, recalculates the route, searches for the request hop-by-hop or broadcast multicast according to the routing algorithm, sends a new frame in the next time slot, polls the access control end-to-end route, converts the broadcast channel into a point-to-point channel, and updates the routing table information at the UAV node.

[0010] To better realize the present invention, the communication protocol model further facilitates information transmission between different layers of the protocol stack, exchanges hello messages between nodes, establishes a one-hop neighbor table and a two-hop neighbor table at each node, calculates the nearest neighbor to the fixed ground station and forwards routes to the neighbor, allocates channel resources for newly joined nodes, and reclaims and redistributes the channel resources originally allocated to decommissioned nodes.

[0011] To better implement the present invention, the one-hop neighbor table further includes the IP address and IPv4 address of the one-hop neighbor node, the one-way connectivity status, the two-way connectivity status, the position coordinates (x, y, z) in the spatial rectangular coordinate system, the duration n*HELLO_INTERVAL / ms, symmetric neighbor type information, and asymmetric neighbor type information.

[0012] According to claim 1, a distributed routing protocol method applicable to large-scale UAV swarm networks is characterized in that the two-hop neighbor table includes the IP address of the two-hop neighbor node, the neighbor type of the two symmetrical neighbors, the geographical coordinates (x', y', z'), the maintenance time 2*HELLO_INTERVAL, the maintenance time 3*HELLO_INTERVAL, and the hop neighbors passed through.

[0013] To better implement the present invention, the hello message further includes the node's IP address, the node's IPv4 address, the node's geographical location information, the IP address of the first-hop neighbor, the IPv4 address of the first-hop neighbor, the geographical location information of the first-hop neighbor, the spatial Cartesian coordinate system of the first-hop neighbor, the time-to-live (TTL) of the first-hop neighbor, and the neighbor type of the first-hop neighbor.

[0014] To better implement this invention, when the source node S receives a hello message from the next-hop routing node A, it processes the hello message and performs the following operations: Step S1: Source node S polls each subordinate node in a round-robin manner, takes the next-hop routing node A of the hello message as a one-hop neighbor, adds the IP address and location coordinates of the next-hop routing node A to the one-hop neighbor table of source node S, updates the retention time to 3*HELLO_INTERVAL, and removes neighbors whose retention time is reduced to 0 as invalid neighbors from the neighbor list. Step S2: Update the one-hop neighbor list, verify the connection status with neighboring nodes, and search the one-hop neighbor table containing the next-hop routing node A in the hello message. If the source node S is listed in the one-hop neighbor table of the next-hop routing node A, the communication link between the next-hop routing node A and the source node S is bidirectionally connected, and the next-hop routing node A will regard the source node S as a symmetric neighbor. Step S3: Source node S adds the one-hop neighbor of next-hop routing node A as a two-hop neighbor to its two-hop neighbor list, updates the two-hop neighbor list, and calculates the next-hop route; each node in the drone swarm relays the data packet to another node and then forwards it to the destination node. The mobile ad hoc network (MANET) calculates the Euclidean distance between the symmetrical one-hop and two-hop neighbors and the fixed ground station.

[0015] Where, x n y n z n Let x be the spatial rectangular coordinates of the neighboring nodes. s y s z s The spatial rectangular coordinates of the fixed ground station; Step S4: Source node S calculates the nearest neighbor node B to the destination base station. If neighbor node B is a one-hop neighbor of source node S, neighbor node B is used as the next-hop node of source node S. If neighbor node B is a two-hop neighbor of S, then the one-hop neighbor node A that reaches neighbor node B via the next-hop route is used as the next-hop node of source node S, and the routing table is updated.

[0016] To better implement this invention, when a request to send data is received from a node in the UAV swarm, the specific operation of routing and forwarding is as follows: First, it is determined whether the destination ground station address is in the current node's one-hop neighbor table. If the destination ground station is a one-hop neighbor of the current node, the data packet is sent directly to the destination ground station. If communication between the current node and the destination ground station requires multiple hops, the data packet is transmitted to the next-hop node according to the routing table maintained by the current node, and forwarded node by node until the data packet reaches the destination ground station. When a relay node receives a data packet destined for the destination node, it determines whether a path exists in the routing table. If a path exists and the link between the path and the next-hop node is connected, the path is forwarded to the next-hop node according to the routing table. Otherwise, according to the geographical routing greedy forwarding strategy, the nearest neighbor node to the destination node is selected as the next-hop node until the destination UAV receives the data from the source UAV.

[0017] The present invention has the following beneficial effects: (1) By allocating channel resources within the network, the present invention enables UAVs to form a chain-like network through multi-hop routing relay forwarding, which greatly improves the distance of measurement and control and the scope of operation. Furthermore, through reasonable node deployment, it can adapt to complex terrain environments such as mountains and canyons and achieve beyond-line-of-sight communication.

[0018] (2) In this invention, the channel access control protocol of the UAV self-organizing network adjusts and allocates the channel resources in the network as needed. The channel resources are dynamically allocated, so that the channel resources in the network can be adjusted and allocated as needed, optimize the selection of transmission paths, ensure service QoS, and ensure that the MAC protocol and routing protocol can efficiently access and use the limited channel resources. At the same time, it is necessary to achieve the goals of low latency, high reliability and high throughput. The control overhead is used to improve the utilization rate of channel resources and effectively support the dynamic allocation of wireless channel resources under the conditions of late entry and dynamic exit of nodes.

[0019] (3) This invention obtains the latest local topology information through HELLO message interaction, minimizing the control overhead caused by network reconstruction. By utilizing the fact that each node only collects local topology information and selects the optimal next-hop node from neighboring nodes within two hops for forwarding, it does not need to obtain the global topology or wait for a response from the destination node. Compared to the relative movement speed, remaining energy, and packet loss rate, it can better cope with the highly dynamic UAV network structure, effectively improve the packet delivery rate, and make the cluster structure formed by the network more stable. It not only reduces the number of route breaks but also has lower latency and packet loss rate. Without increasing bandwidth and transmission power, it greatly improves spectrum utilization and network system capacity.

[0020] (4) The present invention is based on local information routing decision-making. It determines the next-hop route by calculating the Euclidean distance. It is applied in the UAV swarm network environment with highly dynamic topology changes to establish low-latency end-to-end routing. It has the advantages of large network scale, many relay hops, long communication distance, high transmission rate, stable communication network, wide operating frequency range, fast network entry / exit, fast route switching speed, support for high-speed node movement, communication security and confidentiality, clear IP voice, and strong anti-interference ability. It solves the problem of frequent route failure in existing routing protocols and reduces route discovery overhead.

[0021] (5) This invention reduces the routing overhead of clustered networks by introducing a geolocation-assisted greedy forwarding method. The end-to-end latency is reduced by approximately 2ms, improving the stability of the clustered structure. Compared with the traditional CBRP and OLSR protocols, the control overhead is reduced by approximately 30% and 50%, respectively, and the packet delivery rate is increased by approximately 10% and 15%, respectively.

[0022] (6) This invention selects the path with the shortest distance from the source node to the destination node, evaluates the performance of the distributed routing protocol under different node numbers and node movement speeds, designs a UAV self-organizing network communication model, a node movement model, and a communication protocol model, and combines the advantages of geographic location routing. For dynamically changing networks, it is not necessary to obtain global topology information; geographic location routing can be used to select the next-hop node. By utilizing the local topology information of the UAV, the transmission path between the source node and the destination node is constructed, and the number of hops is selected as the path metric, ensuring the connectivity of routing information in the network topology. In the absence of global routing information, it can dynamically, flexibly, and quickly form a network, and nodes can be added as needed to ensure end-to-end transmission performance of services. It does not require complex pre-planning of communication and has strong scalability.

[0023] (7) The present invention and the UAV self-organizing network model can perceive network changes in a timely manner, automatically configure or reconstruct the network, ensure real-time connectivity of data links, and have a high degree of autonomy, adaptability and intelligence. Attached Figure Description

[0024] Figure 1 This is a schematic block diagram of the forwarding structure of the distributed routing protocol method for large-scale UAV swarm networks provided by the present invention; Figure 2 This is a schematic diagram of the process for calculating the next-hop route provided by the present invention. Detailed Implementation

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments, and therefore should not be regarded as a limitation on the scope of protection. 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.

[0026] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set up," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0027] With the continuous development of drone technology, the types of drones are increasing and becoming more mature, with many drones emerging rapidly. As drone technology and communication capabilities continue to improve, drones are widely used in various scenarios such as emergency search and disaster relief. They are already widely applied in agriculture, industry, emergency response, and the military, playing an increasingly important role. Currently, the most common application is using a single drone for missions. Because this method involves single-machine operation, if the mission area is large, the drone needs to make multiple round trips or even relocations to fully cover the mission area, resulting in long processing times and relatively low efficiency. Furthermore, most drone communication networks use narrowband data transmission radios for one-way communication to distribute positioning data and control commands. Prescribed actions cannot be coordinated with environmental perception, and actions cannot be reasonably coordinated based on actual conditions, sometimes leading to tragic accidents due to loss of control. In some areas, terrain factors restrict communication between drones and ground control stations, preventing drones from reaching the operational area and affecting mission deployment. In mountainous areas, complex terrain and environments can cause data link blockages when using a single drone, affecting communication between the drone and the ground station. With increasing environmental and operational complexity, single-rack drones are no longer sufficient to meet mission requirements. Ad hoc drone networks, however, can integrate information acquired and analyzed by various drone nodes, achieving a more comprehensive and accurate understanding of the target and the multi-drone collaborative environment. Using an ad hoc drone network, each drone can act as a relay, eliminating data link obstruction issues. Compared to the problems associated with single drones, ad hoc drone networks offer numerous advantages in addressing these issues. For example, multiple drones can collaborate to perceive the mission environment, enabling rapid information sharing and transmission among them, and achieving large-scale monitoring of the mission area. When line-of-sight communication is in a blind spot, multi-drone relays can achieve seamless coverage. In environmental monitoring, ad hoc drone networks allow for adjustments in the number of drones in the network based on the mission requirements. For smaller monitoring areas, one or two drones can be used; for larger areas, multiple drones can be deployed, enabling flexible and efficient mission completion. Unmanned aerial vehicle (UAV) swarm systems do not rely on individual drones. When some drones leave or join the swarm, the entire swarm maintains a certain degree of integrity and can continue to perform its tasks. Therefore, wireless ad hoc networks, with their high degree of autonomy, resilience, and dynamic topology variability, have become a suitable technology for building UAV networks. Since collaborative tasks among UAV swarms require ad hoc networks, UAV swarms use Mobile Ad Hoc Networks (MA-NETs) to form networks, employing multi-hop wireless routing communication technology to extend the reconnaissance range and distance of UAVs and enhance network reliability and resilience. A UAV ad hoc network is a dynamic, self-organizing network system with an arbitrary, temporary, and autonomous network topology, composed of UAVs acting as network nodes.A drone ad hoc network system comprises a ground control station node and several drone nodes. Each drone, acting as a network node, is equipped with a Mobile Ad Hoc Network (MANET) communication module, possessing both routing and message forwarding capabilities, allowing for the formation of arbitrary network topologies via wireless connections. Each drone in this network functions as both a task node and a relay node: as a task node, it can execute mission objectives under the command and control of the ground control station or other drones; as a relay node, it participates in route maintenance and packet forwarding based on the network's routing policies and routing tables. In a drone ad hoc network (MANET), due to limitations in wireless transmission distance or terrain, routing between drones sometimes requires multiple network segments. As the ad hoc network grows large, maintaining a significant amount of routing information results in routing information messages filling the entire network. With the development of intelligent collaborative control technology, the number of nodes will increase; the communication distance between nodes within the cluster is mostly between 5 and 30 km, and the distance between the drone cluster and the ground control station is mostly between 50 and 100 km, with some requiring 200 to 300 km. During missions, drone nodes move at high speeds, leading to highly dynamic changes in the drone network topology. Nodes constantly join or leave the network, causing changes in their relative positions and resulting in a highly dynamic topology. Continuous access to ground service data places significant transmission pressure on the drone network. Consequently, short-term, bursty access to ground services can cause widespread congestion and even substantial data loss, degrading the network's communication performance. This necessitates addressing the design challenges of routing protocols for drone communication networks in short-term, bursty service scenarios. Furthermore, the high node density of drone swarms in ad hoc networks makes the application environment more complex than traditional mobile ad hoc networks. When the network node density is low, greedy forwarding strategies do not guarantee path establishment, and relying solely on location routing may result in the inability to find a complete route. When large-scale damage to ground communication facilities occurs due to natural disasters such as typhoons, earthquakes, and floods, ground terminals can only receive communication access services from temporarily deployed quasi-static drone communication networks. When ground terminals generate a large number of service demands, how to alleviate the congestion of the UAV network, reduce network latency, improve network throughput, and solve the characteristics of rapid movement and limited energy of UAV nodes through the routing protocol of the UAV communication network poses a great challenge to the network design.

[0028] Since the Transmission Control Protocol (TCP) was proposed, various versions of TCP have emerged, but without exception, they are all congestion control protocols based on window control. Window-based protocols require each terminal to maintain a window size. While the window size allows transmission, the number of data packets, or data sets, that have not yet received a response can only be transmitted into the channel after the terminal receives a response to the previous data packet. However, due to time-varying channel fading and environmental factors, the channel capacity of wireless channels is often time-varying. Therefore, TCP may not achieve ideal congestion control in UAV communication networks. Furthermore, with numerous traffic flows in the network, directly utilizing TCP when the traffic volume is large can lead to severe underutilization of resources at each terminal, which is difficult to implement in large-scale networks.

[0029] Currently, most UAV ad hoc networks adopt routing technology from mobile ad hoc networks. As a crucial development direction for the intelligence, swarming, and networking of UAVs, ad hoc networks have become a large-scale network for emerging UAV swarm application models in the information age. Ad hoc networks are characterized by being decentralized, multi-hop transmission, highly resilient, and rapidly deployable, making them the preferred communication mode for swarmed UAV networks. In ad hoc networks, the role of routing protocols is to establish effective communication paths for any node with communication needs. Swarmed UAVs can utilize ad hoc network technology to achieve high-speed information sharing among UAVs, collaboratively completing tasks in an autonomous and intelligent manner. The routing protocol of UAV ad hoc networks finds, establishes, and maintains paths for data transmission within the UAV ad hoc network and provides path decisions, forming the foundation for the coordinated completion of various tasks by the swarm and significantly influencing the performance of the UAV ad hoc network. The UAV swarm ad hoc network communication system is a self-organizing peer-to-peer wireless communication network composed of wireless communication nodes, characterized by distributed, decentralized multi-hop, multi-service, long-distance, and precise positioning capabilities, operating within a dynamically autonomous local area. Each UAV acts as a network node, maintaining communication with the ground station and ground network using a phased array antenna, while beamforming technology maintains communication between nodes. Each node in the UAV ad hoc network can communicate directly with neighboring nodes via direct links without a centralized infrastructure. Nodes unable to communicate directly can use relay nodes to achieve multi-hop transmission. Each node can not only communicate directly with the ground station but also act as a relay for communication between other UAVs or between other UAVs and the ground, and as a relay for communication between the UAV ground station and other equipment further away. This enables UAV swarms to achieve collaborative task planning and execution. Dynamic joining and leaving of swarm members is supported, meeting the collaborative communication needs of large-scale, highly dynamic UAV swarms, and is also suitable for solving network communication problems in various complex and special environments. In this networking mode, data routing and forwarding is one of the key technologies to ensure end-to-end transmission performance. To date, many routing protocols based on different strategies have emerged for mobile ad hoc networks, such as the plane-based AODV routing protocol, the source dynamic routing protocol DSR, and the cluster-based CBRP routing protocol and CGSR routing protocol. AODV has the disadvantage of high latency. While the DSR protocol has its own advantages, it also inevitably has some shortcomings.

[0030] During the route finding process of the DSR routing protocol, path selection is based solely on the shortest hop count, without considering path quality, node energy, or hotspots. For example, under the same conditions of processing capacity and bandwidth for all nodes, nodes in the network center will bear more packet forwarding tasks, leading to localized congestion, increased energy consumption, and shortened network lifetime, thus creating a bottleneck affecting the overall network throughput. This effect becomes more pronounced as network load continues to increase, potentially even causing network paralysis.

[0031] The three routing protocols mentioned above are unsuitable for swarm drone networks with a large number of nodes, high relative movement speeds, and limited resources. As node movement speed increases, network topology changes more rapidly, leading to increased control overhead and more route repair and reconstruction operations per unit time. In swarm drone networks, the large number of nodes and rapid topology changes are the primary cause of network performance degradation. The high relative movement speeds and frequent topology changes between nodes significantly increase packet loss rates, severely impacting network stability and performance. If the shortest path node moves quickly, experiences high congestion, or has insufficient energy, it can easily lead to longer network latency, unbalanced intra-network traffic, and network paralysis. The stability and reliability of routing protocols applied to drone swarms in ad hoc drone networks are primarily assessed using data delivery success rate, average end-to-end latency, and average throughput as key indicators.

[0032] Today, research on routing protocols for airborne communication networks has made significant progress. Based on different routing strategies, routing protocols can be categorized into proactive routing protocols, on-demand routing protocols, and hybrid routing protocols. Proactive routing protocols: Each network node maintains a routing table to other nodes. Regardless of whether a node sends data, it broadcasts control packets at regular intervals. Nodes continuously detect changes in network topology and link quality, updating their routing tables through periodic message exchanges. The routing table accurately reflects the network topology.

[0033] Such routing protocols mainly include DSDV, OLSR, FSR, and WRP, but these protocols are mostly designed for general-purpose systems. In other words, to adapt to various network scenarios, these protocols have become increasingly complex and bloated. Among them, the Node Sequence Distance Vector Protocol (DSDV) is the earliest self-organizing network routing protocol. It is an active routing protocol based on the distributed Bellan-Ford algorithm. Each node maintains a routing table, and each entry includes: the destination address, the metric for reaching the destination node (such as the minimum hop count), and the sequence number issued by the destination node. This sequence number is used to identify the age of the route, serving as the basis for route updates and packet forwarding, ensuring loop-free routing. However, maintaining the routing table incurs significant overhead, making it difficult to maintain.

[0034] OLSR (Optical Topology Control Response) is an improvement on traditional Link-State (LS) protocols. Through HELLO message exchanges, it selects a relay forwarding node (MPR) that covers all strictly two-hop neighbors to forward TC (Topology Control) information. Each node obtains global topology information through TC message exchanges and establishes a routing table to each destination node. Compared to traditional link-state algorithms, OLSR avoids the flooding of topology control information, reducing the overhead required for each node to obtain global topology information. While OLSR offers the best performance in terms of throughput, packet loss rate, and latency jitter, it suffers from increased latency. Proactive routing protocols have shorter end-to-end network delays. However, because UAV swarm networks exhibit more dramatic topology changes compared to traditional ad hoc networks, leading to frequent route failures, proactive routing protocols are difficult to apply to future UAV ad hoc networks.

[0035] On-demand routing protocols: In contrast to proactive routing, on-demand routing only looks up and creates routes when it has a service demand; it does not actively explore routes when there is no service demand. Typical protocols include DSR, AODV, TOR A, and AeroRP. The AODV protocol uses an intermediate node acknowledgment mechanism, which can reduce route discovery waiting time and the number of RREQ broadcasts. It also has a local link repair mechanism, which can reduce data transmission delays caused by link failures and improve packet arrival rate. On-demand routing has lower network overhead because it does not need to periodically broadcast packets or maintain routing tables for all nodes. However, the need for route discovery increases the end-to-end latency from the source node to the destination node.

[0036] Hybrid routing protocols combine the characteristics of active and passive routing, such as ZRP and CBRP. Through network logical hierarchy, they can combine the advantages of proactive and on-demand routing protocols. Nodes in the network are divided into different "clusters" based on node location or node relationships. Each cluster selects a cluster head. Proactive routing algorithms are used between nodes within a cluster, while on-demand routing algorithms are used for inter-cluster communication. This combines the advantages of proactive and on-demand routing protocols while avoiding the huge overhead required for each node to maintain a global routing table. However, the highly dynamic topology of UAV MANETs leads to frequent cluster head failures, increased cluster maintenance costs, and routing failures. Unlike the well-established terrestrial communication networks, the characteristics of aerial networks present new challenges to end-to-end data transmission performance.

[0037] (1) Unlike network forms where routing nodes are fixed or relatively static, the high-speed mobility of UAV swarms leads to short-term contact between UAVs and frequent changes in link status. The high-speed mobility of network nodes and the uncertainty of the airborne wireless communication environment will exacerbate the frequent changes in the network topology of high-density UAVs, making it impossible for airborne wireless communication links to interconnect in real time, thereby increasing the possibility of routing failure; (2) Research by Kevin Peters et al. shows that when UAV swarm networks have high mobility, most traditional UAV swarm network protocols perform poorly in terms of packet arrival rate, end-to-end latency, and overhead. The high-speed movement of UAV nodes causes frequent link failures between nodes, making traditional UAV swarm network protocols unsuitable for such a highly dynamic environment. AeroRP has some advantages, but due to the lack of local topology information, the performance of the relatively optimal next hop calculated from one-hop neighbor information may still differ from the globally optimal value. This highlights the shortcomings of existing UAV swarm network routing protocols.

[0038] Example 1: This embodiment proposes a distributed routing protocol method suitable for large-scale UAV swarm networks. The distributed routing includes: a multi-channel access control (MAC) protocol module with multiple access channels in the data link layer of the UAV node wireless ad hoc network (MANET) with both wireless communication and dynamic routing functions; multi-hop end-to-end routing; a UAV ad hoc network communication model; a node mobility model; and a communication protocol model, which are organized to form a multi-hop communication network for the UAV node network layer and the link layer nodes in multi-flying single-control or multi-control working modes. Each node in the drone swarm interacts with HELLO messages via a broadcast link, introducing a geolocation-assisted greedy forwarding method to obtain its own flight position information and the latest local topology information in the network. It then transmits information to a fixed ground station whose location is known beyond its maximum communication distance, rapidly constructing a distributed, decentralized, self-organizing, self-recovering, and dynamically autonomous network. Each drone communicates with the ground station via line-of-sight links or satellite links. Each node in the drone swarm periodically accesses the channel and sends HELLO message frames from its source node to all neighbors within its communication range. The MAC protocol module uses distributed routing based on local topology information, polling each node in a round-robin manner to obtain its own position information. End-to-end routing uses a new HELLO message format to process received HELLO messages and establish a two-hop neighbor table for obtaining local topology information. In the node mobility model, each node receives HELLO messages from other nodes, updates its one-hop and two-hop neighbor lists, and adds its own position information and one-hop neighbor table to the HELLO message in the HELLO message format. The communication protocol model calculates the next-hop route based on the location information in the one-hop and two-hop neighbor tables, calculates the Euclidean distance between its one-hop and two-hop neighbor nodes and the ground fixed station, and determines the next-hop route based on the neighbor node with the smallest Euclidean distance. Each node periodically updates its routing table, updates the one-hop and two-hop neighbor lists containing the geographical location information of neighbor nodes, compares them with the routing table of the node itself, calculates the shortest path, uses the stability of the link state between nodes as the routing parameter, establishes the data transmission path of data packets from the source node to the destination node, selects the number of hops as the path metric, adjusts and allocates channel resources in the network as needed, and dynamically allocates resources through dynamic routing relay to quickly allocate appropriate channel resources to each node. After each node receives a HELLO message from other nodes within its communication range, each UAV communicates with the ground station via a line-of-sight link or satellite link. It then performs autonomous flight according to onboard software or executes tasks according to remote control commands sent by the ground station. A distributed algorithm coordinates communication among the UAVs to achieve collaborative tasks. The ground base station makes routing decisions based on local information, selecting the shortest path from the source node to the destination node. The performance of the distributed routing protocol is evaluated under different node numbers and movement speeds. A UAV ad hoc network communication model, a node mobility model, and a communication protocol model are designed. The UAV ad hoc network communication model promptly senses network changes and automatically configures or reconstructs the network. The node mobility model allocates channel resources on demand based on the high-speed mobility and distributed dynamics of the UAV node network layer and link layer nodes to achieve information sharing. The communication protocol model selects hop count as the path metric, processes the received information, and makes autonomous decisions to achieve intelligent execution of distributed routing protocol tasks.

[0039] Working Principle: This embodiment addresses the characteristics of a large number of nodes and rapid topology changes in a swarm of drones. Each node in the drone swarm acquires its own flight position information within the network. Each node in the drone ad hoc network possesses both wireless communication and dynamic routing capabilities, enabling it to perform traditional point-to-point communication and telemetry functions as well as relay forwarding of data packets along optimal paths. Channel resources within the network are adjusted and allocated as needed. The multi-hop routing relay forwarding function allows drones to form chain-like networks, significantly increasing the telemetry and control distance and operational range. Through reasonable node deployment, it can adapt to complex terrain environments such as mountains and canyons, achieving beyond-line-of-sight communication. The channel access control protocol (MAC protocol) of the drone ad hoc network adjusts and allocates channel resources within the network as needed. This dynamic allocation of channel resources allows for the adjustment and allocation of channel resources within the network as required, optimizing transmission paths and ensuring service QoS. The MAC and routing protocols efficiently access and utilize limited channel resources while achieving low latency, high reliability, and high throughput. Control overhead is used to improve the utilization rate of channel resources, effectively supporting the dynamic allocation of wireless channel resources under conditions of late node entry and dynamic exit. This allows nodes to dynamically join and leave the network, temporarily add or remove them, use multi-hop relays, move freely, and dynamically change the network topology. No pre-planning of the communication network is required, making it ideal for temporarily scheduling the number of drones to meet practical application needs. Nodes add their location information and one-hop neighbor table to a HELLO message format and broadcast it to neighboring nodes. The HELLO message is only sent to the node's one-hop neighbors and is not forwarded. The designed routing strategy has low route discovery overhead. Each node in the drone network only needs to periodically broadcast HELLO messages to its neighbors. Nodes do not forward received HELLO messages but select relay nodes (MPR nodes) to forward broadcast data packets. The optimal route link is selected based on the hop count for data transmission. Other non-relay nodes only receive data packets and do not forward them, thus reducing the flooding range of control packets. This method allows the movement pattern of a node in the next moment to be obtained based on the movement pattern of the node in the previous moment. Individual drones complement each other's strengths and cooperate to form an organic whole, achieving better task execution results than individual drones, and avoiding the overhead of some routing strategies that continuously forward topology control messages to obtain the global topology for each node. It also avoids data link loss due to complex terrain or mountain obstructions. In large-scale ad hoc networks, compared to traditional ad hoc network routing protocols, it can complete large-area monitoring tasks, and multiple drones can be used to monitor from different angles when problems are detected; it can achieve good optimization results in shortening network convergence time and reducing routing overhead. Even if one drone malfunctions, it will not affect the execution of the mission.

[0040] This embodiment utilizes HELLO messages received from other nodes. Each node processes these messages, updates its one-hop and two-hop neighbor lists (including the geographical location information of neighboring nodes), and obtains the latest local topology information through HELLO message interaction. This minimizes control overhead caused by network reconfiguration. By collecting only local topology information from its neighbors within two hops, and selecting the optimal next-hop node for forwarding, it avoids the need for global topology information and waiting for a response from the destination node. Compared to other methods, it better handles the highly dynamic UAV network structure, effectively improving packet delivery rate and making the network cluster structure more stable. This not only reduces the number of route breaks but also results in lower latency and packet loss. Without increasing bandwidth or transmission power, it significantly improves spectrum utilization and network system capacity.

[0041] This embodiment uses local information-based routing decision-making. Each UAV node calculates the Euclidean distance between its one-hop and two-hop neighbor nodes and the ground fixed station based on the location information in its one-hop and two-hop neighbor tables. The next-hop route is determined based on the neighbor node with the smallest Euclidean distance. Each node updates its routing table periodically. Applied to UAV swarm network environments with highly dynamic topologies, this method establishes low-latency end-to-end routing. It features large network scale, high relay hop count, long communication distance, high transmission rate, stable communication network, wide operating frequency range, fast network entry / exit, fast route switching speed, support for high-speed node movement, secure and confidential communication, clear IP voice communication, and strong anti-interference capabilities. It solves the problem of frequent route failures in existing routing protocols and reduces route discovery overhead. A geolocation-assisted greedy forwarding method is introduced to reduce the routing overhead of the clustered network. Simulation results show that the end-to-end latency is reduced by approximately 2ms, improving the stability of the cluster structure. Compared with the traditional CBRP and OLSR protocols, the GACB protocol reduces control overhead by approximately 30% and 50%, respectively, and increases packet delivery rate by approximately 10% and 15%, respectively. This embodiment uses a ground base station to make routing decisions based on local information, selecting the path with the shortest distance from the source node to the destination node. The performance of the distributed routing protocol is evaluated under different node numbers and movement speeds. A UAV ad hoc network communication model, node movement model, and communication protocol model are designed. Leveraging the advantages of geographic location routing, for dynamically changing networks, global topology information is not required; geographic location routing can be used to select the next-hop node. The transmission path between the source and destination nodes is constructed using the UAV's local topology information, and the hop count is chosen as the path metric, ensuring the connectivity of routing information in the network topology. Even when global routing information is unavailable, dynamic, flexible, and rapid network formation is possible, with nodes added as needed to guarantee end-to-end service transmission performance. No complex pre-planning of communication is required, resulting in strong scalability.

[0042] This embodiment, with its UAV self-organizing network model, promptly senses network changes, automatically configures or reconstructs the network, and ensures real-time data link connectivity. It exhibits a high degree of autonomy, adaptability, and intelligence. Because the UAV self-organizing network communication model eliminates the simple chain structure between all UAV clusters, even if any link in the chain fails, the entire UAV system will not be paralyzed. This significantly improves the UAV system's anti-interference capability, supporting temporary UAV joining and leaving, multi-hop automatic routing relay, dynamic network topology changes, rate adaptation, and on-demand bandwidth allocation. It can stably support 64-node communication networks and is suitable for long-distance, large-scale cluster networking applications ranging from 30 to 200 km. It is highly suitable for collaborative communication networking of UAV clusters, and therefore, particularly for UAV self-organizing networks with dynamic topology changes and node positioning capabilities. It is an end-to-end routing and forwarding protocol suitable for autonomous UAV cluster networking. It can be widely applied in agriculture, inspection, security, rescue, and other fields.

[0043] Example 2: This embodiment is based on the above embodiment 1, such as... Figure 1 , Figure 2 As shown, a specific embodiment will be described in detail.

[0044] like Figure 1 As shown, the distributed routing includes: a multi-channel access control (MAC) protocol module in the data link layer of the MANET wireless ad hoc network of UAV nodes, which has both wireless communication and dynamic routing functions; multi-hop end-to-end routing; UAV ad hoc network communication model; node mobility model; and communication protocol model, which are organized to form a multi-hop communication network of UAV node network layer and link layer nodes with multi-flying single-control or multi-control working modes. Each node in the drone swarm interacts with HELLO messages via a broadcast link, introducing a geolocation-assisted greedy forwarding method to obtain its own flight position information and the latest local topology information in the network. It then transmits information to a fixed ground station whose location is known beyond its maximum communication distance, rapidly constructing a distributed, decentralized, self-organizing, self-recovering, and dynamically autonomous network. Each drone communicates with the ground station via line-of-sight links or satellite links. Each node in the drone swarm periodically accesses the channel and sends HELLO message frames from its source node to all neighbors within its communication range. The MAC protocol module uses distributed routing based on local topology information, polling each node in a round-robin manner to obtain its own position information. End-to-end routing uses a new HELLO message format to process received HELLO messages and establish a two-hop neighbor table for obtaining local topology information. In the node mobility model, each node receives HELLO messages from other nodes, updates its one-hop and two-hop neighbor lists, and adds its own position information and one-hop neighbor table to the HELLO message in the HELLO message format. The communication protocol model calculates the next-hop route based on the location information in the one-hop and two-hop neighbor tables, calculates the Euclidean distance between its one-hop and two-hop neighbor nodes and the ground fixed station, and determines the next-hop route based on the neighbor node with the smallest Euclidean distance. Each node periodically updates its routing table, updates the one-hop and two-hop neighbor lists containing the geographical location information of neighbor nodes, compares them with the routing table of the node itself, calculates the shortest path, uses the stability of the link state between nodes as the routing parameter, establishes the data transmission path of data packets from the source node to the destination node, selects the number of hops as the path metric, adjusts and allocates channel resources in the network as needed, and dynamically allocates resources through dynamic routing relay to quickly allocate appropriate channel resources to each node. After each node receives a HELLO message from other nodes within its communication range, each UAV communicates with the ground station via a line-of-sight link or satellite link. It then performs autonomous flight according to onboard software or executes tasks according to remote control commands sent by the ground station. A distributed algorithm coordinates communication among the UAVs to achieve collaborative tasks. The ground base station makes routing decisions based on local information, selecting the shortest path from the source node to the destination node. The performance of the distributed routing protocol is evaluated under different node numbers and movement speeds. A UAV ad hoc network communication model, a node mobility model, and a communication protocol model are designed. The UAV ad hoc network communication model promptly senses network changes and automatically configures or reconstructs the network. The node mobility model allocates channel resources on demand based on the high-speed mobility and distributed dynamics of the UAV node network layer and link layer nodes to achieve information sharing. The communication protocol model selects hop count as the path metric, processes the received information, and makes autonomous decisions to achieve intelligent execution of distributed routing protocol tasks.

[0045] The UAV node network layer transmits some or all of the video collected by the UAV's electro-optical spheres back to the ground control station via data link. All UAVs transmit platform parameters such as altitude, latitude and longitude, speed, fuel level, battery level, and attitude to the ground control station via data link. The ground station transmits control commands to the UAVs via data link. UAVs share certain platform parameters via data link for formation control, group coordination, and collision avoidance. Based on the channel collision principle, there is no priority control for each node; packets are sent as they arrive. As network traffic is gradually increased, when the packet transmission success rate drops to near the 99% threshold, the sum of the number of pulses sent and received on the channel within this statistical time window is obtained. UAVs then share key data collected and processed by sensors via data link, enabling collaborative perception, collaborative processing, target task allocation, and collaborative operation.

[0046] The link-layer communication protocol model is a distributed routing protocol based on local topology information. Each node establishes a one-hop and two-hop neighbor list, calculates the nearest neighbor to the fixed ground station, and forwards routes to it. When the current operating frequency is interfered with, it adapts to frequency selection to handle hostile or unintentional interference, quickly selects a backup path, and automatically switches to an uninterrupted frequency for communication. It performs real-time interference recognition and communication quality assessment on preset candidate frequencies. The carrier frequency rapidly hops according to a preset frequency hopping pattern. It integrates QoS and congestion control technologies from the transport layer, continuously monitors channel occupancy, listens for the number of pulses in the channel, and periodically updates the number of transmitted and received pulses. Based on the number and characteristics of the transmitted and received pulses monitored in the channel, it determines the various possible channel states and compares the channel occupancy rate with a data packet threshold to determine whether a service packet should access the channel. If the channel occupancy rate is lower than the data packet threshold, the data packet is sent; if the channel occupancy rate is higher than the data packet threshold, a backoff time is set according to the backoff algorithm. The system employs a backoff waiting mechanism. After the backoff time is reached, the channel occupancy rate is checked again and compared with a threshold to determine whether to access the channel. A channel occupancy statistic is calculated and compared with a data packet threshold to determine whether the packet can be sent. When a network layer data packet arrives at the MAC layer, it is first inserted into the corresponding queue according to its priority. Then, the highest priority data packet is retrieved from the queue, and the current channel occupancy rate is compared with the threshold corresponding to this data packet's priority. If the channel occupancy rate is less than the threshold, the data packet is sent. If the channel occupancy rate is greater than the threshold, the data packet will backoff for a period of time according to its priority and channel occupancy. During the data packet backoff time, if a lower priority data packet arrives, it will queue in the queue for transmission. After the backoff time is reached, the channel occupancy rate is checked again, and the above process is repeated until the higher priority data packet has been sent. In the event of a routing interruption, a backup path is quickly selected, automatically avoiding various interferences. This adaptive frequency selection anti-interference function can improve the adaptability of the communication network system to work in complex electromagnetic environments. It has strong anti-interference and anti-interception capabilities, ensuring the reliability of information transmission. Furthermore, it can quickly select a backup path in the event of a routing outage, meeting the requirement for rapid routing response and preventing network congestion under overload conditions.

[0047] The node mobility model restricts route selection based on link lifecycle parameters, restarts the route calculation process, searches for requests hop-by-hop or via broadcast multicast according to the routing algorithm, sends new frames in the next time slot, polls access control for end-to-end routes, converts the broadcast channel to a point-to-point channel, and updates the routing table information at the drone nodes. This significantly reduces the routing impact of rapidly changing drone swarm topology designs.

[0048] When swarmed drones perform missions, the drone swarm network is a highly dynamic network with rapidly changing topology and constant node additions and removals. Drone nodes transmit information to a fixed ground station located beyond their maximum communication distance, establishing multi-hop end-to-end routes. Routing strategies are needed to handle this highly dynamic network, and considering the limited energy of drone nodes, route discovery overhead must be minimized. To achieve this, a distributed routing protocol based on local topology information is used. Through information transmission between layers of the protocol stack, HELLO message exchanges between nodes are completed with extremely low latency. Each node establishes one-hop and two-hop neighbor lists, calculates the nearest neighbor to the fixed ground station and forwards routes to it, allocates channel resources to newly joined nodes, allocates more channel resources to nodes requiring big data transmission, and reclaims and redistributes the channel resources previously allocated to decommissioned nodes.

[0049] During each HELLO message cycle, each drone node in the network processes the HELLO messages received from neighboring nodes, writes its own location information and one-hop neighbor table into the HELLO message, broadcasts it to neighboring nodes, and updates its own one-hop, two-hop neighbor tables and routing tables; this reduces the frequency of routing interruptions and improves routing stability.

[0050] The one-hop neighbor table contains the IP addresses and IPv4 addresses of the neighboring nodes, the connection status of one-way and two-way connectivity, the location coordinates (x, y, z) in the spatial rectangular coordinate system (in km), the maintenance time n*HELLO_INTERVAL (in ms), and information on symmetric and asymmetric neighbor types. Each node in the UAV network can obtain its own location information and maintain its own one-hop neighbor table based on the received HELLO messages. HELLO_INTERVAL is a set value representing the time interval at which each node sends HELLO messages.

[0051] The two-hop neighbor table contains the IP addresses of the two-hop neighbors, the neighbor types of the two symmetrical neighbors, their geographical coordinates (x, y, z), the duration of 2*HELLO_INTERVAL, 3*HELLO_INTERVAL, and the hop neighbors along the route. It obtains the data packets to be sent and their target nodes. Combining the network topology map and the target nodes, the geographical location assists in discovering the shortest path. It obtains the shortest path from each neighbor node to the target node and the neighbor nodes connecting to the target node, so as to shorten the latency caused by route discovery and route reconstruction.

[0052] The routing table contains the IP address of the next-hop node and the route's duration of 1 * HELLO_INTERVAL. Once the duration reaches 0, the node recalculates the next-hop route.

[0053] like Figure 2 In step S1, to obtain local topology information using HELLO message interaction, a proprietary HELLO message format is set. The created HELLO message contains the node's own IP address (IPv4 address), geographical location information, and the IP address and geographical location information of its one-hop neighbor (IPv4 address, geographical location information: spatial Cartesian coordinates x, y, z, unit km), time-to-live (Nbrx_TTL), and neighbor type (Nbrx_symetric, whether bidirectional communication is possible). Each node periodically sends HELLO messages, which are only sent to the nodes adjacent to each node's one-hop neighbor; HELLO messages are not forwarded. When the next-hop routing node A wants to send a HELLO message, it includes its own IP address (192.168.0.4), location information (x, y, z), and a list of one-hop neighbors in the HELLO message.

[0054] Step S2: Process the HELLO message. All nodes in the network periodically send HELLO messages. When the source node S receives a HELLO message from the next-hop routing node A, it processes the HELLO message and performs the following operations: 1) Source node S polls each subordinate node in a round-robin fashion, treating the next-hop routing node A that sent the HELLO message as a one-hop neighbor. It adds the IP address (192.168.0.4) and location information (x=5.2, y=4.2, z=5.7, in km) of next-hop routing node A to its own one-hop neighbor table, updating the retention time to 3*HELLO_INTERVAL. Neighbors whose retention time decreases to 0 are considered invalid neighbors and deleted from the neighbor list. Then, it updates its own one-hop neighbor list. Next, it verifies the connection status with neighboring nodes, searching the one-hop neighbor table of next-hop routing node A contained in the HELLO message. If source node S is listed in the one-hop neighbor table of next-hop routing node A, it indicates that the communication link between node A and source node S is bidirectionally connected, and next-hop routing node A considers source node S a symmetric neighbor. 2) The source node S regards the one-hop neighbor of the next-hop routing node A as its own two-hop neighbor, which can be reached through the next-hop routing node A, adds it to its own two-hop neighbor list, and updates its own two-hop neighbor list.

[0055] Step S3: Calculate the next-hop route. Each node in the drone swarm relays the data packet to another node, and then forwards it to the destination node. The Mobile Ad Hoc Network (MANET) calculates the Euclidean distances between all its symmetrical one-hop and two-hop neighbors and the fixed ground station as follows:

[0056] Source node S calculates its nearest neighbor node B to the destination base station. If neighbor node B is a one-hop neighbor of source node S, it is designated as the next-hop node of source node S. If neighbor node B is a two-hop neighbor of S, then the one-hop neighbor node A, which is reached via a next-hop route, is designated as the next-hop node of source node S, and the routing table is updated. x n y n , z n The spatial rectangular coordinates of the neighboring nodes to be calculated. x s , y s z s The spatial rectangular coordinates of the fixed ground station.

[0057] Step S4: Perform routing forwarding. When a node in the drone swarm needs to send data, the source node S first determines whether the destination ground station address is in its one-hop neighbor table. If the ground station is a one-hop neighbor of the node, the data packet is sent directly to the ground station. When it is found that communication with the ground station requires multiple hops, the node transmits the data packet to the next hop node according to the routing table it maintains, forwarding it node by node until the data packet reaches the ground station.

[0058] Once the relay node receives a data packet destined for the destination node, the source node S first checks if a connected path exists in its routing table. If it does and the link to the next-hop node is still available, it forwards the packet directly to the next-hop node according to the routing table. Otherwise, it uses a geo-based greedy forwarding strategy, selecting the nearest neighbor node as the next hop. This process continues until the destination drone receives data from the source drone.

[0059] The other parts of this embodiment are the same as those in Embodiment 1 above, so they will not be described again.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A distributed routing protocol method suitable for large-scale unmanned aerial vehicle (UAV) swarm networks, characterized in that, Specifically, the following steps are included: Step 1: Introduce the geolocation-assisted greedy forwarding method to each node of the UAV swarm to obtain local topology information and establish a UAV self-organizing network communication model; Step 2: Transmit the local topology information to a fixed ground station outside the maximum communication distance via a line-of-sight link or satellite link to establish a multi-hop end-to-end route; Step 3: Periodically connect each node in the drone cluster to the channel and send the hello message transmission frame of the drone source node to all neighbors within the communication range; according to the distributed routing of the local topology information, poll each node in a round-robin manner to establish the MAC protocol module; Step 4: Process the received hello messages according to the set hello message format, establish a two-hop neighbor table of local topology information, and add the node's own position information and one-hop neighbor table received from other nodes to the hello message to establish a node movement model. Step 5: Calculate the next-hop route and establish a communication protocol model based on the location information in the one-hop neighbor table and the two-hop neighbor table; Step 6: Update the one-hop neighbor table and the two-hop neighbor table containing the geographical location information of neighboring nodes, and compare them with the routing table of the current node to calculate the shortest path; Step 7: Using the link state stability between nodes as the routing parameter, establish the data transmission path data packet from the source node to the destination node, use the hop count as the path metric, adjust the channel resources in the network, and allocate the channel resources of each node through dynamic routing relay.

2. The distributed routing protocol method for large-scale UAV swarm networks according to claim 1, characterized in that, When the UAV node network layer transmits some or all of the video collected by the UAV's photoelectric ball back to the ground control station via data link, step 2 specifically includes: Step 21: Transmit the platform parameters obtained from the UAV to the ground control station via data link; Step 22: Transmit the UAV control commands received from the ground control station to the UAV via data link; Step 23: Share platform parameters between drones via data link; Step 24: Based on the channel collision principle, when the transmission success rate drops to a set limit, obtain the sum of the number of pulses sent and received on the channel within the statistical time window at this time; Step 25: Data collected and processed from sensors between drones will be transmitted and shared via a data link.

3. The distributed routing protocol method for large-scale UAV swarm networks according to claim 2, characterized in that, When the current operating frequency is interfered with, before periodically connecting each node in the UAV cluster to the channel as described in step 3, the following operations are performed: The channel status is determined based on the number of transmitted and received pulses monitored in the channel; the channel occupancy rate is compared with the data packet threshold; and the channel access is determined based on the comparison result. If the channel occupancy rate is lower than the data packet threshold, a data packet is transmitted; if the channel occupancy rate is higher than the data packet threshold, a backoff time is set according to the backoff algorithm to wait. After the backoff time is reached, the channel occupancy rate is detected again and compared with the data packet threshold to determine whether to connect to the channel. A channel occupancy statistic associated with the channel occupancy status is obtained through numerical calculation; the channel occupancy statistic is compared with the data packet threshold to determine whether to transmit a data packet. If the channel occupancy statistic is lower than the data packet threshold, the channel is connected and a data packet is transmitted.

4. The distributed routing protocol method for large-scale UAV swarm networks according to claim 1, characterized in that, After establishing the node mobility model as described in step 5, the node mobility model will restrict the parameters based on the link lifecycle to route selection, recalculate the route, search for requests hop-by-hop or via broadcast multicast according to the routing algorithm, send a new frame in the next time slot, poll the access control end-to-end route, convert the broadcast channel to a point-to-point channel, and update the routing table information at the UAV node.

5. A distributed routing protocol method suitable for large-scale unmanned aerial vehicle (UAV) swarm networks according to claim 1, characterized in that, The communication protocol model transmits information between different layers of the protocol stack, exchanges HELLO messages between nodes, establishes a one-hop neighbor table and a two-hop neighbor table for each node, calculates the nearest neighbor to the fixed ground station and forwards routes to the neighbor, allocates channel resources for newly joined nodes, and reclaims and redistributes the channel resources originally allocated to decommissioned nodes.

6. A distributed routing protocol method suitable for large-scale UAV swarm networks according to claim 1, characterized in that, The one-hop neighbor table includes the IP address of the one-hop neighbor node, the one-way connectivity status, the two-way connectivity status, the position coordinates (x, y, z) in the spatial rectangular coordinate system, the duration n*HELLO_INTERVAL / ms, the symmetric neighbor type information, and the asymmetric neighbor type information.

7. A distributed routing protocol method suitable for large-scale UAV swarm networks according to claim 1, characterized in that, The two-hop neighbor table includes the IP addresses of the two-hop neighbor nodes, the neighbor types of the two symmetrical neighbors, their geographical coordinates (x', y', z'), the duration of 2*HELLO_INTERVAL, the duration of 3*HELLO_INTERVAL, and the neighbors that have passed through one hop.

8. A distributed routing protocol method suitable for large-scale unmanned aerial vehicle (UAV) swarm networks according to claim 1, characterized in that, The hello message includes the node's IP address, the node's geographical location information, the IP address of the first-hop neighbor, the geographical location information of the first-hop neighbor, the spatial Cartesian coordinate system of the first-hop neighbor, the time-to-live (TTL) of the first-hop neighbor, and the neighbor type of the first-hop neighbor.

9. A distributed routing protocol method suitable for large-scale unmanned aerial vehicle (UAV) swarm networks according to claim 1, characterized in that, When source node S processes the hello message obtained from next-hop routing node A, it performs the following operations: Step S1: Source node S polls each subordinate node in a round-robin manner, takes the next-hop routing node A of the hello message as a one-hop neighbor, adds the IP address and location coordinates of the next-hop routing node A to the one-hop neighbor table of source node S, updates the retention time to 3*HELLO_INTERVAL, and removes neighbors whose retention time is reduced to 0 as invalid neighbors from the neighbor list. Step S2: Update the one-hop neighbor list, verify the connection status with neighboring nodes, and search the one-hop neighbor table containing the next-hop routing node A in the hello message. If the source node S is listed in the one-hop neighbor table of the next-hop routing node A, the communication link between the next-hop routing node A and the source node S is bidirectionally connected, and the next-hop routing node A will regard the source node S as a symmetric neighbor. Step S3: Source node S adds the one-hop neighbor of the next-hop routing node A as a two-hop neighbor, updates the two-hop neighbor list, and calculates the next-hop route; Each node in the drone swarm relays data packets to another node, and then forwards them to the destination node. The mobile ad hoc network (MANET) calculates the Euclidean distances between symmetric one-hop and two-hop neighbors and the fixed ground station: ； Where, x n y n z n Let x be the spatial rectangular coordinates of the neighboring nodes. s y s z s The spatial rectangular coordinates of the fixed ground station; Step S4: The source node S calculates the neighbor node B that is closest to the destination base station from the ground station. If the neighbor node B is a one-hop neighbor of the source node S, the neighbor node B is taken as the next-hop node of the source node S. If neighbor node B is a two-hop neighbor of S, then the one-hop neighbor node A, which is the next-hop route to neighbor node B, is taken as the next-hop node of source node S, and the routing table is updated.

10. A distributed routing protocol method suitable for large-scale unmanned aerial vehicle (UAV) swarm networks according to claim 1, characterized in that, When a request to send data is received from a node in the drone swarm, the specific operation of routing and forwarding is as follows: First, it is determined whether the destination ground station address is in the current node's one-hop neighbor table. If the destination ground station is the current node's one-hop neighbor, then the data packet is sent directly to the destination ground station. If communication between the current node and the destination ground station requires multiple hops, the data packet is transmitted to the next hop node according to the routing table maintained by the current node, and forwarded node by node until the data packet reaches the destination ground station. When a relay node receives a data packet destined for a destination node, it determines whether a path exists in the routing table. If a path exists and the link between the path and the next-hop node is connected, the data packet is forwarded to the next-hop node according to the routing table. Otherwise, following the geographically-based greedy forwarding strategy, the nearest neighbor node to the destination node is selected as the next-hop node until the destination drone receives data from the source drone.

Images