Routing protocol optimization method in UAV hierarchical networking architecture under spectrum denial

Abstract

This paper proposes a routing protocol optimization method for a hierarchical drone networking architecture under spectrum denial. For drones with different hierarchical networking architectures, the method first evaluates the communication performance of drone nodes by calculating the available communication time between drone nodes through GPS location updates. Secondly, it evaluates the network performance of drone nodes by changing physical and data link layer parameters. Finally, by comprehensively considering the communication performance of drone nodes and network performance, it identifies multi-point relay nodes in the network topology to improve routing stability. This method can effectively improve the routing stability and network performance of drone cluster networks in the presence of interference sources in wireless communication environments.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) self-organizing networks, and in particular relates to a routing protocol optimization method in a UAV layered networking architecture under spectrum denial. Background Art

[0002] The presence of interference in wireless communication environments can have various impacts on communication systems, causing degradation of wireless signal quality, reduced communication range, communication interruptions, and data loss. Spectrum sensing technology can be used to address these interference environments. By using sensing devices, these devices monitor and analyze radio spectrum usage. These devices collect spectrum data, including frequency band occupancy, signal strength, and spectrum utilization. By processing and analyzing this data, spectrum sensing systems can gain a comprehensive understanding of the spectrum environment, enabling applications such as electronic surveillance, radio interference detection, and radio spectrum regulation.

[0003] A drone ad hoc network (DAN) is a wireless mobile communication network composed of a number of drones acting as network nodes through self-organization and self-management. These nodes can act as source nodes, destination nodes, or intermediate nodes, and can forward data independently. Data transmission is fundamental to the efficient execution of tasks in a drone network, and routing protocols are primarily responsible for determining the transmission paths of data packets within the drone network, ensuring that information can be efficiently transmitted from the source node to the destination node.

[0004] A hierarchical networking architecture is a complex and efficient network design approach commonly used in drone networks. In this architecture, drones of different types or functions are divided into multiple subnetworks based on their mission, performance, or communication capabilities. Each subnetwork operates relatively independently and maintains its own routing protocol. Each subnetwork can select different routing algorithms and network protocols based on specific application scenarios, thereby optimizing performance in terms of communication efficiency, latency, and bandwidth utilization. The advantage of hierarchical networking is that by dividing network functions into multiple layers, it reduces the complexity and overhead that a single protocol might bring in large-scale networks, while also enhancing network scalability and robustness.

[0005] However, spectrum resources currently available for drone use are scarce, and the available communication channel bandwidth in ad hoc networks is limited. Furthermore, differences in node configuration, routing selection, network congestion levels, and link instability caused by potential interference within wireless communication environments can lead to packet loss, impacting subsequent data transmission. Therefore, it is necessary to optimize routing protocols to make cluster routing more efficient and stable. Designing adaptive routing protocol optimization methods to improve drone performance in environments with limited bandwidth and interference sources is one of the challenges currently facing drone clusters. Summary of the Invention

[0006] This paper addresses the shortcomings of existing technologies and provides a method for optimizing routing protocols in a hierarchical UAV networking architecture under spectrum denial. This method can effectively improve routing stability and network performance in UAV cluster networks when communication channel bandwidth is limited and interference sources are present.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] The present invention provides a method for optimizing routing protocols in a hierarchical networking architecture of unmanned aerial vehicles under spectrum denial, comprising the following steps:

[0009] Step 1: Determine the routing protocol used in the hierarchical network in the spectrum denial scenario. When the routing protocol used is OLSR, identify the first-hop neighbor nodes and the second-hop neighbor nodes of each drone node.

[0010] Step 2: Determine the willingness value of the one-hop neighbor node to become an MPR node, set the node with the willingness value willalways as an MPR node and add it to the MPR set, and determine whether all the two-hop neighbor nodes are covered by the MPR set. If all are covered, jump to step 5;

[0011] Step 3: For the one-hop neighbor nodes that are not set as MPR nodes, the comprehensive performance of the nodes is calculated considering their communication performance and network performance, and the ordered set N is obtained by sorting according to the comprehensive performance;

[0012] Step 4: For the ordered set N, select the node with the highest comprehensive performance score, set it as the MPR node and add it to the MPR set. At the same time, remove it from the ordered set N and determine whether all two-hop neighbor nodes are covered by the MPR set. If not, repeat this step.

[0013] Step 5: By comparing the willingness value of each node in the MPR set to become an MPR node with the set threshold, the MPR set is optimized to obtain the optimal MPR set.

[0014] Optionally, in step 3, the communication performance is calculated by calculating the available communication time between nodes based on the relative movement between nodes, obtaining the communication performance of the nodes and performing normalization processing.

[0015] Optionally, the calculation formula of the communication performance of the node is as follows:

[0016]

[0017] Where S CP,j is the communication performance after normalization, Δt j is the communication available time of UAV node j, max i Δt i is the maximum value of the communication available time among the one-hop neighbor nodes of drone node i, min i Δt i is the minimum value of the communication available time among the one-hop neighbor nodes of drone node i.

[0018] Optionally, the calculation formula for the available communication time between the nodes is as follows:

[0019]

[0020]

[0021] Where Δt is the available communication time between UAV nodes i and j, x i (t) is the x coordinate of drone node i at time t, x j (t) is the x-coordinate of drone node j at time t, v x,rel (t) is the relative motion speed of drone node j relative to drone node i on the x-axis, y i (t) is the y coordinate of drone node i at time t, y j (t) is the y coordinate of drone node j at time t, v y,rel (t) is the relative motion speed of UAV node j relative to UAV node i on the y-axis, d(t) is the distance between UAV nodes i and j at time t, and R is the communication range between UAVs.

[0022] Optionally, in step 3, the network performance is calculated as follows: first, the normalized network performance p of the node is calculated based on the packet error rate of the physical layer statistics. j ; Then, according to the number of packet losses caused by no routing in the data link layer statistics, the normalized network performance of the node is calculated. j According to p j and a j Calculate the comprehensive network performance S NP,j .

[0023] Optionally, the comprehensive network performance S NP,j The calculation formula is as follows:

[0024]

[0025] Where, P j is the packet error rate of drone node j, min i p i is the minimum value of the packet error rate among the one-hop neighbor nodes of drone node i, max i p i A is the maximum packet error rate among the one-hop neighbor nodes of drone node i; j is the number of packet losses caused by the lack of routing for drone node j, min i A i is the minimum value of the number of packet losses caused by the lack of routing in the one-hop neighbor node of drone node i, max i A i is the maximum number of packet losses caused by the lack of routing in the one-hop neighboring nodes of drone node i.

[0026] Optionally, in step 3, the calculation formula of the comprehensive performance of the node is as follows:

[0027] S C,j =7×(α×S CP,j +(1-α)×S NP,j );

[0028] Where S C,j is the comprehensive performance of the node, S CP,j For communication performance, S NP,j is the network performance, and α is the weight factor.

[0029] Optionally, in step 5, the process of optimizing the MPR set is as follows: determine whether there is a node in the current MPR set whose willingness value to become an MPR node is less than a set threshold and after removing the node, all two-hop neighboring nodes are still covered by the MPR set; if so, remove the node that meets the conditions to obtain the optimized MPR set; if not, select the current MPR set as the optimal MPR set.

[0030] The beneficial effects of the present invention are:

[0031] 1. Compared with the standard Optimized Link State Routing (OLSR) protocol, which cannot perform adaptive adjustments based on network status and has poor communication performance in scenarios with spectrum denial interference, the present invention cleverly utilizes the mobility of drone nodes and the parameter changes of the physical layer and data link layer caused by spectrum interference in the presence of spectrum denial interference in the network, better evaluates the comprehensive performance of neighboring nodes, and selects better MPR nodes to better adapt to the interference environment.

[0032] 2. The present invention takes into account the optimal parameter settings of the routing protocol under different interference levels, speeds up the network topology update speed, and thus improves the stability and reliability of the network.

[0033] 3. The present invention comprehensively considers the impact of interference on the communication performance of UAVs and the mobility of nodes, selects more stable MPR nodes, optimizes routing performance, and submits data packet delivery rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of the routing protocol optimization method in the layered networking architecture of UAVs under spectrum denial in the present invention.

[0035] Figure 2 It is a schematic diagram of the actual deployment of the UAV layered networking architecture in the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0037] This paper proposes a routing protocol optimization method in a hierarchical network architecture of UAVs under spectrum denial. The process is as follows: Figure 1 As shown, the specific steps include:

[0038] (1) Initialization: Determine whether there is interference in each subnet in the current hierarchical networking architecture. For the subnet with spectrum denial interference, when the routing protocol used by the subnet is OLSR, obtain the one-hop neighbor node and the two-hop neighbor node of the drone node in the drone network.

[0039] (2) Preliminary MPR set confirmation: The UAV node determines the willingness value of its one-hop neighbor node to become an MPR set node, sets the node with the willingness value willalways as an MPR node, and adds it to the MPR set. It then determines whether all two-hop neighbor nodes are covered. If all are covered, it jumps to step (5).

[0040] (3) Calculation of comprehensive node performance: Based on the position change of the one-hop neighbor node, the relative position change of the node and the neighbor node is calculated. By calculating the relative position relationship of the nodes, the communication performance of the node can be determined. As the distance between the nodes increases, the available communication time may decrease, and the communication performance will decline. The available communication time between the node and the neighbor node is calculated based on the communication distance range to obtain the node's communication performance. The network performance of the neighbor node is calculated based on the packet error rate of the physical layer and the number of packet losses caused by the lack of routing at the data link layer. Finally, the comprehensive performance of the node is obtained by comprehensively considering the communication performance and network performance, and then sorted to obtain the ordered set N.

[0041] In this embodiment, step (3) specifically includes the following steps:

[0042] (31) The available communication time between nodes is calculated based on the relative speed between the node and its neighboring nodes. The specific calculation formula is as follows:

[0043]

[0044] Where Δt is the available communication time between UAV nodes i and j, x i (t) is the x coordinate of drone node i at time t, x j (t) is the x-coordinate of drone node j at time t, v x,rel (t) is the relative motion speed of drone node j relative to drone node i on the x-axis, y i (t) is the y coordinate of drone node i at time t, y j (t) is the y coordinate of drone node j at time t, v y,rel (t) is the relative motion speed of UAV node j relative to UAV node i on the y-axis, d(t) is the distance between UAV nodes i and j at time t, and R is the communication range between UAVs.

[0045] (32) The normalized network performance of the neighboring node is calculated based on the packet error rate of the neighboring node's physical layer statistics. The specific calculation formula is as follows:

[0046]

[0047] Among them, P j is the packet error rate of drone node j, min i p iis the minimum value of the packet error rate among the one-hop neighbor nodes of drone node i, max i p i is the maximum value of the packet error rate among the one-hop neighbor nodes of drone node i.

[0048] (33) According to the number of packet losses caused by the lack of routing statistics at the neighboring node data link layer, the normalized network performance of the neighboring node is calculated. The specific calculation formula is as follows:

[0049]

[0050] Among them, A j is the number of packet losses caused by the lack of routing for drone node j, min i A i is the minimum value of the number of packet losses caused by the lack of routing in the one-hop neighbor node of drone node i, max i A i is the maximum number of packet losses caused by the lack of routing in the one-hop neighboring nodes of drone node i.

[0051] (34) According to the communication available time calculated in (31), the normalized communication performance of the neighboring node is calculated. The specific calculation formula is as follows:

[0052]

[0053] Where Δt j is the communication available time of UAV node j, max i Δt i is the maximum value of the communication available time among the one-hop neighbor nodes of drone node i, min i Δt i is the minimum value of the communication available time among the one-hop neighbor nodes of drone node i.

[0054] (35) According to the normalized network performance calculated by (32) and (33), the comprehensive network performance is calculated. The specific calculation formula is as follows:

[0055]

[0056] (36) Based on the normalized communication performance and comprehensive network performance calculated by (34) and (35), the comprehensive performance score of the node is weighted and calculated. The specific calculation formula is as follows:

[0057] S C,j =7×(α×S CP,j +(1-α)×S NP,j );

[0058] Among them, α is the weight factor, which can determine the weight of communication performance and network performance in the comprehensive performance score;

[0059] After calculating the comprehensive performance scores of the nodes, they are sorted from high to low to obtain an ordered set N.

[0060] (4) MPR set update: For the ordered set N, select the node with the highest comprehensive performance score, set it as the MPR node and add it to the MPR set. Finally, remove it from the ordered set N and determine whether all the two-hop neighbor nodes of the node are fully covered. If not, repeat this step.

[0061] (5) MPR set optimization: After obtaining the MPR set of the node, the node performs optimization judgment to determine whether there is a node in the current MPR set whose willingness to become an MPR node is less than the set threshold and all two-hop neighbor nodes are still fully covered after the node is removed. If so, the node that meets the conditions is removed, and finally the optimized MPR set is obtained. If not, the current MPR set is selected.

[0062] Next, the effectiveness of the method proposed in this embodiment is illustrated with reference to examples.

[0063] In the EXata5.4 simulation platform, a 1000m*1000m drone self-organizing network area was constructed, with 20 drone source nodes and 1 interference source node set. The interference mode adopted spectrum denial interference, and the interference frequency band was set to 2-6GHZ. Figure 2 A schematic diagram of the actual deployment is given.

[0064] The main steps of implementing this method are as follows:

[0065] 1. Initialization: Determine the routing protocol used in hierarchical networking in spectrum denial scenarios. When the routing protocol used is OLSR, confirm the first-hop and two-hop neighbor nodes of each node.

[0066] 2. Confirm the initial MPR set: Based on the node's set of one-hop neighbor nodes, determine its willingness to become an MPR node. If the willingness is willalways, the node is set as an MPR node and added to the MPR set to obtain the initial MPR set. Based on the initial MPR set, if the existing MPR node does not cover all two-hop neighbor nodes, calculate the comprehensive performance scores of the remaining one-hop neighbor nodes. If the existing MPR node can cover all two-hop neighbor nodes, proceed to step 5.

[0067] 3. Calculation of comprehensive performance scores: Analyze the nodes in the one-hop neighbor nodes that are not set as MPR nodes. Based on the relative movement between the nodes, calculate the available communication time between the nodes to obtain the node's communication performance, and normalize it among all one-hop neighbor nodes. Based on the packet error rate statistics of the physical layer, the network performance score of the physical layer is normalized in the one-hop neighbor nodes. Based on the number of packet losses caused by the lack of routing statistics of the data link layer, the network performance score evaluated at the data link layer is normalized. Finally, based on the calculated communication performance score, the network performance scores of the physical layer and the data link layer, a weighted calculation is performed to obtain the comprehensive performance score of the node, and then sorted to obtain an ordered set.

[0068] 4. MPR Set Update: Based on the ordered set obtained in step 3, select the node with the highest overall performance score, set it as the MPR node, add it to the MPR set, and remove it from the ordered set. The existing MPR set is evaluated. If the existing MPR node cannot cover all two-hop neighbor nodes, step 4 is repeated until the existing MPR node can cover all two-hop neighbor nodes.

[0069] 5. MPR Set Optimization: For the existing MPR set, determine whether there is a node whose willingness to become an MPR is less than the set threshold, and all two-hop neighbor nodes are still covered after removing the node. If a node that meets the conditions exists, it is removed to obtain the optimized node. If not, the existing MPR set is the optimal MPR set.

[0070] The implementation results show that when calculating the MPR set of drone node E, its one-hop neighbor node set is {B, D, F, I}, and its two-hop neighbor node set is {A, C, G, H, J, K}. Compared with the traditional OLSR routing protocol, the optimized routing protocol has significantly improved performance in terms of throughput and delivery rate.

[0071] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A routing protocol optimization method in a hierarchical UAV networking architecture under spectrum denial, characterized in that: The steps include: Step 1: Determine the routing protocol used in the hierarchical network in the spectrum denial scenario. When the routing protocol used is OLSR, identify the first-hop neighbor nodes and the second-hop neighbor nodes of each drone node. Step 2: Determine the willingness value of the one-hop neighbor node to become an MPR node, set the node with the willingness value willalways as an MPR node and add it to the MPR set, and determine whether all the two-hop neighbor nodes are covered by the MPR set. If all are covered, jump to step 5; Step 3: For the one-hop neighbor nodes that are not set as MPR nodes, the comprehensive performance of the nodes is calculated considering their communication performance and network performance, and the ordered set N is obtained by sorting according to the comprehensive performance; The communication performance is calculated by calculating the available communication time between nodes based on the relative movement between nodes, obtaining the communication performance of the nodes and performing normalization processing. The calculation formula of the communication performance of the nodes is as follows: Where S CP,j is the communication performance after normalization, Δt j is the communication available time of UAV node j, max i Δt i is the maximum value of the communication available time among the one-hop neighbor nodes of drone node i, min i Δt i is the minimum value of the communication available time among the one-hop neighbor nodes of drone node i; The network performance is calculated as follows: first, the normalized network performance p of the node is calculated based on the packet error rate of the physical layer statistics. j ; Then, according to the number of packet losses caused by no routing in the data link layer statistics, the normalized network performance of the node is calculated. j According to p j and a j Calculate the comprehensive network performance S NP,j The comprehensive network performance S NP,j The calculation formula is as follows: Where, P j is the packet error rate of drone node j, min i p i is the minimum value of the packet error rate among the one-hop neighbor nodes of drone node i, max i p i A is the maximum packet error rate among the one-hop neighbor nodes of drone node i; j is the number of packet losses caused by the lack of routing for drone node j, min i A i is the minimum value of the number of packet losses caused by the lack of routing in the one-hop neighbor node of drone node i, max i A i is the maximum number of packet losses caused by the lack of routing in the one-hop neighboring nodes of drone node i; The calculation formula of the comprehensive performance of the node is as follows: S C,j =7×(α×S CP,j +(1-a)×S NP,j ); Where S C,j is the comprehensive performance of the node, S CP,j For communication performance, S NP,j is the network performance, α is the weight factor; Step 4: For the ordered set N, select the node with the highest comprehensive performance score, set it as the MPR node and add it to the MPR set. At the same time, remove it from the ordered set N and determine whether all two-hop neighbor nodes are covered by the MPR set. If not, repeat this step. Step 5: By comparing the willingness value of each node in the MPR set to become an MPR node with the set threshold, the MPR set is optimized to obtain the optimal MPR set.

2. The method for optimizing routing protocols in a hierarchical network architecture of unmanned aerial vehicles under spectrum denial according to claim 1, wherein: The calculation formula for the available communication time between the nodes is as follows: Where Δt is the available communication time between UAV nodes i and j, x i (t) is the x coordinate of drone node i at time t, x j (t) is the x-coordinate of drone node j at time t, v x,rel (t) is the relative motion speed of drone node j relative to drone node i on the x-axis, y i (t) is the y coordinate of drone node i at time t, y j (t) is the y coordinate of drone node j at time t, v y,rel (t) is the relative motion speed of UAV node j relative to UAV node i on the y-axis, d(t) is the distance between UAV nodes i and j at time t, and R is the communication range between UAVs.

3. The method for optimizing routing protocols in a hierarchical network architecture for UAVs under spectrum denial according to claim 1, wherein: In step 5, the process of optimizing the MPR set is as follows: determine whether there is a node in the current MPR set whose willingness to become an MPR node is less than a set threshold and after removing the node, all two-hop neighboring nodes are still covered by the MPR set. If so, remove the node that meets the conditions to obtain the optimized MPR set. If not, select the current MPR set as the optimal MPR set.

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