CN111050301B - Unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology - Google Patents

Unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology Download PDF

Info

Publication number
CN111050301B
CN111050301B CN201911295378.8A CN201911295378A CN111050301B CN 111050301 B CN111050301 B CN 111050301B CN 201911295378 A CN201911295378 A CN 201911295378A CN 111050301 B CN111050301 B CN 111050301B
Authority
CN
China
Prior art keywords
node
nodes
olsr
distance
unmanned aerial
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201911295378.8A
Other languages
Chinese (zh)
Other versions
CN111050301A (en
Inventor
米志超
姜雨卿
王海
于卫波
李艾静
赵宁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Army Engineering University of PLA
Original Assignee
Army Engineering University of PLA
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Army Engineering University of PLA filed Critical Army Engineering University of PLA
Priority to CN201911295378.8A priority Critical patent/CN111050301B/en
Publication of CN111050301A publication Critical patent/CN111050301A/en
Application granted granted Critical
Publication of CN111050301B publication Critical patent/CN111050301B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/18—Communication route or path selection, e.g. power-based or shortest path routing based on predicted events
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/34—Modification of an existing route
    • H04W40/38—Modification of an existing route adapting due to varying relative distances between nodes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

An unmanned aerial vehicle network OLSR routing self-adaptive strategy based on dynamic topology relates to the technical field of unmanned aerial vehicle network OLSR routing self-adaptive strategy. The invention utilizes Kalman filtering algorithm to obtain the simulation result of the next state of the node; calculating the current distance N between nodes and the predicted distance M between nodes; and obtaining the broadcasting period of the Hello messages of the two nodes, and reducing the broadcasting period of the remaining two times of Hello messages to an optimal value for the first time of receiving the Hello message of the new node, and simultaneously, not receiving any new Hello message. When the unmanned aerial vehicle group frequently moves in the network, the nodes predict the state of the linear mobile nodes through a Kalman filtering algorithm, the routing protocol can dynamically detect whether the topology changes or not, and the broadcasting period of the control message is adaptively adjusted.

Description

Unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology
Technical Field
The invention relates to the technical field of an unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology.
Background
In a network of multiple unmanned nodes, any node movement may cause a change in the network topology. Frequent changes in the topology of the mobile network can cause substantial fluctuations or even severe degradation in overall network performance. The ability of a routing protocol adapted to an unmanned aerial vehicle ad hoc network to dynamically detect topology changes is one of the most important research objectives in this field. Currently existing main dynamic routing protocols, such as a link state-based routing protocol OSPF protocol, a distance vector-based routing protocol RIP protocol, or an optimized link state-based routing protocol OLSR protocol, obtain a network topology structure through periodic or triggered interactive connectivity, and when a network changes, a new topology table or routing table can be calculated through the updated connectivity.
Traditional routing protocols use Hello messages of a fixed broadcast period to detect network topology, and this strategy obviously cannot adapt to the changes in dynamic topology to adjust routing parameters, which appears to be too dead in the military tactics of unmanned aerial vehicle group dynamic networking. Traditional routing protocols initiate topology upgrade messages, such as Hello and TC messages, only when the node link on-off relationship changes. In the environment of rapid movement of nodes, a certain time is required for the topology update messages to propagate into the network, and during the time, part of routes in the original routing tables are old failure routes, and part of data are lost in forwarding due to errors of the routing tables. Furthermore, in the case where the network is not yet fully connected, the data may not be sent out because of no route. These conditions will reduce the throughput of the network and increase the transmission delay of the data.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology, when an unmanned aerial vehicle group frequently moves in a network, nodes predict the state of a linear mobile node through a Kalman filtering algorithm, a routing protocol can dynamically detect whether the topology changes or not, and the broadcasting period of control messages of the routing protocol can be self-adaptively adjusted. The method does not need to schedule the time point of the task in advance, can actively predict the network topology, and adaptively adjusts corresponding routing parameters once the trend of topology change is found.
An unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology comprises the following steps:
step 1) obtaining a simulation result of the next state of the node by using a Kalman filtering algorithm;
step 2) calculating the current distance N between nodes and the predicted distance M between nodes;
step 3) judging whether the current distance N between nodes is larger than or equal to the maximum effective distance L and smaller than R, and meanwhile, the predicted distance M between nodes is larger than R, wherein R is the communication range of the nodes, if so, executing the next step, and if not, executing the step 7);
step 4) to obtain
Figure GDA0004157675450000021
Wherein->
Figure GDA0004157675450000022
In the formula, H is the broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of the neighbor nodes;
step 5), the routing protocol sends a third Hello message, if yes, the next step is executed, if no, the last step is returned;
step 6) setting
Figure GDA0004157675450000023
And returning to the first step; wherein->
Figure GDA0004157675450000024
In the formula, H is the broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of the neighbor nodes;
step 7) judging whether the node receives the Hello information of the new node for the first time, if so, executing the next step, and if not, returning to the first step;
step 8) reducing the H of the remaining two times of the Hello information to an optimal value, and simultaneously, not receiving any new Hello information;
step 9) judging the result of the step 8), if yes, recovering H to a default value, and returning to the step 1); if not, returning to the step 7).
By adopting the technical scheme, when the unmanned aerial vehicle group frequently moves in the network, the nodes predict the state of the linear mobile node through the Kalman filtering algorithm, the routing protocol can dynamically detect whether the topology changes, and the broadcasting period of the control message can be adaptively adjusted. The method does not need to schedule the time point of the task in advance, can actively predict the network topology, and adaptively adjusts corresponding routing parameters once the trend of topology change is found.
Drawings
Fig. 1 is a schematic representation of unmanned node predictions in a unmanned network.
Fig. 2 is a schematic flow chart of a routing calculation method based on dynamic topology.
FIG. 3 is a flow chart of the simulation platform of the present invention.
Fig. 4 is a schematic diagram of test results of simulation experiments on node throughput at different time periods of node flight.
Fig. 5 is a schematic diagram of test results of a simulation experiment on a node packet loss rate under different time periods of node flight.
FIG. 6 is a graph of the results of the number of Hello messages sent during Exata simulation.
FIG. 7a is a diagram comparing DT-OLSR protocol with original OLSR protocol at a maximum effective communication time of 500 ms.
FIG. 7b is a diagram comparing DT-OLSR protocol with original OLSR protocol at a maximum effective communication time of 200ms.
FIG. 7c is a diagram comparing DT-OLSR protocol with original OLSR protocol at a maximum effective communication time of 100ms.
Fig. 8a is a graph comparing throughput using DT-OLSR protocol and using original OLSR protocol based on the same number of table tennis mobile model nodes.
Fig. 8b is a graph comparing packet loss rates using DT-OLSR protocol and using original OLSR protocol based on the same number of table tennis mobile model nodes.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the accompanying drawings:
as shown in fig. 2, an unmanned aerial vehicle network OLSR route self-adaptation method based on dynamic topology includes the following steps:
step 1) obtaining a simulation result of the next state of the node by using a Kalman filtering algorithm;
step 2) calculating the current distance N between nodes and the predicted distance M between nodes;
step 3) judging whether the current distance N between nodes is larger than or equal to the maximum effective distance L and smaller than R, and meanwhile, the predicted distance M between nodes is larger than R, wherein R is the communication range of the nodes, if so, executing the next step, and if not, executing the step 7);
step 4) to obtain
Figure GDA0004157675450000041
Wherein->
Figure GDA0004157675450000042
In the formula, H is the broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of the neighbor nodes;
step 5), the routing protocol sends a third Hello message, if yes, the next step is executed, if no, the last step is returned;
step 6) setting
Figure GDA0004157675450000051
And returning to the first step; wherein->
Figure GDA0004157675450000052
In the formula, H is the broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of the neighbor nodes;
step 7) judging whether the node receives the Hello information of the new node for the first time, if so, executing the next step, and if not, returning to the step 1);
step 8) reducing the H of the remaining two times of the Hello information to an optimal value, and simultaneously, not receiving any new Hello information;
step 9) judging the result of the step 8), if yes, recovering H to a default value, and returning to the step 1); if not, returning to the step 7).
The specific process of the step 2) of the invention is as follows:
the node receives the Hello message, obtains the coordinates (x, y) of the neighbor node and the relative velocity v, calculates the distance N between the node and the neighbor node at the moment as
Figure GDA0004157675450000053
The coordinate (x ', y ') and the relative velocity v ' of the node at the next moment calculated by the Kalman filtering algorithm are calculated, and the distance M between the node at the next moment and the neighbor node is calculated as +.>
Figure GDA0004157675450000054
Figure GDA0004157675450000055
D in the formula ij Is the distance between two nodes, x i X is the abscissa of node i j Is the abscissa of node j, y i Is the ordinate of node i, y j Is the ordinate of node j.
The specific process of the step 3) of the invention is as follows:
when (when)
Figure GDA0004157675450000061
And->
Figure GDA0004157675450000062
If the second node B has a certain distance from the communication boundary of the first node a, and the topology disturbance in the range is ignored, the Hello message broadcast period H of the two nodes remains unchanged, and the distance is called a maximum effective distance L, and is preset through DT-OLSR.
The specific process of the step 4) of the invention is as follows:
when the second node B moves to the maximum effective distance L, the effective time 3H for sending the Hello message is less than or equal to the maximum effective communication time T of the node, namely 0 < 3 H.ltoreq.T, so that H back-pushing is estimated as
Figure GDA0004157675450000063
Wherein->
Figure GDA0004157675450000064
The specific process of the step 6) of the invention is as follows:
when the second node B is to
Figure GDA0004157675450000065
After the broadcast period of (1) has been completed, the DT-OLSR protocol is started according to the current validity time +.>
Figure GDA0004157675450000066
Final refinement of H, i.e. +.>
Figure GDA0004157675450000067
H maintains this high frequency until the first node a updates the routing table. />
The specific process of the step 7) and the step 8) of the invention is as follows:
if the third node C is to join the communication range of the first node a, the third node C typically needs to send three Hello messages to be added to the routing table; when the first node A receives the first strange Hello message, the broadcasting period of the two other Hello messages is reduced to the optimal value, and H maintains the frequency until the first node A completes updating the routing table
Step S101: the node receives the Hello message, obtains the coordinates (x, y) of the neighbor node and the relative velocity v, calculates the distance between the node and the neighbor node at the moment
Figure GDA0004157675450000068
The coordinate (x ', y ') and the relative speed v ' of the node at the next moment calculated by the Kalman filtering algorithm are calculated, and the distance between the node at the next moment and the neighbor node is calculated>
Figure GDA0004157675450000069
Step S102: when (when)
Figure GDA00041576754500000610
And->
Figure GDA00041576754500000611
If B is still a certain distance from the communication boundary of a, and topology disturbance in the range can be ignored, the Hello message broadcasting period H of the node remains unchanged. This distance is called the maximum effective distance L, and is preset by DT-OLSR;
step S103: when the second node B moves to the maximum effective distance L, the effective time 3H for sending the Hello message is not greater than the maximum effective communication time T of the node, namely 0 < 3 H.ltoreq.T, so that H back-pushing is estimated as
Figure GDA0004157675450000071
Wherein->
Figure GDA0004157675450000072
Step S104: when the second node B is to
Figure GDA0004157675450000073
After the broadcast period of (1) has been completed, the DT-OLSR protocol is started according to the current validity time +.>
Figure GDA0004157675450000074
Final refinement of H, i.e. +.>
Figure GDA0004157675450000075
H maintains this high frequency until the first node a updates the routing table.
Step S105: if the third node C is to join the communication range of the first node a, it typically needs to send three Hello messages to be added to the routing table. When the first node a receives the first strange Hello message, the broadcasting period of the Hello message is reduced to an optimal value for the remaining two times. H maintains this frequency until the first node a completes the update of the routing table.
Step S106: after the node has confirmed that the network topology remains relatively stationary, the broadcast period of the Hello message is adjusted back to a default value.
In large-scale UANET, the unmanned node can ideally predict the location of the next state of the node through a kalman filter algorithm. The Kalman filtering algorithm can utilize the transformation of the state matrix to predict the topology change of the next second according to the conditions of the coordinate position, the speed, the acceleration and the like of the current states of the source node and the target node. The kalman filter algorithm is:
Figure GDA0004157675450000076
Figure GDA0004157675450000077
Figure GDA0004157675450000078
where k represents the discrete time at which the node sends the Hello message,
Figure GDA0004157675450000079
is a state vector of the movement of the node,
Figure GDA0004157675450000081
is the node movement predicted state vector at the next discrete time instant of sending the Hello message. x and y are the position coordinates of the node, vx and v y Is the relative speed of the nodes in different directions. Phi is the state transition matrix, B is the noise matrix, H is the observation matrix, +.>
Figure GDA0004157675450000082
Is the acceleration of the unmanned plane, is regarded as white noise processing in a short time, +.>
Figure GDA0004157675450000083
Can be from GPS systemThe observation vector obtained in the system. />
Figure GDA0004157675450000084
Is observation noise, determined by the accuracy of the GPS device.
Fig. 1 is a schematic diagram of unmanned aerial vehicle node prediction in the unmanned aerial vehicle network according to the present invention.
Figure GDA0004157675450000085
Is the distance between the nodes of the network,
Figure GDA0004157675450000086
the distance between nodes after the Kalman filtering algorithm predicts is calculated by a formula (4).
Figure GDA0004157675450000087
R is the communication range of the node. By comparison of
Figure GDA0004157675450000088
And R, we can further design an adaptive routing strategy based on dynamic topology.
In military warfare, unmanned aerial vehicles are sometimes required to scout the surrounding environment to obtain important military spying and related combat information. In order to collect more abundant and comprehensive data information, the information combat departments need to place unmanned aerial vehicles in a plurality of directions and collect information at fixed points. The combat headquarter linearly traverses the whole military information network through a movable unmanned aerial vehicle, communicates with each reconnaissance unmanned aerial vehicle, and sends data acquired by all fixed-point unmanned aerial vehicles to the combat destination. In order to be close to reality, the scene is simulated in the whole course in Exata simulation software, and the nodes all adopt a road point movement model. The plurality of nodes remain stationary, simulating a scout drone. In addition, an unmanned aerial vehicle with a flight track traversing the whole unmanned aerial vehicle network topology is arranged, and the unmanned aerial vehicle is summarized by simulation information. In the simulation scene range of 1.5km by 1.5km, the furthest unmanned aerial vehicle in the topology and the unmanned aerial vehicle in the flight are selected for fixed bit rate (Constant Bit Rate, CBR) service in an experiment, and the traffic size is 1.04Mbps. Traffic is generated from the 10 th second for 33 seconds and the simulation is continued for 43 seconds. The protocols for each different parameter are independently simulated 100 times and then compared for network performance before and after improvement.
The DT-OLSR protocol was simulated and its feasibility verified using Matlab and Exata5.1 simulation software. In order to enable a Kalman filtering algorithm to obtain relevant information of nodes, socket communication service is built between Matlab and Exata simulation platforms. The method comprises the steps of importing information such as node coordinates and relative speed in Exata software into Matlab software through UDP communication, then simulating in a Matlab simulation platform according to a Kalman filtering algorithm, predicting position information of unmanned aerial vehicle nodes and neighbor nodes, and calculating the distance between the nodes through a formula (4). The data obtained by Matlab simulation is transmitted to an Exata simulation platform, and the Exata uses a self-adaptive routing algorithm based on dynamic topology to adjust the parameters of the current routing protocol. The simulation flow chart is shown in fig. 3.
Exata5.1 simulation software uses the original OLSR protocol with a Hello message broadcast period of constant 2s for comparison experiments with the present modified DT-OLSR protocol. The unmanned aerial vehicle of simulation experiment uses the omnidirectional antenna, and the channel bandwidth is 2Mbps, and communication range is about 340m. The MAC layer adopts IEEE802.11 protocol and is accessed in a CSMA/CA mode. The specific experimental parameters are shown in table 1.
TABLE 1
Item category Value taking
Antenna type Omnidirectional antenna
Mobile model Fixed point/linear movement
Communication range 340m
Simulation scene scope 1.5km*1.5km
Service data type CBR(255～1040Kb/s)
Channel bandwidth 2Mbps
Number of nodes 5～40
Speed of movement 15m/s
The method uses Exata simulation software to randomly distribute up to 40 nodes on a map to form a large-scale UANET. The nodes all adopt a table tennis movement model to carry out linear movement, and the scene of the unmanned aerial vehicle swarm in large-scale reconnaissance enemy and battlefield environments is simulated. Simulation configuration as tested above, the maximum effective time takes the best 200ms of performance improvement. We tested the network performance when the number of nodes was 5, 10, 20, 30, 40, respectively.
Fig. 4 is a schematic diagram of test results of simulation experiments on node throughput at different time periods of node flight. Fig. 5 is a schematic diagram of test results of a simulation experiment on a node packet loss rate under different time periods of node flight. In the figure, the time in the DT-OLSR suffix bracket is the maximum effective communication time of the node preset by the DT-OLSR protocol.
FIG. 6 is a graph of the results of the number of Hello messages sent during Exata simulation. The results show that the number of Hello messages sent peaks at a node time of flight of 30s and that the shorter the maximum validity time, the more Hello messages sent by the DT-OLSR protocol. Therefore, the smaller the maximum effective time of the DT-OLSR protocol is not, the better. While increasing the frequency of transmission of Hello messages may have some benefit in stabilizing network performance, too much frequency may increase node overhead, burdening the channel, and making the purpose of the routing policy counterproductive.
Fig. 7 is a schematic diagram of the DT-OLSR protocol of the present invention compared with the original OLSR protocol in three scenarios, respectively, the traffic sizes of the test change CBR service data are 1500Kbps,1040Kbps and 510Kbps, respectively, fig. 7a is a maximum effective communication time of 500ms, fig. 7b is a maximum effective communication time of 200ms, and fig. 7c is a maximum effective communication time of 100ms. As can be seen from fig. 7, as shown in fig. 7. Under the same scene, with the increase of CBR business data flow, the OLSR protocol and the DT-OLSR protocol both improve the average throughput of the nodes. However, the performance of using the DT-OLSR protocol is better than that of using the OLSR protocol, regardless of the increase in CBR.
The simulation result of the table tennis movement model is shown in fig. 8. FIG. 8a is a graph comparing throughput using DT-OLSR protocol and using original OLSR protocol when the number of nodes is the same; fig. 8b is a comparison diagram of packet loss rate using DT-OLSR protocol and using original OLSR protocol when the number of nodes is the same. It can be seen from the figure that the overall network performance is improved after using the DT-OLSR protocol compared with that of the original OLSR protocol, and when the number of nodes is the same, the throughput of the DT-OLSR protocol is larger than that of the OLSR protocol and the packet loss rate is smaller.

Claims (7)

1. An unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology is characterized by comprising the following steps:
step 1) obtaining a simulation result of the next state of the node by using a Kalman filtering algorithm;
step 2) calculating the current distance N between nodes and the predicted distance M between nodes;
step 3) judging whether the current distance N between nodes is larger than or equal to the maximum effective distance L and smaller than R, and meanwhile, the predicted distance M between nodes is larger than R, wherein R is the communication range of the nodes, if so, executing the next step, and if not, executing the step 7);
step 4) to obtain
Figure FDA0004157675440000011
Wherein->
Figure FDA0004157675440000012
In the formula, H is the broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of the neighbor nodes;
step 5), the routing protocol sends a third Hello message, if yes, the next step is executed, if no, the last step is returned;
step 6) setting
Figure FDA0004157675440000013
And returning to the first step; wherein->
Figure FDA0004157675440000014
In the formula, H is the broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of the neighbor nodes;
step 7) judging whether the node receives the Hello information of the new node for the first time, if so, executing the next step, and if not, returning to the step 1);
step 8) reducing the H of the remaining two times of the Hello information to an optimal value, and simultaneously, not receiving any new Hello information;
step 9) judging the result of the step 8), if yes, recovering H to a default value, and returning to the step 1); if not, returning to the step 7).
2. The unmanned aerial vehicle network OLSR route adaptive method based on dynamic topology according to claim 1, wherein the kalman filtering algorithm is:
Figure FDA0004157675440000021
Figure FDA0004157675440000022
Figure FDA0004157675440000023
where k represents the discrete time at which the node sends the Hello message,
Figure FDA0004157675440000024
is a state vector of node movement, +.>
Figure FDA0004157675440000025
Is the node movement predicted state vector at the next discrete time instant of the Hello message, x and y are the position coordinates of the node, vx and v y Is the relative speed of the nodes in different directions, phi is a state transition matrix, B is a noise matrix, H is an observation matrix,>
Figure FDA0004157675440000026
is the acceleration of the unmanned plane, is regarded as white noise processing in a short time, +.>
Figure FDA0004157675440000027
Is an observation vector available from the GPS system, a ∈>
Figure FDA0004157675440000028
Is observation noise, determined by the accuracy of the GPS device.
3. The unmanned aerial vehicle network OLSR route adaptive method based on dynamic topology according to claim 1, wherein the specific process of the step 2) is as follows:
the node receives the Hello message, obtains the coordinates (x, y) of the neighbor node and the relative velocity v, calculates the distance N between the node and the neighbor node at the moment as
Figure FDA0004157675440000029
The coordinate (x ', y ') and the relative velocity v ' of the node at the next moment calculated by the Kalman filtering algorithm are calculated, and the distance M between the node at the next moment and the neighbor node is calculated as +.>
Figure FDA00041576754400000210
Figure FDA00041576754400000211
D in the formula ij Is the distance between two nodes, x i X is the abscissa of node i j Is the abscissa of node j, y i Is the ordinate of node i, y j Is the ordinate of node j.
4. The adaptive method for OLSR routing of the unmanned aerial vehicle network based on the dynamic topology according to claim 1, wherein the specific process of the step 3) is as follows:
when (when)
Figure FDA0004157675440000031
And->
Figure FDA0004157675440000032
If the second node B has a certain distance from the communication boundary of the first node a, and the topology disturbance in the range is ignored, the Hello message broadcast period H of the two nodes remains unchanged, and the distance is called a maximum effective distance L, and is preset through DT-OLSR.
5. The adaptive method for OLSR routing of the unmanned aerial vehicle network based on the dynamic topology according to claim 1, wherein the specific process of the step 4) is as follows:
when the second node B moves to the maximum effective distance L, the effective time 3H for sending the Hello message is less than or equal to the maximum effective communication time T of the node, namely 0 < 3 H.ltoreq.T, so that H back-pushing is estimated as
Figure FDA0004157675440000033
Wherein->
Figure FDA0004157675440000034
6. The unmanned aerial vehicle network OLSR route adaptive method based on dynamic topology according to claim 1, wherein the specific process of the step 6) is as follows:
when the second node B is to
Figure FDA0004157675440000035
After the broadcast period of (1) has been completed, the DT-OLSR protocol is started according to the current validity time +.>
Figure FDA0004157675440000036
Final refinement of H, i.e. +.>
Figure FDA0004157675440000037
H maintains this high frequency until the first node a updates the routing table.
7. The unmanned aerial vehicle network OLSR route self-adaption method based on the dynamic topology according to claim 1, wherein the specific processes of the step 7) and the step 8) are as follows:
if the third node C is to join the communication range of the first node a, the third node C typically needs to send three Hello messages to be added to the routing table; when the first node a, upon receiving the first strange Hello message, reduces the broadcasting period of the Hello message to the optimal value for the remaining two times, H maintains this frequency until the first node a completes updating the routing table.
CN201911295378.8A 2019-12-16 2019-12-16 Unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology Active CN111050301B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911295378.8A CN111050301B (en) 2019-12-16 2019-12-16 Unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911295378.8A CN111050301B (en) 2019-12-16 2019-12-16 Unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology

Publications (2)

Publication Number Publication Date
CN111050301A CN111050301A (en) 2020-04-21
CN111050301B true CN111050301B (en) 2023-05-26

Family

ID=70236793

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911295378.8A Active CN111050301B (en) 2019-12-16 2019-12-16 Unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology

Country Status (1)

Country Link
CN (1) CN111050301B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112383872B (en) * 2020-11-12 2022-09-20 中国空气动力研究与发展中心 Optimization method for broadcast transmission frequency of distributed unmanned aerial vehicle formation state information
CN112469054A (en) * 2020-11-18 2021-03-09 西北工业大学 Topology-aware routing method for unmanned aerial vehicle cluster formation
CN112672398B (en) * 2020-12-28 2023-06-16 上海微波技术研究所（中国电子科技集团公司第五十研究所） 3D-GPSR routing method based on self-adaptive kalman prediction

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109922513A (en) * 2019-03-06 2019-06-21 上海交通大学 A kind of OLSR method for routing and system based on moving projection and latency prediction

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9818136B1 (en) * 2003-02-05 2017-11-14 Steven M. Hoffberg System and method for determining contingent relevance

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109922513A (en) * 2019-03-06 2019-06-21 上海交通大学 A kind of OLSR method for routing and system based on moving projection and latency prediction

Also Published As

Publication number Publication date
CN111050301A (en) 2020-04-21

Similar Documents

Publication Publication Date Title
CN108600942B (en) Routing method of unmanned aerial vehicle ad hoc network
CN111050301B (en) Unmanned aerial vehicle network OLSR route self-adaption method based on dynamic topology
CN102916889B (en) Instant route selection based on multi-path communication time and credibility in VANET (Vehicular Ad-Hoc Network)
CN109922513B (en) OLSR routing method and system based on mobile prediction and time delay prediction
CN110475205B (en) Relay selection method based on track relation in unmanned aerial vehicle ad hoc network and unmanned aerial vehicle
CN111343608B (en) Internet of vehicles reinforcement learning routing method based on position information
CN110167097B (en) Mobile robot relay routing method based on weighted metric forwarding and path planning
Hu et al. An enhanced GPSR routing protocol based on the buffer length of nodes for the congestion problem in VANETs
CN108024200B (en) Self-adaptive method and system for intermittently connected unmanned aerial vehicle self-organizing network
CN113115399B (en) Route optimization method for self-organizing network of heterogeneous unmanned aerial vehicle
CN111614559B (en) Method, system and medium for realizing global optimization of AODV routing
CN108271139A (en) A kind of method for routing based on software definition car networking
CN103619046B (en) The In-vehicle networking unicast routing method of adaptive vehicle density
Husain et al. Simulated analysis of location and distance based routing in VANET with IEEE802. 11p
CN104835316B (en) Traffic flow density-based solution to problem of VANET sparse connectivity
CN109688614B (en) Implementation method and system for high-dynamic unmanned aerial vehicle ad hoc network routing protocol
CN110267321B (en) Greedy multicast routing protocol design method in three-dimensional scene
CN109561392B (en) Self-adaptive dynamic sensing route for driving environment of Internet of vehicles
Huo et al. A UAV mobile strategy in mobile ad hoc networks
CN109587751B (en) Self-adaptive routing method and system for unmanned aerial vehicle ad hoc network
CN112672398B (en) 3D-GPSR routing method based on self-adaptive kalman prediction
Shafiq et al. QoS in Vehicular Ad Hoc Networks–A Survey
CN111356199B (en) Vehicle-mounted self-organizing network routing method in three-dimensional scene
Jiang et al. Research on OLSR adaptive routing strategy based on dynamic topology of UANET
CN113645589A (en) Counter-fact strategy gradient-based unmanned aerial vehicle cluster routing calculation method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant