OLSR protocol adaptive strategy based on position information of unmanned aerial vehicle

Abstract

The invention discloses an OLSR protocol adaptive strategy based on unmanned aerial vehicle position information, and belongs to the technical field of computer network routing protocols. The OLSR protocol method comprises the steps that position coordinates and speed information of nodes of an unmanned aerial vehicle are obtained through a Beidou satellite navigation system (BDS) carried by the unmanned aerial vehicle, then the position, namely the predicted position, of the next moment is calculated through the speed information of the unmanned aerial vehicle, the cosine similarity of the actual position and the predicted position of the unmanned aerial vehicle is calculated according to the actual position and the predicted position of the unmanned aerial vehicle, and the cosine similarity of the actual position and the predicted position of the unmanned aerial vehicle is calculated. And comparing the numerical values of the cosine similarity, defining a new HELLO message sending period by utilizing the speed of the HELLO message, modifying a data packet format field of the HELLO message, and adding the cosine similarity, the position information and the speed information.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer network routing protocols, and in particular relates to an OLSR protocol self-adaptation strategy based on unmanned aerial vehicle position information. Background Art

[0002] Mobile ad hoc networks have been widely used in emergency services, military operations and other fields because of their lack of infrastructure, strong anti-destruction capabilities, support for self-configuration and easy expansion. With the research progress of mobile ad hoc networks and the continuous development of drone swarm collaboration, drone ad hoc networks have become one of the research hotspots. Compared with traditional mobile ad hoc networks, drone ad hoc networks have a wider coverage, and drones have higher flexibility and adaptability, can cross ground obstacles, and can be equipped with a variety of advanced hardware, such as sensors, cameras and communication equipment. Therefore, drone ad hoc networks show strong advantages in changing and complex environments. However, despite the unique advantages of drone ad hoc networks, their key technical difficulties are similar to those of traditional mobile ad hoc networks, and are still concentrated on the design and optimization of routing protocols. Among them, the OLSR (Optimized Link State Routing) protocol is one of the most widely used routing protocols in ad hoc networks.

[0003] The OLSR protocol is an a priori routing protocol that updates the network's routing information by periodically sending control messages (such as HELLO messages and TC messages). In the traditional OLSR protocol, 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 can no longer adapt to its dynamic network environment. Summary of the invention

[0004] In view of this, the present invention provides an OLSR protocol adaptive strategy based on drone position information. In the present invention, in order to improve the adaptability to the drone ad hoc network environment, a new HELLO message sending cycle is defined according to the actual position and predicted position of each drone and its own speed.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] An OLSR protocol adaptive strategy based on UAV position information, characterized in that the method comprises the following steps:

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

[0008] Step 2) According to the current time t obtained, drone F i The position information and speed information of the drone are used to calculate the position information of the drone at the next time t+1, that is, the predicted position;

[0009] Step 3) Calculate the drone F i The cosine similarity CS between the position information at time t+1 and the predicted position information i ;

[0010] Step 4) Determine the size of the cosine similarity and whether the cosine similarity is within the range If it is within, go to the next step if it is judged to be yes, and go to step 8 if it is judged to be no;

[0011] Step 5) Determine the speed value V of the drone at time t+1 i t+1 Is it less than the velocity value V at the previous moment t? i t If the answer is yes, proceed to the next step; if the answer is no, proceed to step 7;

[0012] Step 6) Setup Where HT i t is the drone F at time t i The corresponding HELLO message sending cycle;

[0013] Step 7) Set up HT i t+1 =HT i t , that is, the HELLO message sending cycle is consistent with the previous moment; Step 8) Set Where V max , V min are the maximum and minimum speed values ​​of the drone swarm.

[0014] Further, in step 1, drone F i According to the Beidou satellite navigation system (BDS) carried by itself, the accurate location information at time t+1 is obtained and drone F i Position information at time t Next is the drone F i Velocity information, i.e. speed and direction of movement in Represents the magnitude of the UAV's velocity vector, It represents the angle between the projection of the UAV velocity vector on the XY plane and the X axis. Indicates the angle between the drone's velocity vector and the Z axis.

[0015] Further, in step 2, drone F i Obtain time t UAV F through its own BeiDou Satellite Navigation System (BDS) i The position information and speed information, that is, the position information and speed information Calculate the position information of the drone at the next time t+1, that is, the predicted position The corresponding and The calculation formula is as follows:

[0016]

[0017] Where: V i t (V min ≤V i t ≤V max ) indicates drone F i The magnitude of the velocity vector, Indicates drone F i HELLO message sending cycle, It represents the angle between the projection of the UAV velocity vector on the XY plane and the X axis. Indicates the angle between the drone's velocity vector and the Z axis.

[0018] Further, in step 3, drone F i According to the Beidou satellite navigation system (BDS) carried by itself, obtain the accurate location information at the current time t+1 The accurate position information at time t+1 obtained using the Beidou Satellite Navigation System (BDS) The drone’s position information is calculated and predicted Compare; use the cosine similarity calculation formula to determine the cosine similarity of the position of the drone node at time t+1. The calculation formula is as follows

[0019]

[0020] Further, in step 4, the size of the cosine similarity is determined, where the cosine similarity calculation is an indicator to measure the direct similarity of 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, and the closer the value is to -1, the two vectors are in opposite states. In the above formula, CS i Indicates the similarity between the two positions of the drone, and its value range is [-1,1]. The closer the value is to 1, the higher the similarity between the predicted position and the actual position. i Close to -1, the two positions are very different; when the cosine similarity is in the range When the cosine similarity is within this range, the control overhead is reduced by changing the sending period of the HELLO message; if the cosine similarity is not within this range, the sending period of the HELLO message needs to be reduced, so as to update the neighbor relationship and network topology information faster to improve the message sending efficiency.

[0021] Further, in step 5, after determining the range of cosine similarity is After that, we need to calculate the speed V of the drone at time t+1 i t+1 and the velocity value V at the previous moment t i t To make a comparison, if the speed value V at time t+1 i t+1 Compared with the speed value V at the previous moment t i t If it is small, it can indirectly indicate that the dynamic topology of the drone swarm does not change much.

[0022] Further, in step 6, as in the above steps, the HELLO message sending period of the drone is adjusted using the range of position information similarity values; after determining that the range of cosine similarity is within And the speed value V of the drone at time t+1 i t+1 Less than the velocity value V at the previous moment t i t Afterwards, the specific adjusted HELLO message sending cycle size value is revised according to the speed value of the drone; the changed HELLO message sending cycle is as follows:

[0023]

[0024] Further, in step 7, when determining the range of cosine similarity However, if the speed increases, there is no need to change the HELLO message sending cycle, and the default value is maintained; the HELLO message sending cycle is as follows:

[0025] HT i t+1 =HT i t .

[0026] Further, in step 8, when the position information cosine similarity When , it means that the actual position and the predicted position are not similar to each other, which reflects that the network topology changes dynamically. Then, the HELLO message sending cycle can be reduced to allow the drone node to update the neighbor relationship and network topology information faster. The reduced value is modified according to the speed value of the drone. The changed HELLO message sending cycle is as follows:

[0027]

[0028] Where V max and V min Indicates the maximum and minimum values ​​of the drone speed in the network.

[0029] The effective effect of the present invention lies in: an OLSR protocol adaptive strategy based on drone position information, which makes corresponding improvements to the technical defect that the HELLO message of the traditional OLSR protocol adopts a fixed sending period under different network environments, and defines a new HELLO message sending period according to the actual position and predicted position of each drone and its own speed. In the case of rapid topology changes, the sending frequency is increased, thereby updating the neighbor relationship and network topology information more quickly to improve the message sending efficiency; in the case of slow topology changes and relatively stable nodes, the sending frequency is reduced to reduce the control overhead. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to make the purpose, technical solution and beneficial effects of the present invention more clear, the present invention provides the following drawings for illustration:

[0031] Figure 1 This is a flow chart of the OLSR protocol self-adaptation strategy described in an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of the predicted position and actual position of the drone according to an embodiment of the present invention;

[0033] Figure 3 This is a HELLO message format diagram described in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. The described embodiments are only some examples of the present invention, but not all of the embodiments.

[0035] Example

[0036] The present invention provides an OLSR protocol adaptive strategy based on drone location information, wherein the adaptive strategy for control messages comprises the following steps:

[0037] Step 1) Use the Beidou Satellite Navigation System (BDS) installed on the drone to obtain the current drone F i Position information and speed information;

[0038] Step 2) According to the current time t obtained, drone F i The position information and speed information of the drone are used to calculate the position information of the drone at the next time t+1, that is, the predicted position;

[0039] Step 3) Calculate the drone F i The cosine similarity CS between the position information at time t+1 and the predicted position information i ;

[0040] Step 4) Determine the size of the cosine similarity and whether the cosine similarity is within the range If it is within, go to the next step if it is judged to be yes, and go to step 8 if it is judged to be no;

[0041] Step 5) Determine the speed value V of the drone at time t+1 i t+1 Is it less than the velocity value V at the previous moment t? i t If the answer is yes, proceed to the next step; if the answer is no, proceed to step 7;

[0042] Step 6) Setup Where HT i t is the drone F at time t i The corresponding HELLO message sending cycle;

[0043] Step 7) Set up HT i t+1 =HT i t , that is, the HELLO message sending cycle is consistent with the previous moment;

[0044] Step 8) Setup Where V max , V min are the maximum and minimum speed values ​​of the drone swarm.

[0045] The above process is as follows Figure 1 shown.

[0046] Among them, in step 2, according to the current time t obtained, drone F i The position information and speed information of the drone are used to calculate the next position information of the drone at time t+1, that is, the predicted position; Figure 2 The actual position and predicted position can be observed more clearly.

[0047] In step 7, set HT i t+1 =HT i t , that is, the HELLO message sending cycle is consistent with the previous moment; the initial HELLO message sending cycle is 2 seconds, that is, HT i 0 =2.

[0048] in, Figure 3 It is represented as a modified HELLO message format, in which the drone's location information, speed information and cosine similarity CS are added i .

[0049] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than 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 in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. An OLSR protocol adaptive strategy based on UAV position information, characterized by: The following steps are involved: Step 1) Use the Beidou Satellite Navigation System (BDS) installed on the drone to obtain the current drone F i Position information and speed information; Step 2) According to the current time t obtained, drone F i The position information and speed information of the drone are used to calculate the position information of the drone at the next time t+1, that is, the predicted position; Step 3) Calculate the drone F i The cosine similarity CS between the position information at time t+1 and the predicted position information i ; Step 4) Determine the size of the cosine similarity and whether the cosine similarity is within the range If it is within, go to the next step if it is judged to be yes, and go to step 8 if it is judged to be no; Step 5) Determine the speed of the drone at time t+1 Is it less than the speed value at the previous moment t? If the judgment is yes, proceed to the next step, if the judgment is no, proceed to step 7; Step 6) Setup In the formula is the drone F at time t i The corresponding HELLO message sending cycle; Step 7) Setup That is, the HELLO message sending cycle remains the same as the previous moment; Step 8) Setup Where V max , V min are the maximum and minimum speed values ​​of the drone swarm.

2. The OLSR protocol adaptive strategy based on drone location information according to claim 1 is characterized in that: The specific process in step 1 includes: Drone F i According to the Beidou satellite navigation system (BDS) carried by itself, the accurate location information at time t+1 is obtained and drone F i Position information at time t Next is the drone F i Velocity information, i.e. speed and direction of movement in Represents the magnitude of the UAV's velocity vector, It represents the angle between the projection of the UAV velocity vector on the XY plane and the X axis. Indicates the angle between the drone's velocity vector and the Z axis.

3. The OLSR protocol adaptive strategy based on drone location information according to claim 1 is characterized in that: The specific process in step 2 includes: Drone F i Obtain time t UAV F through its own BeiDou Satellite Navigation System (BDS) i The position information and speed information, that is, the position information and speed information Calculate the position information of the drone at the next time t+1, that is, the predicted position The corresponding and The calculation formula is as follows: Where: Indicates drone F i The magnitude of the velocity vector, Indicates drone F i HELLO message sending cycle, It represents the angle between the projection of the UAV velocity vector on the XY plane and the X axis. Indicates the angle between the drone's velocity vector and the Z axis.

4. The OLSR protocol adaptive strategy based on drone location information according to claim 1 is characterized in that: The specific process in step 3 includes: Drone F i According to the Beidou satellite navigation system (BDS) carried by itself, obtain the accurate location information at the current time t+1 The accurate position information at time t+1 obtained using the Beidou Satellite Navigation System (BDS) The drone’s position information is calculated and predicted Compare; use the cosine similarity calculation formula to determine the cosine similarity of the position of the drone node at time t+1. The calculation formula is as follows:

5. The OLSR protocol adaptive strategy based on drone location information according to claim 1 is characterized in that: The specific process in step 4 includes: Determine the size of cosine similarity, where cosine similarity calculation is an indicator to measure the direct similarity of 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, and the closer the value is to -1, the two vectors are in opposite states. In the formula of step 3, CS i Indicates the similarity between the two positions of the drone, and its value range is [-1,1]. The closer the value is to 1, the higher the similarity between the predicted position and the actual position. i Close to -1, the two positions are very different; when the cosine similarity is in the range When the cosine similarity is within this range, the control overhead is reduced by changing the sending period of the HELLO message; if the cosine similarity is not within this range, the sending period of the HELLO message needs to be reduced, so as to update the neighbor relationship and network topology information faster to improve the message sending efficiency.

6. The OLSR protocol adaptive strategy based on drone location information according to claim 1 is characterized in that: The specific process in step 5 includes: After determining the range of cosine similarity After that, the speed value of the drone at time t+1 is also required and the velocity value at the previous moment t Make a comparison of the size. If the speed value at time t+1 is Compared with the speed value at the previous moment t If it is small, it can indirectly indicate that the dynamic topology of the drone swarm does not change much.

7. The OLSR protocol adaptive strategy based on drone location information according to claim 1 is characterized in that: The specific process in step 6 includes: As in the above steps, the HELLO message sending period of the drone is adjusted using the range of position information similarity values; after determining the range of cosine similarity And the speed value of the drone at time t+1 Less than the speed value at the previous moment t Afterwards, the specific adjusted HELLO message sending cycle size value is revised according to the speed value of the drone; the changed HELLO message sending cycle is as follows:

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

9. The OLSR protocol adaptive strategy based on drone location information according to claim 1 is characterized in that: The specific process in step 8 includes: When the position information cosine similarity When , it means that the actual position and the predicted position are not similar to each other, which reflects that the network topology changes dynamically. Then, by reducing the HELLO message sending cycle, the drone node can update the neighbor relationship and network topology information faster. The reduced value is modified according to the speed value of the drone. The changed HELLO message sending cycle is as follows: Where V max and V min Indicates the maximum and minimum values ​​of the drone speed in the network.

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