An Adaptive Strategy for the OLSR Protocol Based on UAV Location Information

Abstract

This invention discloses an adaptive strategy for the OLSR protocol based on UAV location information, belonging to the field of computer network routing protocol technology. The OLSR protocol method includes: obtaining the position coordinates and velocity information of the UAV's own node through its built-in BeiDou Navigation Satellite System (BDS); calculating the predicted position at the next moment using the UAV's velocity information; calculating the cosine similarity between the actual and predicted positions; comparing the values ​​of the cosine similarity; defining a new HELLO message sending cycle using the UAV's own velocity; and modifying the data packet format fields of the HELLO message by adding cosine similarity, position information, and velocity information.

Description

Technical Field

[0001] This invention belongs to the field of computer network routing protocol technology, specifically relating to an adaptive strategy for the OLSR protocol based on UAV location information. Background Technology

[0002] Mobile ad hoc networks (MANs) have been widely used in emergency services, military operations, and other fields due to their characteristics such as no reliance on infrastructure, strong resilience, self-configuration, and easy expansion. With the advancement of MAN research and the continuous development of UAV swarm collaboration, UAV ad hoc networks have become a research hotspot. Compared to traditional MANs, UAV ad hoc networks offer wider coverage, and UAVs possess greater flexibility and adaptability, capable of traversing ground obstacles and equipped with various advanced hardware such as sensors, cameras, and communication devices. Therefore, UAV ad hoc networks exhibit strong advantages in dynamic and complex environments. However, despite their unique advantages, the key technical challenges of UAV ad hoc networks are similar to those of traditional MANs, still focusing on the design and optimization of routing protocols. Among these, OLSR (Optimized Link State Routing) is one of the most widely used routing protocols in ad hoc networks.

[0003] OLSR is an a priori routing protocol that updates network routing information by periodically sending control messages (such as HELLO and TC messages). In traditional OLSR protocols, the generation and sending cycle of control messages is fixed. However, due to the high flexibility and rapidly changing network topology of UAV ad hoc networks, the traditional fixed-cycle mechanism is no longer suitable for their dynamic network environment. Summary of the Invention

[0004] Therefore, this invention provides an adaptive strategy for the OLSR protocol based on UAV location information. In this invention, to improve adaptability to UAV ad hoc network environments, a new HELLO message sending cycle is defined based on each UAV's actual and predicted locations and its own speed.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An adaptive strategy for the OLSR protocol based on UAV location information, characterized by the following steps:

[0007] Step 1) Use the BeiDou Navigation Satellite System (BDS) installed on the UAV to obtain the current UAV F i Location and speed information;

[0008] Step 2) Based on the obtained current time t, the drone F i The location and velocity information are used to calculate the drone's position information at the next time t+1, i.e., the predicted position;

[0009] Step 3) Calculate the predicted location information and the UAV F i Cosine similarity CS of accurate location information at time t+1 i ;

[0010] Step 4) Determine the magnitude of the cosine similarity and whether the magnitude of the cosine similarity is within the range. If the condition is met, proceed to the next step; otherwise, proceed to step 8.

[0011] Step 5) Determine the magnitude V of the drone's velocity at time t+1. i t+1 Is the velocity V less than the value at the previous moment t? i t If the determination is yes, proceed to the next step; otherwise, proceed to step 7.

[0012] Step 6) Settings In the formula HT i t For time t, the drone F i The corresponding HELLO message sending cycle;

[0013] Step 7) Configure HT i t+1 =HT i t That is, the HELLO message sending cycle remains consistent with the previous moment;

[0014] Step 8) Settings In the formula V max V min These represent the maximum and minimum speed values ​​for the drone swarm.

[0015] Furthermore, in step 1, the drone F i Based on its own BeiDou Navigation Satellite System (BDS), it obtains accurate location information at time t+1. and drone F i Location information at time t Secondly, there is the drone F. i Speed ​​information, namely speed and direction of motion. Where V i t (V min ≤V i t ≤V max ) represents the magnitude of the drone's velocity vector. This represents the angle between the projection of the UAV's velocity vector onto the XY plane and the X-axis. This represents the angle between the drone's velocity vector and the Z-axis.

[0016] Furthermore, in step 2, the drone F i The UAV obtains its time by installing the BeiDou Navigation Satellite System (BDS). i Location information and velocity information, i.e., location information and speed information Calculate the drone's position information at the next time step t+1, i.e., predict its position. The corresponding here and The calculation formula is as follows:

[0017]

[0018] In the formula: V i t (V min ≤V i t ≤V max ) indicates the drone F i Velocity vector magnitude, HT i t Indicates drone F i The HELLO message sending cycle, This represents the angle between the projection of the UAV's velocity vector onto the XY plane and the X-axis. This represents the angle between the drone's velocity vector and the Z-axis.

[0019] Furthermore, in step 3, the drone F i Based on its own BeiDou Navigation Satellite System (BDS), it obtains the accurate location information at the current time t+1. Accurate location information at time t+1 obtained using the BeiDou Navigation Satellite System (BDS) Compared with the predicted drone location information based on calculations A comparison is made; the cosine similarity of the UAV node's position at time t+1 is determined using the cosine similarity calculation formula, as follows:

[0020]

[0021] Furthermore, in step 4, the cosine similarity is determined. Cosine similarity is an indicator of the direct similarity between two vectors. A cosine similarity value closer to 1 indicates greater similarity between the two vectors; a value closer to 0 indicates less similarity; and a value closer to -1 indicates opposite similarity. In the above formula, CS...i This represents the similarity between two locations of the drone, with a value ranging from [-1, 1]. A value closer to 1 indicates a high degree of similarity between the predicted and actual locations. If the value is less than 1, the similarity is lower. i If the cosine similarity is close to -1, then the two positions are very different; when the cosine similarity is within the range... Within this range, control overhead can be reduced by changing the sending cycle of HELLO messages; if the cosine similarity is outside this range, the sending cycle of HELLO messages needs to be reduced to update neighbor relationships and network topology information more quickly and improve message sending efficiency.

[0022] Furthermore, in step 5, after determining the range of cosine similarity... Next, the velocity value V of the drone at time t+1 also needs to be calculated. i t+1 The velocity value V at the previous time t i t Perform a comparison of magnitudes; if the velocity value V at time t+1... i t+1 The velocity value V compared to the previous time t i t A small value can indirectly indicate that the dynamic topology of the drone swarm is not changing significantly.

[0023] Furthermore, in step 6, as described above, the HELLO message sending cycle of the drone is adjusted using the range of location information similarity values; after determining the range of cosine similarity... The magnitude of the drone's velocity V at time t+1 i t+1 The magnitude of the velocity V is less than the value at the previous moment t. i t Subsequently, the specific adjustment to the HELLO message sending cycle value was revised based on the drone's speed value; the revised HELLO message sending cycle is as follows:

[0024]

[0025] Furthermore, in step 7, the range of cosine similarity is determined to be within... However, if the speed is increased, there is no need to change the HELLO message sending cycle; the default value remains. The HELLO message sending cycle is as follows:

[0026] HT i t+1 =HT i t .

[0027] Furthermore, in step 8, when the cosine similarity of the location information... When the actual location and the predicted location are dissimilar, it indicates a significant dynamic change in the network topology. To address this, the HELLO message sending cycle can be reduced to allow drone nodes to update neighbor relationships and network topology information more quickly. The reduction value is adjusted based on the drone's speed. The modified HELLO message sending cycle is as follows:

[0028]

[0029] In the formula V max and V min This represents the maximum and minimum speeds of drones in the network.

[0030] The advantages of this invention are as follows: An adaptive strategy for the OLSR protocol based on UAV location information addresses the technical deficiency of the traditional OLSR protocol's fixed sending cycle for HELLO messages under different network environments. This strategy defines a new HELLO message sending cycle based on each UAV's actual and predicted locations and its own speed. When topology changes rapidly, the sending frequency is increased to update neighbor relationships and network topology information more quickly, thereby improving message sending efficiency. When topology changes slowly and nodes are relatively stable, the sending frequency is reduced to decrease control overhead. Attached Figure Description

[0031] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

[0032] Figure 1 This is a flowchart of the adaptive strategy of the OLSR protocol according to an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram showing the predicted and actual positions of the UAV as described in an embodiment of the present invention.

[0034] Figure 3 This is a diagram of the HELLO message format as described in an embodiment of the present invention. Detailed Implementation

[0035] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings. The described embodiments are only a part of the embodiments of the present invention, and not all of them.

[0036] Example

[0037] This invention provides an adaptive strategy for the OLSR protocol based on UAV location information. The adaptive strategy for control messages includes the following steps:

[0038] Step 1) Use the BeiDou Navigation Satellite System (BDS) installed on the UAV to obtain the current UAV F iLocation and speed information;

[0039] Step 2) Based on the obtained current time t, the drone F i The location and velocity information are used to calculate the drone's position information at the next time t+1, i.e., the predicted position;

[0040] Step 3) Calculate the predicted location information and the UAV F i Cosine similarity CS of accurate location information at time t+1 i ;

[0041] Step 4) Determine the magnitude of the cosine similarity and whether the magnitude of the cosine similarity is within the range. If the condition is met, proceed to the next step; otherwise, proceed to step 8.

[0042] Step 5) Determine the magnitude V of the drone's velocity at time t+1. i t+1 Is the velocity V less than the value at the previous moment t? i t If the determination is yes, proceed to the next step; otherwise, proceed to step 7.

[0043] Step 6) Settings In the formula HT i t For time t, the drone F i The corresponding HELLO message sending cycle;

[0044] Step 7) Configure HT i t+1 =HT i t That is, the HELLO message sending cycle remains consistent with the previous moment;

[0045] Step 8) Settings In the formula V max V min These represent the maximum and minimum speed values ​​for the drone swarm.

[0046] The above process is as follows Figure 1 As shown.

[0047] In step 2, the drone F is based on the obtained current time t. i Using the location and velocity information, calculate the drone's position at the next time step t+1, i.e., predict its position; Figure 2 This allows for a clearer observation of the actual location and the predicted location.

[0048] In step 7, HT is set i t+1 =HT it This means the HELLO message sending period remains consistent with the previous moment; the initial HELLO message sending period is 2 seconds, i.e., HT i 0 =2.

[0049] in, Figure 3 This is represented by the modified HELLO message format, which includes the drone's location information, speed information, and cosine similarity (CS). i .

[0050] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. An adaptive strategy for the OLSR protocol based on UAV location information, characterized in that: Includes the following steps: Step 1) Use the BeiDou Navigation Satellite System (BDS) installed on the UAV to obtain the current UAV F i Location and speed information; Step 2) Based on the obtained current time t, the drone F i The location and velocity information are used to calculate the drone's position information at the next time t+1, i.e., the predicted position; Step 3) Calculate the predicted location information and the UAV F i Cosine similarity CS of accurate location information at time t+1 i ; Step 4) Determine the magnitude of the cosine similarity and whether the magnitude of the cosine similarity is within the range. If the condition is met, proceed to the next step; otherwise, proceed to step 8. Step 5) Determine the magnitude V of the drone's velocity at time t+1. i t+1 Is the velocity V less than the value at the previous moment t? i t If the determination is yes, proceed to the next step; otherwise, proceed to step 7. Step 6) Settings In the formula For time t, the drone F i The corresponding HELLO message sending cycle; Step 7) Settings That is, the HELLO message sending cycle remains consistent with the previous moment; Step 8) Settings In the formula V max V min These represent the maximum and minimum speed values ​​for the drone swarm.

2. The adaptive strategy for the OLSR protocol based on UAV location information according to claim 1, characterized in that, The specific process in step 1 includes: Drone F i Based on its own BeiDou Navigation Satellite System (BDS), it obtains accurate location information at time t+1. and drone F i Location information at time t Secondly, there is the drone F. i Speed ​​information, namely speed and direction of motion. Where V i t (V min ≤V i t ≤V max ) represents the magnitude of the drone's velocity vector. This represents the angle between the projection of the UAV's velocity vector onto the XY plane and the X-axis. This represents the angle between the drone's velocity vector and the Z-axis.

3. The OLSR protocol adaptive strategy based on UAV location information according to claim 1, characterized in that, The specific process in step 2 includes: Drone F i The UAV obtains its time by installing the BeiDou Navigation Satellite System (BDS). i Location information and velocity information, i.e., location information and speed information Calculate the drone's position information at the next time step t+1, i.e., predict its position. The corresponding here and The calculation formula is as follows: In the formula: V i t (V min ≤V i t ≤V max ) indicates the drone F i Magnitude of velocity vector Indicates drone F i The HELLO message sending cycle, This represents the angle between the projection of the UAV's velocity vector onto the XY plane and the X-axis. This represents the angle between the drone's velocity vector and the Z-axis.

4. The adaptive strategy for the OLSR protocol based on UAV location information according to claim 1, characterized in that, The specific process in step 3 includes: Drone F i Based on its own BeiDou Navigation Satellite System (BDS), it obtains the accurate location information at the current time t+1. Accurate location information at time t+1 obtained using the BeiDou Navigation Satellite System (BDS) Compared with the predicted drone location information based on calculations A comparison is made; the cosine similarity of the UAV node's position at time t+1 is determined using the cosine similarity calculation formula, which is as follows:

5. The adaptive strategy for the OLSR protocol based on UAV location information according to claim 1, characterized in that, The specific process in step 4 includes: Determine the magnitude of cosine similarity. Cosine similarity is an indicator of the direct similarity between two vectors. The closer the cosine similarity value is to 1, the more similar the two vectors are; the closer the value is to 0, the less similar the two vectors are; the closer the value is to -1, the more opposite the two vectors are. In step 3 of the formula, CS... i This represents the similarity between two locations of the drone, with a value ranging from [-1, 1]. A value closer to 1 indicates a high degree of similarity between the predicted and actual locations. If the value is less than 1, the similarity is lower. i If the cosine similarity is close to -1, then the two positions are very different; when the cosine similarity is within the range... Within this range, control overhead can be reduced by changing the sending cycle of HELLO messages; if the cosine similarity is outside this range, the sending cycle of HELLO messages needs to be reduced to update neighbor relationships and network topology information more quickly and improve message sending efficiency.

6. The adaptive strategy for the OLSR protocol based on UAV location information according to claim 1, characterized in that, The specific process in step 5 includes: After determining the range of cosine similarity... Next, the velocity value V of the drone at time t+1 also needs to be calculated. i t+1 The velocity value V at the previous time t i t Perform a comparison of magnitudes; if the velocity value V at time t+1... i t+1 The velocity value V compared to the previous time t i t A small value can indirectly indicate that the dynamic topology of the drone swarm is not changing significantly.

7. The OLSR protocol adaptive strategy based on UAV location information according to claim 1, characterized in that, The specific process in step 6 includes: As described above, the HELLO message sending cycle of the drone is adjusted using the range of location information similarity values; after determining the range of cosine similarity... The magnitude of the drone's velocity V at time t+1 i t+1 The magnitude of the velocity V is less than the value at the previous moment t. i t Subsequently, the specific adjustment to the HELLO message sending cycle value was revised based on the drone's speed value; the revised HELLO message sending cycle is as follows:

8. The OLSR protocol adaptive strategy based on UAV location information according to claim 1, characterized in that, The specific process in step 7 includes: After determining the range of cosine similarity... However, if the speed is increased, there is no need to change the HELLO message sending cycle; the default value remains. The HELLO message sending cycle is as follows:

9. The adaptive strategy for the OLSR protocol based on UAV location information according to claim 1, characterized in that, The specific process in step 8 includes: When the cosine similarity of location information When the actual location and the predicted location are dissimilar, it indicates a significant dynamic change in the network topology. Therefore, by reducing the HELLO message sending cycle, drone nodes can update neighbor relationships and network topology information more quickly. The reduction value is modified according to the drone's speed. The modified HELLO message sending cycle is as follows: In the formula V max and V min This represents the maximum and minimum speeds of drones in the network.

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