Efficient communication method based on high-connectivity inter-cluster routing protocol

Abstract

The invention provides an efficient communication method based on a high-connectivity inter-cluster routing protocol, and the method comprises the steps: constructing an unmanned aerial vehicle cluster, and taking each unmanned aerial vehicle as a node; each unmanned aerial vehicle broadcasts node key information in the group; processing the key node information by adopting a greedy forwarding mechanism based on a communication security area to obtain a candidate node for transmitting a current unmanned aerial vehicle node signal to another unmanned aerial vehicle node; calculating a utility function of the current unmanned aerial vehicle signal propagating to the candidate node; screening out an optimal node according to the utility function, and entering the next hop for forwarding; judging the routing state of the next-hop unmanned aerial vehicle node, if the next-hop unmanned aerial vehicle node is caught in a routing hole, forwarding the information by adopting a three-dimensional peripheral forwarding mechanism, otherwise, directly forwarding the information; after the target node receives the information, information transmission is completed; a three-dimensional peripheral forwarding mechanism is designed, the two-dimensional plane constraint is broken through, the problem of routing voids is effectively solved, and the data transmission efficiency is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicle cluster communication, and particularly relates to an efficient communication method based on a high-connectivity inter-cluster routing protocol. BACKGROUND

[0002] In recent years, unmanned aerial vehicle technology has made significant progress, and its development trend has attracted widespread attention worldwide. Flying Ad-hoc Network (FANET) based on a cluster structure significantly improves communication efficiency through a hierarchical architecture that divides the network into a backbone network and a subnet. The backbone network mainly undertakes core data forwarding and global collaborative control tasks between clusters (CH), with less interaction, while the subnet focuses on local information exchange and resource scheduling between clusters (CM), with more interaction. Therefore, generally speaking, the subnet usually adopts an active routing protocol, and the backbone network usually adopts a passive routing protocol.

[0003] Although traditional passive routing protocols are mature in application, when applied to a clustered network with a highly dynamic topology, the relative positions between nodes change constantly, which makes the network topology change rapidly and frequently. This dynamic nature makes it difficult for traditional passive routing protocols to adapt effectively, and a new routing protocol that can perceive network topology changes in real time and make routing decisions quickly is needed. The Greedy Perimeter Stateless Routing (GPSR) routing protocol, with its unique design concept and efficient routing mechanism, has great application potential and development prospects in high-dynamic networks, but still faces many challenges and problems that need to be solved. For example, GPSR is a strategy based on geographical location, which relies on accurate node location information to select the next hop node closest to the target. However, due to the high-speed movement of unmanned aerial vehicles, the next hop node selected at the current time may have moved out of the communication range during data packet transmission, so most research focuses on predicting the position of the unmanned aerial vehicle at the next time.

[0004] However, drone trajectories are subject to mission changes and are highly uncertain, making accurate position prediction difficult. This ineffectiveness of greedy forwarding strategies that rely on proximity is rendered ineffective. Furthermore, current GPSR-based research is largely limited to two-dimensional applications. Due to this limitation, even when encountering routing holes, these protocols fail to take neighboring node height information into account. In the decision-making process for selecting the next-hop node, the actual distance between nodes cannot be determined, and it is difficult to accurately calculate spatial angle relationships. This neglect of three-dimensional spatial features directly leads to the inability to effectively select the optimal next-hop node, making the process of circumventing routing holes extremely difficult, making it difficult to adapt to the actual needs of highly dynamic three-dimensional scenarios such as drone swarm communications. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes an efficient communication method based on a high-connectivity inter-cluster routing protocol, which includes: building a drone cluster and taking each drone as a node; each drone broadcasts key node information within the cluster; using a greedy forwarding mechanism based on a multi-dimensional path selection model to process key node information, and obtain candidate nodes for the current drone node signal to propagate to another drone node; calculating the utility function of the current drone signal propagating to the candidate nodes; screening out the optimal node according to the utility function, and entering the next hop forwarding; judging the routing status of the next hop drone node, if the next hop drone node falls into a routing hole, using a three-dimensional peripheral forwarding mechanism to forward the information, otherwise directly forwarding the information; when the target node receives the information, the information transmission is completed.

[0006] Beneficial effects of the present invention:

[0007] 1. This invention avoids the problem of location information failure caused by the high-speed movement of drones by abandoning traditional location prediction methods. It comprehensively considers key factors such as the drone's secure connectivity rate, residual energy ratio, signal strength ratio, and node degree ratio to form a dynamically weighted optimal path evaluation model and achieve greedy forwarding.

[0008] 2. The present invention designs a three-dimensional peripheral forwarding mechanism, which preferentially selects the drone with the largest angle difference with the previous hop node and the closest distance to the destination node as the next hop, breaking through the two-dimensional plane constraints, effectively solving the routing hole problem, and improving data transmission efficiency.

[0009] 3. Design lightweight Hello packets and routing forwarding packets to implement lightweight dynamic routing discovery and updates, avoid maintaining static routing tables, significantly reduce routing overhead, and improve network adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1A flowchart of a high-connectivity inter-cluster routing protocol based on a communication security zone according to the present application;

[0011] Figure 2 An SCA security zone according to the present application;

[0012] Figure 3 A three-dimensional space horizontal angle deviation according to the present application;

[0013] Figure 4 A three-dimensional space vertical angle deviation according to the present application. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0015] An efficient communication method based on a high-connectivity inter-cluster routing protocol, as shown in Figure 1 includes: constructing a UAV cluster and taking each UAV as a node; each UAV broadcasts node key information in the cluster; a greedy forwarding mechanism based on a multi-dimensional path selection model is used to process the key node information to obtain a candidate node to which the current UAV node signal is propagated to another UAV node; a utility function of the current UAV signal propagation to the candidate node is calculated; the optimal node is screened out according to the utility function, and the next hop forwarding is entered; the routing state of the next hop UAV node is judged, if the next hop UAV node falls into a routing hole, a three-dimensional peripheral forwarding mechanism is used to forward the information, otherwise the information is directly forwarded; when the target node receives the information, the information transmission is completed.

[0016] The present embodiment proposes a high-connectivity inter-cluster routing protocol based on a communication security zone (SCA-GPSR), which improves the traditional greedy forwarding method based on geographical position. The proposed greedy forwarding mechanism based on a communication security zone aims to comprehensively consider key factors such as SLR, RER, SSR and NDR of the UAV to select the optimal transmission path. Meanwhile, aiming at the problem of falling into a routing hole, the peripheral forwarding mode of the traditional two-dimensional GPSR is improved, the UAV with the largest angle difference between the previous hop node and the current node and closest to the target node is selected as the next hop of the three-dimensional peripheral forwarding, solving the problem that the right-hand rule is not available in the two-dimensional space; the lightweight Hello data packet and the routing forwarding data packet are innovatively designed, the traditional routing discovery and path maintenance mechanism is abandoned, the routing overhead is reduced, and efficient data transmission is realized.

[0017] Specifically, the construction of the communication security zone-based greedy forwarding mechanism specifically includes:

[0018] S1, calculation of safety communication rate. The present application introduces the concept of SCA. As shown in Figure 2 , based on SCA, SLR is proposed as a core index to support the selection of the next hop unmanned aerial vehicle. The design logic of this index is to: by quantitatively evaluating the effective connectivity of the adjacent unmanned aerial vehicle in the efficient communication area, the adjacent node in the communication edge area is preferentially avoided. Such nodes may cause sudden disconnection of the communication link due to the invalidation of location information (such as affected by environmental interference, accumulation of positioning error, etc.).

[0019] In the present application, the radius with a communication distance less than or equal to 0.9R is defined as the safety radius , that is . As the communication distance increases from to R, the data packet loss rate will increase sharply, and the communication link performance will deteriorate rapidly.

[0020] The SLR of the source node s and the neighbor cluster head node ( ) can be calculated by the following formula:

[0021]

[0022] Wherein is the edge coefficient. If the current positions of the source node and the neighbor node are and at any time , then can be calculated by the following formula:

[0023]

[0024] The unmanned aerial vehicle c is constantly approaching the source node unmanned aerial vehicle, and ; if the unmanned aerial vehicle c is constantly moving away from the source node unmanned aerial vehicle, then , thereby reducing the probability of being selected, and the calculation formula is:

[0025]

[0026]

[0027] Therefore, when the distance difference between the next hop node and the source node is , that is , this will make the of the unmanned aerial vehicle c reach the maximum value 1.

[0028] In the SCA-GPSR routing method, the source node makes a screening decision on the next hop node according to the SLR index. When the UAV is in the safe area, the SLR value is positively correlated with the distance from the source node: the farther the distance, the higher the SLR value, and the priority of becoming the next hop node is also improved; when the UAV is outside the safe area, the SLR value is negatively correlated with the distance, the farther the distance from the source node, the lower the SLR value, and the probability of being selected as the next hop node is also reduced. This mechanism realizes the differentiated selection of nodes in different areas by dynamically adjusting the SLR value, and guarantees the safety and reliability of the data transmission path.

[0029] S2, calculation of the residual energy ratio.

[0030] In the application, the residual energy calculation formula of the UAV c is:

[0031]

[0032] wherein, is the energy consumed by each node degree per unit time, is the energy consumed per unit time when hovering, is the node degree of the UAV c when acting as a CH for the jth time, is the time when the UAV acts as a cluster head for the jth time, and are the weight and speed of the UAV respectively.

[0033] The RER calculation formula of the UAV c is:

[0034]

[0035] wherein, is the initial energy of the UAV.

[0036] S3, calculation of the signal strength ratio.

[0037] According to the actual flight environment of the UAV in a complex non-line-of-sight (Non Line of Sight, NLOS) environment (such as urban building groups, mountains and forests, indoor environment) and the like, a multi-factor fusion fading model of log-normal shadow fading, atmospheric attenuation and multipath fading is introduced in the GPSR routing protocol, and the next hop is selected by using a signal strength ratio (Signal strength ratio, SSR) between the source node and the neighbor nodes. The model receives the signal power The calculation formula is:

[0038]

[0039] wherein is the transmission power, is the reference distance path loss at distance d, is path loss exponent, d is the distance between the transmitting node and the receiving node, is a shadow fading random variable obeying normal distribution represents power loss caused by atmospheric attenuation, represents multipath fading factor.

[0040] Let the transmitting power of the source node be , the path loss at reference distance d be , and the path loss exponent be . Then the predicted value of the signal strength at the neighbor node c is , and the formula is:

[0041]

[0042] wherein is the shadow fading random variable corresponding to the neighbor node c, is the multipath fading factor at the neighbor node c. The present application defines the signal strength criterion calculation formula as:

[0043]

[0044] wherein and are the maximum and minimum received strengths of the neighbor nodes.

[0045] In the selection of the next hop node, the signal strength criterion value of each neighbor node is calculated , and the neighbor node with the maximum value is determined as the next hop. This process fully considers the path loss, shadow fading, atmospheric attenuation, and multipath effect caused by the distance, and quantifies the signal attenuation caused by various factors. In this way, the routing decision can accurately reflect the comprehensive changes of the signal strength in the actual environment, effectively improving the communication stability and reliability of the selected path.

[0046] S4, calculation of node degree ratio.

[0047] In the routing selection mechanism, the source node preferentially selects a node with a large number of neighbor nodes as the next hop, which can effectively reduce the risk of routing holes caused by sparse node distribution. From the network topology structure, nodes with high node degree are usually located in the central area of the network. Such nodes have more stable links (lower risk of link breakage) and more potential forwarding path options due to their dense neighbor connection characteristics, thereby providing more reliable routing protection for data transmission.

[0048] ​​According to the formula, the node degree of the unmanned plane c can be obtained, and therefore, the NDR calculation formula is:

[0049]

[0050] wherein, is the number of neighbor nodes of the node c.

[0051] S5, design of utility function.

[0052] In FANETs, achieving efficient and stable information propagation is the core goal of guaranteeing network performance. In order to accurately control the selection of data transmission path, the application comprehensively considers the above SLR, RER, SSR and NDR by designing an evaluation fitness (EF), which can select the best next hop node in the routing and forwarding stage, and effectively balance the transmission efficiency, energy consumption, link quality and other factors.

[0053] In the routing decision process, the EF of the unmanned plane is taken as the judgment standard, and the unmanned plane with the maximum EF value is selected as the best next hop node for data packet forwarding. The EF value calculation formula of the unmanned plane c is as follows:

[0054]

[0055]

[0056] wherein , , and are the weights of the above four parameters; is the safe connectivity rate, is the residual energy ratio, is the signal strength ratio, is the node degree ratio.

[0057] In the embodiment, the three-dimensional peripheral forwarding mechanism is used to forward information, including: converting the information into a routing packet format; calculating the horizontal angle deviation, vertical angle deviation and distance quantity of the candidate node according to the data information of the candidate node; constructing a combined cost function according to the horizontal angle deviation, vertical angle deviation and distance quantity; and forwarding information to the next hop by taking the node with the maximum value of the combined cost function as the next hop.

[0058] Specifically, as shown in Figure 3 and Figure 4 , the implementation of the three-dimensional peripheral forwarding mechanism specifically includes: assuming that the current node position is , the position of the candidate node is , and the position of the previous hop node is The comprehensive cost function is composed of three parts, horizontal angle deviation , vertical angle deviation and distance difference .

[0059] S1, calculation of horizontal angle deviation. The calculation of horizontal angle deviation is as follows:

[0060]

[0061]

[0062]

[0063] S2, calculation of vertical angle. The calculation of vertical angle is as follows:

[0064]

[0065]

[0066]

[0067] S3, calculation of distance difference. The calculation formula of distance difference is as follows:

[0068]

[0069] S4, calculation of comprehensive cost function. The calculation formula of comprehensive cost function is as follows:

[0070]

[0071] When dealing with the routing hole problem, the SCA-GPSR protocol makes a choice of the next hop node with the help of the comprehensive cost function. The function takes into account the angle difference (including and ) and the distance difference to the target node, and balances the influence of each factor through weights , and . The higher the value of the comprehensive cost function, the better the node is in bypassing the routing hole. When entering the three-dimensional peripheral forwarding recovery mode, according to the value of the comprehensive cost function, the two principles of changing the direction of data transmission and approaching the destination are considered, and the more suitable next hop drone is accurately selected. In this way, the speed and efficiency of bypassing the routing hole are effectively improved, the efficiency and stability of data transmission are guaranteed, and the adaptability and reliability of the routing protocol in complex scenarios are significantly enhanced.

[0072] The SCA-GPSR routing protocol utilizes innovative design for Hello packets and route forwarding packets, as shown in Tables 1 and 2. This approach eliminates traditional route discovery and path maintenance mechanisms, significantly improving data transmission efficiency and network adaptability. This protocol utilizes two core control packets to establish a dynamic routing system, avoiding the complex interaction between route requests (RREQs) and route replies (RREPs) in traditional protocols. It also eliminates the need to maintain static routing tables, significantly reducing routing overhead and achieving efficient data transmission.

[0073]

[0074]

[0075] In FANETs, ​​high-connectivity inter-cluster routing protocols are the core elements for improving network performance and ensuring stable connections between CH drones. It is not only related to the efficiency of data transmission, but also directly affects the overall reliability of the network. However, due to the high-speed movement of drones and complex environmental factors, the FANETs network topology presents highly dynamic changes, which poses a huge challenge to the design of stable and efficient routing solutions. To address this problem, the present invention optimizes the traditional GPSR protocol and proposes an SCA-GPSR routing protocol based on efficient communication areas. The protocol first designs key indicators such as SLR, RER, SSR and NDR of drones, constructs a multi-dimensional path selection model, and applies this model to the greedy forwarding process. By comprehensively evaluating these indicators, the optimal data transmission path between the source node and the destination node can be accurately screened, thereby effectively improving the stability and efficiency of data propagation. Secondly, when dealing with the routing hole problem, the SCA-GPSR protocol designs an efficient three-dimensional peripheral forwarding mechanism. This mechanism is based on the horizontal angle deviation of neighboring drones. , vertical angle deviation and distance , constructing a utility function. By calculating the utility function value of each neighboring node and selecting the node with the highest value as the next hop, efficient path detour is achieved in routing hole scenarios. Finally, this invention abandons traditional routing discovery and path maintenance mechanisms and innovatively designs Hello packets and routing forwarding packets, significantly reducing routing overhead and achieving efficient data transmission.

[0076] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation plans of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An efficient communication method based on high connectivity inter-cluster routing protocol, characterized in that, The application comprises the following steps: Constructing a UAV cluster and taking each UAV as a node; Each UAV broadcasts node key information in the cluster; Processing the key node information by using a greedy forwarding mechanism based on a multi-dimensional path selection model to obtain candidate nodes to which the current UAV node signal is propagated to another UAV node; Calculating the utility function of the current UAV signal propagation to the candidate node; Screening the optimal node according to the utility function and entering the next hop forwarding; Judging the routing state of the next hop UAV node, if the next hop UAV node falls into a routing hole, using a three-dimensional peripheral forwarding mechanism to forward the information, otherwise directly forwarding the information; When the target node receives the information, the information transmission is completed.

2. The efficient communication method based on high-connectivity inter-cluster routing protocol according to claim 1, characterized in that, The node key information is designed in the form of a Hello data packet, wherein the key node information comprises a node IP, a node signal strength, a number of neighbor nodes, neighbor node key information, a current node coordinate, a current node residual energy, and node degree information.

3. The method according to claim 2, wherein, Processing the key node information by using a greedy forwarding mechanism based on a multi-dimensional path selection model comprises the following steps: S101, calculating the safe connectivity rate of the UAV according to the position coordinates and communication radius of the neighbor UAV; S102, calculating the residual energy ratio according to the residual energy of the UAV; S103, calculating the signal strength of the field UAV node by using a signal strength attenuation model according to the position coordinates of the neighbor UAV to obtain a signal strength ratio; S104, calculating the node degree ratio according to the node degree of the neighbor UAV; S105, weighting the safe connectivity rate, the residual energy ratio, the signal strength ratio, and the node degree ratio to obtain the utility function.

4. The method according to claim 3, wherein, Calculating the signal strength of the field UAV node by using the signal strength attenuation model comprises the following steps: the signal strength attenuation model comprises attenuation factors of logarithmic-normal shadow fading, atmospheric attenuation, and multipath fading; calculating the power of the received signal based on the attenuation factor calculation model; calculating the loss of the model according to the power of the received signal; calculating the signal strength prediction value of the neighbor node based on the loss of the model; and calculating the signal strength ratio of the UAV according to the signal strength prediction value.

5. The method according to claim 4, wherein, The calculation formula of the signal strength prediction value of the neighbor node is as follows: ； wherein, Psourceis the source node transmit power, P0is a reference distance Ppathlossis the path loss at distance d, Ppathlossis the path loss at distance d, P0is a reference distance Pshadowis the shadowing random variable corresponding to neighbor node c, P0is a reference distance P0is a reference distance 6. The method according to claim 3, wherein, The utility function expression is as follows: ； ； wherein, , , and are weights, is a safety connectivity rate, is a residual energy ratio, is a signal strength ratio, is a node degree ratio.

7. The method according to claim 1, wherein, Screening the optimal node according to the utility function comprises the following steps: calculating the utility value of each node by using the utility function, and taking the node with the maximum utility value as the next hop node of the greedy forwarding.

8. The method according to claim 1, wherein, Forwarding the information by using the three-dimensional peripheral forwarding mechanism comprises the following steps: converting the information into a routing packet format; calculating the horizontal angle deviation, the vertical angle deviation, and the distance amount of the candidate node according to the data information of the candidate node; constructing a combined cost function according to the horizontal angle deviation, the vertical angle deviation, and the distance amount; and taking the node with the maximum value of the combined cost function as the next hop for information forwarding.

9. The method according to claim 8, wherein, The information in the routing packet format comprises a node IP, a recovery mode flag, a target node coordinate, a UAV node coordinate in the peripheral forwarding mode, and a last hop node coordinate.

10. The method according to claim 8, wherein, The combined cost function is as follows: ； wherein , , are weights, is a horizontal angular deviation, is a vertical angular deviation, is a distance difference.

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