CN110958661B - Unmanned aerial vehicle network route selection method and device and unmanned aerial vehicle node - Google Patents

Unmanned aerial vehicle network route selection method and device and unmanned aerial vehicle node Download PDF

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CN110958661B
CN110958661B CN201911164018.4A CN201911164018A CN110958661B CN 110958661 B CN110958661 B CN 110958661B CN 201911164018 A CN201911164018 A CN 201911164018A CN 110958661 B CN110958661 B CN 110958661B
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node
unmanned aerial
aerial vehicle
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CN110958661A (en
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张奇勋
冯志勇
姜梦磊
尉志青
黄赛
张轶凡
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Beijing University of Posts and Telecommunications
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    • 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
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • 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
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • 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

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Abstract

The embodiment of the invention provides an unmanned aerial vehicle network route selection method, an unmanned aerial vehicle network route selection device and an unmanned aerial vehicle node, which are applied to the technical field of wireless communication and comprise the following steps: when the destination node is judged not to be in the communication range of the unmanned aerial vehicle node, a spatial straight line between the source node and the destination node is used as an optimal virtual routing line, a virtual relay node on the line is determined, a reference node is selected from the virtual relay nodes, the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node are received, the predicted position of the neighbor node in the current time period is determined, the corresponding judgment values are calculated by utilizing the predicted positions, the corresponding judgment values are respectively applied to the utility function, the neighbor node with the minimum utility function value is determined as a next-hop relay node, and the next-hop relay node is triggered to continue routing for the destination node. The scheme solves the problem that the network communication routing link of the unmanned aerial vehicle is unreliable due to the high-speed mobility of the unmanned aerial vehicle.

Description

Unmanned aerial vehicle network route selection method and device and unmanned aerial vehicle node
Technical Field
The invention relates to the technical field of wireless communication, in particular to a method and a device for selecting network routes of an unmanned aerial vehicle and an unmanned aerial vehicle node.
Background
Unmanned aerial vehicles have great application potential in both public and civil areas, such as emergency communication, environmental monitoring, border surveillance and monitoring, aerial photography, and survivor search and rescue in disasters, due to their capabilities of autonomous flight, rapid deployment, and flexible networking. Compared with a single unmanned aerial vehicle system, the multi-unmanned aerial vehicle system can complete tasks more effectively and economically. And in many unmanned aerial vehicle systems, because unmanned aerial vehicle has quick mobility, can lead to unmanned aerial vehicle network topology's dynamic change for unmanned aerial vehicle's route is unstable.
In the prior art, the influence of the fast mobility of the drone on the stability of the routing algorithm is not considered by the drone routing algorithm, which undoubtedly leads to the following problems: due to the high speed mobility of the drone, the network communication routing link of the drone is unreliable.
Disclosure of Invention
The embodiment of the invention aims to provide an unmanned aerial vehicle network route selection method, an unmanned aerial vehicle network route selection device and an unmanned aerial vehicle node, which are used for solving the problem that an unmanned aerial vehicle network communication route link is unreliable due to high-speed mobility of an unmanned aerial vehicle. The specific technical scheme is as follows:
the embodiment of the invention provides an unmanned aerial vehicle network route selection method, which is applied to an unmanned aerial vehicle node and comprises the following steps:
when the unmanned aerial vehicle node is required to establish a route between a source node and a destination node, judging whether the destination node is located in a communication range of the unmanned aerial vehicle node, if so, taking the destination node as a next hop node of the unmanned aerial vehicle node, and finishing route selection;
if not, taking a spatial straight line between the source node and the destination node as an optimal virtual routing line, and determining at least one virtual relay node on the optimal virtual routing line; selecting a reference node from the at least one virtual relay node according to a preset reference node selection rule; determining the predicted position of each neighbor node in the current time period by using the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node; calculating a judgment value corresponding to each neighbor node by using the reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period; and respectively applying the evaluation value corresponding to each neighbor node to a utility function to obtain a utility function value corresponding to each neighbor node, determining the neighbor node with the minimum utility function value as a next hop relay node of the unmanned aerial vehicle node, and triggering the next hop relay node to continue routing selection for the target node.
Optionally, the determining at least one virtual relay node on the optimal virtual routing line includes:
calculating the target number of the virtual relay nodes to be determined by using a first formula;
according to the target quantity, equally dividing the optimal virtual routing line, and determining each equal division point on the optimal virtual routing line as a virtual relay node;
wherein the first formula is:
Figure BDA0002286926010000021
in the formula, NVIs the target number, dSDIs the spatial linear distance between the source node and the destination node, dmaxThe maximum communication distance between the nodes of the unmanned aerial vehicle.
Optionally, the selecting a reference node from the at least one virtual relay node according to a predetermined reference node selection rule includes:
judging whether a distance between a virtual relay node and a target point on an optimal virtual routing line is smaller than or equal to a preset distance, wherein the target point is a perpendicular point of the unmanned aerial vehicle node on the optimal virtual routing line;
if so, taking the next virtual relay node of a first node as a reference node, wherein the distance between the first node and a target point on the optimal virtual routing line is less than or equal to a preset distance;
and if not, selecting a node closest to the destination point from a plurality of virtual relay nodes behind the destination point as a reference node.
Optionally, the determining the predicted position of each neighbor node in the current time period by using the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node includes:
aiming at each neighbor node in the communication range of the unmanned aerial vehicle node, substituting the actual position and the motion state sent by the neighbor node into a probability density function of a Gaussian-Markov model, and determining the predicted position of the neighbor node in the current time period, wherein the predicted position of the neighbor node is a position coordinate enabling the probability density function value to be maximum, and the probability density function is as follows:
Figure BDA0002286926010000031
wherein, p { ((x)n,yn,zn)|(xn-1,yn-1,zn-1) Denotes a coordinate at a position of (x) }n-1,yn-1,zn-1) Upper neighbor node arrival position coordinate (x)n,yn,zn) Probability of (x)n-1,yn-1,zn-1) Is the actual position of the neighboring node, (x)n,yn,zn) For the predicted position of the neighbour node, δ(k)A parameter representing a gaussian distribution with respect to an acceleration in the motion state in a kth unit time step of a current period,
Figure BDA0002286926010000032
is a parameter of a gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure BDA0002286926010000033
is a parameter of a gaussian distribution with respect to the value of the y coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure BDA0002286926010000034
a parameter that is a gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of a current period of time, wherein the current period of time is divided into m unit time steps in advance.
Optionally, the utility function comprises:
Figure BDA0002286926010000035
in the formula (I), the compound is shown in the specification,
Figure BDA0002286926010000036
for the purpose of the utility function, it is,
Figure BDA0002286926010000037
characterizing a function for the distance in the kth unit time step in the current time period,
Figure BDA0002286926010000038
represents a neighbor node RiA duration of connection with the drone node within a current time period,
Figure BDA0002286926010000039
representing an average distance characterization over the persistent connection period, MAX being a predetermined maximum distance value;
wherein the distance characterization function is:
Figure BDA0002286926010000041
in the formula eta1、η2Is a threshold value of the signal-to-noise ratio, η1+η2＝1，
Figure BDA0002286926010000042
As a neighbor node RiIs measured from the predicted location of the destination node D within the kth unit time step within the current time period,
Figure BDA0002286926010000043
as a neighbor node RiWith the reference node VpThe distance in the kth unit time step in the current time period,
Figure BDA0002286926010000044
a maximum distance for directional communication for the drone node; m is the number of time steps in the current time period,
Figure BDA0002286926010000045
each neighbor node of the unmanned aerial vehicle node.
Optionally, after determining the neighbor node with the minimum utility function value as the next-hop relay node for forwarding the data packet, the method further includes:
calculating an angle to be adjusted corresponding to directional communication by using a third formula, adjusting a data sending angle aiming at the directional communication according to the angle to be adjusted, and sending data to the next hop relay node after adjusting the angle;
wherein the third formula is:
Figure BDA0002286926010000046
in the formula, beta is belonged to (0,1), dijDistance of the drone node to the predicted location of the next-hop relay node,
Figure BDA0002286926010000047
the maximum distance of directional communication when the angle to be adjusted of the unmanned aerial vehicle node is theta, beta is dijAnd the above-mentioned
Figure BDA0002286926010000048
With a predetermined safety factor in between.
The embodiment of the invention also provides an unmanned aerial vehicle network route selection method, which comprises the following steps:
when a route between the source node and a destination node needs to be established, the source node in the unmanned aerial vehicle network judges whether the destination node is located in a communication range of the destination node, if so, the destination node is used as a next hop node, and route selection is finished; if not, taking a spatial straight line between a source node and a destination node as an optimal virtual routing line, determining at least one virtual relay node on the optimal virtual routing line, selecting a first reference node from the at least one virtual relay node according to a preset reference node selection rule, and determining the predicted position of each neighbor node in the current time period by using the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node; calculating a judgment value corresponding to each neighbor node by using the first reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the first reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period; respectively applying evaluation values corresponding to all neighbor nodes to a utility function to obtain utility function values corresponding to all neighbor nodes, determining the neighbor node with the minimum utility function value as a next hop relay node of the unmanned aerial vehicle node, and triggering the next hop relay node to continue routing selection for the target node; the neighbor node of the source node is a node located in the communication range of the source node;
each unmanned aerial vehicle node belonging to a next hop relay node in the unmanned aerial vehicle network judges whether the destination node is located in a communication range of the unmanned aerial vehicle node when the unmanned aerial vehicle node is required to establish a route between the source node and the destination node, if so, the destination node is used as the next hop node, and the route selection is finished; if not, taking a spatial straight line between the source node and the destination node as an optimal virtual routing line, determining at least one virtual relay node on the optimal virtual routing line, selecting a second reference node from the at least one virtual relay node according to a preset reference node selection rule, and determining the predicted position of each neighbor node in the current time period by using the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node; calculating the evaluation value corresponding to each neighbor node by using the selected second reference node and the predicted position, wherein the evaluation value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the second reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period; applying the evaluation value corresponding to each neighbor node to a utility function respectively to obtain a utility function value corresponding to each neighbor node, and determining the neighbor node with the minimum corresponding utility function value as a next hop relay node of the unmanned aerial vehicle node; wherein, each adjacent node of this unmanned aerial vehicle node is the node that is located this unmanned aerial vehicle node's communication range.
The embodiment of the invention also discloses an unmanned aerial vehicle network route selecting device, which is applied to the unmanned aerial vehicle node and comprises the following steps:
the judging module is used for judging whether the destination node is positioned in the communication range of the unmanned aerial vehicle node when the unmanned aerial vehicle node is required to establish a route between a source node and the destination node;
the first processing module is used for taking the destination node as a next hop node of the unmanned aerial vehicle node if the judgment module judges that the destination node is the next hop node of the unmanned aerial vehicle node, and finishing the route selection;
the second processing module is used for taking a spatial straight line between the source node and the destination node as an optimal virtual routing line and determining at least one virtual relay node on the optimal virtual routing line if the judgment module judges that the spatial straight line is not the optimal virtual routing line; selecting a reference node from the at least one virtual relay node according to a preset reference node selection rule; determining the predicted position of each neighbor node in the current time period by using the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node; calculating a judgment value corresponding to each neighbor node by using the reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period; and respectively applying the evaluation value corresponding to each neighbor node to a utility function to obtain a utility function value corresponding to each neighbor node, determining the neighbor node with the minimum utility function value as a next hop relay node of the unmanned aerial vehicle node, and triggering the next hop relay node to continue routing selection for the target node.
The embodiment of the invention also provides an unmanned aerial vehicle node, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing the steps of any unmanned aerial vehicle network route selection method provided by the embodiment of the invention when executing the program stored in the memory.
The embodiment of the invention also provides a computer-readable storage medium, which is characterized in that a computer program is stored in the computer-readable storage medium, and the computer program is executed by a processor to realize the steps of any unmanned aerial vehicle network route selection method provided by the embodiment of the invention.
An embodiment of the present invention further provides a computer program product including instructions, which when run on a computer, causes the computer to execute any one of the above methods for network routing for unmanned aerial vehicles.
The embodiment of the invention has the following beneficial effects:
in the scheme provided by the embodiment of the invention, the rapid mobility of the unmanned aerial vehicle is considered, the positions of neighbor nodes of the unmanned aerial vehicle are predicted, an optimal virtual routing line is established, and reference nodes are selected from virtual relay nodes on the optimal virtual routing line according to a preset reference node selection rule; and then calculating evaluation values of the neighbor nodes, applying the evaluation values corresponding to the neighbor nodes to the utility function to obtain utility function values corresponding to the neighbor nodes, selecting the neighbor node with the best performance, namely the neighbor node with the minimum utility function value, and determining the neighbor node as a next hop relay node until the routing of the unmanned aerial vehicle is finished. Therefore, the positions of the neighbor nodes are predicted, the judgment values of the neighbor nodes are calculated by using the predicted positions, the judgment values are applied to the utility function, the neighbor node with the minimum utility function value is selected as the next-hop relay node, the influence of the rapid mobility of the unmanned aerial vehicle on the routing stability is considered, the routing stability is improved, and the reliability of the network communication link of the unmanned aerial vehicle is improved. Therefore, the problem that the network communication routing link of the unmanned aerial vehicle is unreliable due to high-speed mobility of the unmanned aerial vehicle can be solved through the scheme.
Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a spatial distribution model diagram of nodes of an unmanned aerial vehicle provided in an embodiment of the method;
fig. 2 is a diagram of a dynamic antenna model combining a directional antenna and an omnidirectional antenna provided in an embodiment of the method;
fig. 3 is a flowchart of a method for selecting a network route of an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an optimal virtual routing circuit according to an embodiment of the present invention;
fig. 5(a), fig. 5(b), fig. 5(c), and fig. 5(d) are schematic diagrams respectively illustrating selection of a reference node according to an embodiment of the present invention;
fig. 6 is a schematic diagram illustrating angle adjustment of a directional antenna according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an unmanned aerial vehicle network route selection device according to an embodiment of the present invention;
fig. 8 is a schematic diagram illustrating update of node information of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an unmanned aerial vehicle node provided in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the problem that a network communication routing link of an unmanned aerial vehicle is unreliable due to high-speed mobility of the unmanned aerial vehicle, the embodiment of the invention provides a method and a device for selecting network routes of the unmanned aerial vehicle and an unmanned aerial vehicle node.
First, a method for selecting a network route of an unmanned aerial vehicle according to an embodiment of the present invention is described below.
To understand the method of an embodiment of the invention, fig. 1 shows a spatial distribution model of drone nodes in a drone network. As the unmanned aerial vehicle rapidly moves in the three-dimensional space, a three-dimensional unmanned aerial vehicle network space can be used as a flight area of the unmanned aerial vehicle, the flight area is decomposed into N grids numbered from 1 to N,
Figure BDA0002286926010000081
wherein L isminIs the minimum safe distance between unmanned aerial vehicle nodes, Lmin＝Vmax·Δts，VmaxIs the maximum moving speed of the unmanned aerial vehicle node, delta tsRepresenting the safe braking time of the unmanned aerial vehicle. L, W, H are the length, width and height of the flight area, respectively. Assuming that the length, width and height of the grid are all Lmin. So the drone flight area Q can be expressed as:
Figure BDA0002286926010000082
regard unmanned aerial vehicle as the node in unmanned aerial vehicle network space, a plurality of unmanned aerial vehicle nodes are distributed at random in this flight area, and the node set of unmanned aerial vehicle node is expressed as U ═ U0,u1,...,unIf the unmanned plane node exists in the grid i of the unmanned plane flight area Q, the coordinate of the unmanned plane node may be the center coordinate (X) of the grid i where the unmanned plane node existsi,Yi,Zi)。
In the embodiment of the invention, the coordinates of the unmanned aerial vehicle nodes refer to the central coordinates of the grid in which the unmanned aerial vehicle is located. And due to the limitation of the minimum safety distance, at most one unmanned aerial vehicle node can exist in each grid in the same time period. In the embodiment of the invention, the types of the unmanned aerial vehicles can be the same, the communication capacity can be the same, and communication can be realized when the communication distance between all the nodes of the unmanned aerial vehicles is satisfied. In addition, the network space of the unmanned aerial vehicle accords with a free space model, the interference between any two unmanned aerial vehicle nodes is ignored, and then the signal-to-noise ratio SNR of the signal transmitted from the unmanned aerial vehicle node i to the unmanned aerial vehicle node j is determinedijThe calculation formula of (A) is as follows:
Figure BDA0002286926010000083
wherein, PijRepresenting the signal power of the signal transmitted by drone node i to drone node j, dijRepresenting an initial spatial linear distance between drone node i and drone node jIon, HijRepresents the power gain of the small-scale fading channel, alpha is the attenuation index of the large-scale fading model, N0Represents gaussian white noise in the channel over which drone node i and drone node j transmit signals, and obeys a (0, N) distribution.
In addition, the transmission success probability of the signal transmitted from the unmanned aerial vehicle node i to the unmanned aerial vehicle node j is as follows:
Figure BDA0002286926010000091
where η represents the signal-to-noise threshold.
In order to ensure the QoS of the communication link between the drone node i and the drone node j, the success probability of the signal transmission from the drone node i to the drone node j should satisfy:
P(SNRij≥η)≥ψ
where ψ represents a constraint threshold of QoS (Quality of Service) on the SNR transmission probability. It can be seen that the signal can only be successfully received if the transmission success probability is greater than the constraint threshold.
According to the probability formula, when the unmanned aerial vehicle node i and the unmanned aerial vehicle node j transmit signals, the signal power P is obtainedijWhite gaussian noise N in the channel0The attenuation index alpha is constant, and the transmission success probability P (SNR) of the signalijWhen the distance is larger than or equal to eta), the maximum communication distance d between nodes of the unmanned aerial vehicle can be obtainedmaxComprises the following steps:
Figure BDA0002286926010000092
in order to understand the communication scenario between the nodes of the unmanned aerial vehicle in the embodiment of the present invention, as shown in fig. 2, a dynamic antenna model combining a directional antenna and an omnidirectional antenna is proposed. Referring to fig. 2, for drone node RiIn other words, when unmanned plane node RiWhen a next hop relay node needs to be selected, neighbor nodes R which are selectable around in the last time period need to be detectedjThe neighbor node is RjIn the position of
Figure BDA0002286926010000093
And unmanned aerial vehicle node RiIs located at the position
Figure BDA0002286926010000094
It should be noted that, in order to increase the range of the optional neighbor node, omnidirectional communication is adopted, and the spherical dotted line in fig. 2 represents the unmanned aerial vehicle node RiThe range of the omni-directional communication,
Figure BDA0002286926010000095
for the maximum distance of the omni-directional communication,
Figure BDA0002286926010000096
comprises the following steps:
Figure BDA0002286926010000097
wherein the content of the first and second substances,
Figure BDA0002286926010000098
representing interference from nodes other than node j to node i.
By calculating the ratio of RjTo determine RjIs a next hop relay node. Then, at RiTo RjWhen sending data, because the drone has fast mobility, the drone node RiFrom
Figure BDA0002286926010000099
Move to
Figure BDA0002286926010000101
Next hop relay node RjFrom
Figure BDA0002286926010000102
Move to
Figure BDA0002286926010000103
(real). And in order to ensure effective transmission of data, the unmanned aerial vehicle node RiBased on RjPredicted position of
Figure BDA0002286926010000104
(pre) transmitting data to a next hop relay node by using a directional antenna as indicated by a conical dotted line in the figure to increase a transmission distance, thereby prolonging a connection time and a maximum propagation distance of directional communication
Figure BDA0002286926010000105
Comprises the following steps:
Figure BDA0002286926010000106
antenna gain of
Figure BDA0002286926010000107
Where θ is the beamwidth of the main beam and ignores all side lobes. The coverage angle of the directional antenna is [ theta ]min,θmax]And thetamax＝360°。
The following describes a method for selecting a network route of an unmanned aerial vehicle according to an embodiment of the present invention with reference to the accompanying drawings.
The unmanned aerial vehicle network route selection method provided by the embodiment of the invention is applied to an unmanned aerial vehicle node, namely, an execution main body for executing the method is the unmanned aerial vehicle node. For the routing between the source node and the destination node to be established, the drone node serving as the execution subject may be a drone node belonging to the source node and each drone node belonging to the next-hop relay node determined in the routing process. It should be emphasized that, no matter for the drone node belonging to the source node or the drone node belonging to the next-hop relay node, the steps executed in the process of establishing the route between the source node and the destination node are the same, so in the embodiment of the present invention, each drone node having a route selection action is taken as an execution subject to describe the method for selecting the network route of the drone provided in the embodiment of the present invention. The route selected by each unmanned node is the next hop route.
As shown in fig. 3, the method for selecting a network route of an unmanned aerial vehicle provided in the embodiment of the present invention is applied to an unmanned aerial vehicle node, and may include the following steps:
s101, when the unmanned aerial vehicle node is required to establish a route between a source node and a destination node, judging whether the destination node is located in a communication range of the unmanned aerial vehicle node, and if so, executing S102; otherwise, executing S103;
s102, taking the destination node as a next hop node of the unmanned aerial vehicle node, and finishing the route selection;
s103, taking a spatial straight line between the source node and the destination node as an optimal virtual routing line, determining at least one virtual relay node on the optimal virtual routing line, and selecting a reference node from the at least one virtual relay node according to a preset reference node selection rule;
s104, determining the predicted position of each neighbor node in the current time period by using the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node;
s105, calculating the judgment value corresponding to each neighbor node by using the reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period; and respectively applying the evaluation value corresponding to each neighbor node to a utility function to obtain a utility function value corresponding to each neighbor node, determining the neighbor node with the minimum utility function value as a next hop relay node of the unmanned aerial vehicle node, and triggering the next hop relay node to continue routing selection for the target node.
Steps S103 to S105 are routing actions executed when it is determined that the destination node is not located within the communication range of the drone node.
In step S101, it is determined whether the destination node is located within the communication range of the drone node, that is, whether the spatial linear distance between the destination node and the drone node is less than or equal to the maximum communication distance d of the drone nodemax。
In steps S103-S105, when the destination node is not located within the communication range of the drone node, a next hop route is selected.
In step S103, if the destination node is not in the communication range of the drone node, it is known from the distance between the source node and the destination node that the connection needs to be performed by the one-hop relay node at least. Then the best route L is based on the shortest path principleVI.e. the best virtual route line may be a straight line between the source node to the destination node. There are various implementations of determining at least one virtual relay node on the optimal virtual routing line. For example, in an implementation manner, determining at least one virtual relay node on the optimal virtual routing line may include:
calculating the target number of the virtual relay nodes to be determined by using a first formula;
according to the target quantity, equally dividing the optimal virtual routing line, and determining each equal division point on the optimal virtual routing line as a virtual relay node;
wherein the first formula is:
Figure BDA0002286926010000121
in the formula, NVIs the target number, dsDIs the spatial linear distance between the source node and the destination node, dmaxThe maximum communication distance between the nodes of the unmanned aerial vehicle.
Calculating the target number of the virtual relay nodes to be determined according to a first formula, equally dividing the optimal virtual routing line according to the target number, and equally dividing each point on the optimal virtual routing lineIf all the points are determined as virtual relay nodes, the distance d between adjacent virtual relay nodesVComprises the following steps:
Figure BDA0002286926010000122
the optimal virtual route can then be represented as:
Figure BDA0002286926010000123
the source node is the first node, the destination node is the last node, and the virtual relay nodes are arranged according to the sequence from the source node to the destination node.
For ease of understanding, see fig. 4 for a schematic diagram of an optimal virtual routing line. In FIG. 4, V1(S) as source node and simultaneously as first node on the optimal virtual route line, VNv(D) For the destination node, and as the last node on the optimal virtual routing line, the omnidirectional communication distance between the source node and the destination node is
Figure BDA0002286926010000124
Furthermore, since the spatial straight line between the source node and the destination node is divided by 4, five virtual relay nodes are determined on the optimal virtual routing line: v1(S)、Vi-1、Vi、Vi+1And VNv(D) The distance between two adjacent virtual relay nodes is dv。
The above-mentioned determination of the at least one virtual relay node on the optimal virtual routing line is merely an example, and should not constitute a limitation to the embodiments of the present invention. For example: the number of nodes corresponding to the line length range to which the optimal virtual routing line belongs may be selected from a mapping relationship between the number of nodes and the line length range as a target number, and the optimal virtual routing line may be further equally divided according to the target number, and each equal division point on the optimal virtual routing line may be determined as a virtual relay node.
In step S103, there are various specific implementation manners for selecting a reference node from the at least one virtual relay node according to a predetermined reference node selection rule. For example, in an implementation manner, the selecting a reference node from the at least one virtual relay node according to a predetermined reference node selection rule may include:
judging whether a distance between a virtual relay node and a target point on an optimal virtual routing line is smaller than or equal to a preset distance, wherein the target point is a perpendicular point of the unmanned aerial vehicle node on the optimal virtual routing line;
if so, taking the next virtual relay node of a first node as a reference node, wherein the distance between the first node and a target point on the optimal virtual routing line is less than or equal to a preset distance;
and if not, selecting a node closest to the destination point from a plurality of virtual relay nodes behind the destination point as a reference node.
Wherein the predetermined distance is
Figure BDA0002286926010000131
k is the kth time step of the current time period, dVIs the distance of the adjacent virtual relay nodes.
Specifically, in the initial stage of route establishment, the source node is the first node of the virtual route line, i.e. the source node (S) is the virtual route node V1So the source node S and the virtual routing node V1Must be less than or equal to a predetermined distance, so the source node V1(S) with V2Selecting a next hop relay node from neighbor nodes as a reference node;
updating the next hop relay node into the current unmanned aerial vehicle node RpUnmanned aerial vehicle node RpThe selection rule of the reference node is shown in fig. 5:
if unmanned aerial vehicle node RpFrom the source node V in the S-D direction1(S) is not more than
Figure BDA0002286926010000132
Then unmanned plane node RpIs V2As shown in fig. 5 (a);
if unmanned aerial vehicle node RpFrom virtual relay node V in S-D direction2Is less than or equal to
Figure BDA0002286926010000133
Then unmanned plane node RpIs V3As shown in fig. 5 (b);
if unmanned aerial vehicle node RpFrom the source node V in the S-D direction1(S) and virtual relay node V2Are all greater than
Figure BDA0002286926010000134
Then unmanned plane node RpIs V3As above in fig. 5 (c);
summarized as in FIG. 5 (d):
if unmanned aerial vehicle node RpWith virtual relay node V in S-D directioniIs less than or equal to
Figure BDA0002286926010000135
Namely the unmanned plane node RpIn the area I of FIG. 5(d), the virtual relay node V is setiNext virtual relay node V ofi+1As a reference node;
if unmanned aerial vehicle node RpTwo adjacent front and back virtual relay nodes V in the S-D directioniAnd Vi+1Are all greater than
Figure BDA0002286926010000141
I.e. the drone node is in zone ii of fig. 5(d), then V is selectedi+1As a reference node.
In step S104, the determining the predicted position of each neighboring node in the current time period by using the actual position and the motion state sent by each neighboring node in the communication range of the unmanned aerial vehicle node may include:
aiming at each neighbor node in the communication range of the unmanned aerial vehicle node, substituting the actual position and the motion state sent by the neighbor node into a probability density function of a Gaussian-Markov model, and determining the predicted position of the neighbor node in the current time period, wherein the predicted position of the neighbor node is a position coordinate enabling the probability density function value to be maximum, and the probability density function is as follows:
Figure BDA0002286926010000142
wherein, p { ((x)n,yn,zn)|(xn-1,yn-1,zn-1) Denotes a coordinate at a position of (x) }n-1,yn-1,zn-1) Upper neighbor node arrival position coordinate (x)n,yn,zn) Probability of (x)n-1,yn-1,zn-1) Is the actual position of the neighboring node, (x)n,yn,zn) For the predicted position of the neighbour node, δ(k)A parameter representing a gaussian distribution with respect to an acceleration in the motion state in a kth unit time step of a current period,
Figure BDA0002286926010000143
is a parameter of a gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure BDA0002286926010000144
is a parameter of a gaussian distribution with respect to the value of the y coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure BDA0002286926010000145
a parameter that is a gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of a current period of time, wherein the current period of time is divided into m unit time steps in advance.
The probability density function is derived in the following way:
because the unmanned aerial vehicle has fast mobility, the traditional routing algorithm can not adapt to the change of the network topology structure of the unmanned aerial vehicle, and in order to reduce the influence of the fast mobility of the unmanned aerial vehicle on the routing stability, the position distribution of each neighbor node in the communication range of the unmanned aerial vehicle node needs to be predicted. And determining the predicted position of each neighbor node in the current time period aiming at the actual position of each neighbor node in the previous time period.
Defining Δ t as being between tn-1Time t andnstep per time between moments, current time period Δ T ═ Tn-tn-1M Δ t, m represents the number of unit time steps. Suppose the drone node is at tnThe velocities at the time instants follow a random distribution. Thus, the drone node may be considered to be in uniform motion, and the acceleration of the drone node may be considered to be a series of white gaussian noises with constant variance per unit time.
Then, when the kth unit time step within the current time period Δ T is long, the speed and the coordinate of the node of the unmanned aerial vehicle on the x coordinate axis are:
Figure BDA0002286926010000151
Figure BDA0002286926010000152
wherein the content of the first and second substances,
Figure BDA0002286926010000153
is an acceleration component, the parameters of the gaussian distribution about the acceleration in the motion state in the kth unit time step of the current time period are:
Figure BDA0002286926010000154
the values of the x coordinate axis in the actual position and the parameters of the gaussian distribution of the velocity in the motion state in the kth unit time step of the current time period are:
Figure BDA0002286926010000155
the y coordinate axis and can be deduced by the same methodzParameters of Gaussian distribution of coordinate axes
Figure BDA0002286926010000156
And
Figure BDA0002286926010000157
thus, the position coordinate is (x)n-1,yn-1,zn-1) After k unit time steps, the neighbor node of (2) arrives at the position coordinate (x)n,yn,zn) The probability density function of (a) is implemented as:
Figure BDA0002286926010000161
then the neighbor node RiCoordinate position after nth unit time step
Figure BDA0002286926010000162
Comprises the following steps:
Figure BDA0002286926010000163
assuming that the position of the unmanned aerial vehicle in each unit step time is unchanged, the neighbor node R of the unmanned aerial vehicle can be predictediThe moving track in the current time period is as follows:
Figure BDA0002286926010000164
in step S105, the utility function includes:
Figure BDA0002286926010000165
in the formula (I), the compound is shown in the specification,
Figure BDA0002286926010000166
for the purpose of the utility function, it is,
Figure BDA0002286926010000167
characterizing a function for the distance in the kth unit time step in the current time period,
Figure BDA0002286926010000168
represents a neighbor node RiA duration of connection with the drone node within a current time period,
Figure BDA0002286926010000169
representing an average distance characterization over the persistent connection period, MAX being a predetermined maximum distance value;
wherein the distance characterization function is:
Figure BDA00022869260100001610
in the formula eta1、η2Is a threshold value of the signal-to-noise ratio, η1+η2＝1，
Figure BDA00022869260100001611
As a neighbor node RiIs measured from the predicted location of the destination node D within the kth unit time step within the current time period,
Figure BDA00022869260100001612
as a neighbor node RiWith the reference node VpThe distance in the kth unit time step in the current time period,
Figure BDA00022869260100001613
a maximum distance for directional communication for the drone node; m is the number of time steps in the current time period,
Figure BDA00022869260100001614
each neighbor node of the unmanned aerial vehicle node.
Definition of
Figure BDA00022869260100001615
As a neighbor node RiAnd the distance characterization of the destination node D is as follows:
Figure BDA00022869260100001616
therefore, it is not only easy to use
Figure BDA00022869260100001617
Represents the neighbor node R in the kth time step in the current time periodiAnd characterizing the distance from the destination node.
Wherein the neighbor node RiThe continuous connection time with the unmanned aerial vehicle node in the current time period is as follows:
Figure BDA0002286926010000171
in the formula (I), the compound is shown in the specification,
Figure BDA0002286926010000172
a value representing the duration of the connection time,
Figure BDA0002286926010000173
as a neighbor node RiPredicted position and unmanned aerial vehicle node RpIs as follows:
Figure BDA0002286926010000174
MAX is a predetermined maximum distance value, and since the longer the connection is continued, the more stable the route is, the time of connection should be taken as a first consideration. So when
Figure BDA0002286926010000175
When this is the case, a maximum distance value needs to be preset, e.g., MAX 2dSD。
The average distance over the persistent connection period is characterized by:
Figure BDA0002286926010000176
m' is a neighbor node RiAt unmanned aerial vehicle node RpThe number of unit time steps within the communication range. For simplicity of calculation, the value in the first unit time step
Figure BDA0002286926010000177
As a means of doing
Figure BDA0002286926010000178
Because unmanned aerial vehicle's route stability and neighbor node RiDuration of connection with the drone node within a current time period
Figure BDA0002286926010000179
Neighbor node RiWith the reference node VpDistance in current time period
Figure BDA00022869260100001710
Neighbor node RiIs within a current time period from the predicted location of the destination node
Figure BDA00022869260100001711
In respect of, in particular, the duration of the connection
Figure BDA00022869260100001712
The longer, the distance
Figure BDA00022869260100001713
And distance
Figure BDA00022869260100001714
The smaller the route stability of the drone, the better, so a utility function can be applied that considers the duration of the connection and represents the shortest distance
Figure BDA00022869260100001715
Characterize the stability of drone routing.
In addition, after the neighbor node with the minimum deviation degree is determined as a next-hop relay node for forwarding the data packet, the method further includes:
calculating an angle to be adjusted corresponding to directional communication by using a third formula, adjusting a data sending angle aiming at the directional communication according to the angle to be adjusted, and sending data to the next hop relay node after adjusting the angle;
wherein the third formula is:
Figure BDA0002286926010000181
in the formula, beta is belonged to (0,1), dijDistance of the drone node to the predicted location of the next-hop relay node,
Figure BDA0002286926010000182
the maximum distance of directional communication when the angle to be adjusted of the unmanned aerial vehicle node is theta, beta is dijAnd the above-mentioned
Figure BDA0002286926010000183
With a predetermined safety factor in between.
Specifically, as shown in fig. 6, the drone node RiWill depend on the neighbor node Rj1And Rj2The predicted position of (a) to adjust the direction of the directional antenna and the beam width theta, i.e. the fixed beam widthAnd (4) angle towards the antenna. In the figure, theta1For unmanned aerial vehicle node R after adjustmentiTo neighbor node Rj1The beamwidth of the directional antenna when transmitting data,
Figure BDA0002286926010000184
as a neighbor node Rj1Is predicted to be the position of the target,
Figure BDA0002286926010000185
as a neighbor node Rj1The actual position of (a); theta2For unmanned aerial vehicle node RiTo neighbor node Rj2The beamwidth of the directional antenna when transmitting data,
Figure BDA0002286926010000186
as a neighbor node Rj2Is predicted to be the position of the target,
Figure BDA0002286926010000187
as a neighbor node Rj2Actual position of dij1For unmanned aerial vehicle node RiTo neighbor node Rj1Predicted distance of dij2For unmanned aerial vehicle node RiWith neighbor node Rj2Predicted distance of dmax1Is the beam width theta1Time unmanned aerial vehicle node RiMaximum distance of directional communication, dmax2Is the beam width theta2Time unmanned aerial vehicle node RiMaximum distance for directional communication.
If the transmission power is fixed, the maximum transmission distance and the coverage area can be determined by the angle of the antenna in the corresponding direction, and the relationship between the beam width and the maximum distance of directional communication is as follows:
Figure BDA0002286926010000188
in the formula, PijRepresents the signal power of the signal transmitted by the drone node i to the drone node j, psi represents the constraint threshold of the QoS (Quality of Service) on the transmission probability of the signal-to-noise ratio, η represents the threshold of the signal-to-noise ratio, N0To representWhite gaussian noise, U, in the channel over which drone node i and drone node j transmit signalsIIs an unmanned plane node i, alpha is the attenuation index of a large-scale fading model,
Figure BDA0002286926010000189
the maximum propagation distance for directional communication.
Thus, the angle of the directional antenna can be predicted from dijAnd (6) dynamically adjusting. Due to the neighbor node RjThe difference that may exist between the predicted position and the actual position of (c), we propose the predicted distance dijMaximum communication distance with angular adjustment
Figure BDA00022869260100001810
The safety factor β between:
Figure BDA0002286926010000191
wherein 0<β<The values of 1 and beta depend on the moving speed of the neighbor node. The value of β will decrease as the speed of the drone increases. Thus, the distance d can be predicted according toijAnd dynamically adjusting the angle theta of the directional antenna according to the value of the safety coefficient beta.
In the scheme provided by the embodiment of the invention, the rapid mobility of the unmanned aerial vehicle is considered, the positions of neighbor nodes of the unmanned aerial vehicle are predicted, an optimal virtual routing line is established, and reference nodes are selected from virtual relay nodes on the optimal virtual routing line according to a preset reference node selection rule; and then calculating evaluation values of the neighbor nodes, applying the evaluation values corresponding to the neighbor nodes to the utility function to obtain utility function values corresponding to the neighbor nodes, selecting the neighbor node with the best performance, namely the neighbor node with the minimum utility function value, and determining the neighbor node as a next hop relay node until the routing of the unmanned aerial vehicle is finished. Therefore, the positions of the neighbor nodes are predicted, the judgment values of the neighbor nodes are calculated by using the predicted positions, the judgment values are applied to the utility function, the neighbor node with the minimum utility function value is selected as the next-hop relay node, the influence of the rapid mobility of the unmanned aerial vehicle on the routing stability is considered, the routing stability is improved, and the reliability of the network communication link of the unmanned aerial vehicle is improved. Therefore, the problem that the network communication routing link of the unmanned aerial vehicle is unreliable due to high-speed mobility of the unmanned aerial vehicle can be solved through the scheme.
Another method for selecting a network route of an unmanned aerial vehicle provided by the embodiment of the invention is described below. The routing selection method of the unmanned aerial vehicle network is applied to the unmanned aerial vehicle network, and the routing selection process from a source node to a destination node is embodied through steps executed by the source node and a next hop relay node in the unmanned aerial vehicle network.
Another method for selecting network routes of an unmanned aerial vehicle provided by the embodiment of the invention can include the following steps:
when a route between the source node and a destination node needs to be established, the source node in the unmanned aerial vehicle network judges whether the destination node is located in a communication range of the destination node, if so, the destination node is used as a next hop node, and route selection is finished; if not, taking a spatial straight line between a source node and a destination node as an optimal virtual routing line, determining at least one virtual relay node on the optimal virtual routing line, selecting a first reference node from the at least one virtual relay node according to a preset reference node selection rule, and determining the predicted position of each neighbor node in the current time period by using the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node; calculating a judgment value corresponding to each neighbor node by using the first reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the first reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period; respectively applying evaluation values corresponding to all neighbor nodes to a utility function to obtain utility function values corresponding to all neighbor nodes, determining the neighbor node with the minimum utility function value as a next hop relay node of the unmanned aerial vehicle node, and triggering the next hop relay node to continue routing selection for the target node; the neighbor node of the source node is a node located in the communication range of the source node;
each unmanned aerial vehicle node belonging to a next hop relay node in the unmanned aerial vehicle network judges whether the destination node is located in a communication range of the unmanned aerial vehicle node when the unmanned aerial vehicle node is required to establish a route between the source node and the destination node, if so, the destination node is used as the next hop node, and the route selection is finished; if not, taking a spatial straight line between the source node and the destination node as an optimal virtual routing line, determining at least one virtual relay node on the optimal virtual routing line, selecting a second reference node from the at least one virtual relay node according to a preset reference node selection rule, and determining the predicted position of each neighbor node in the current time period by using the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node; calculating a judgment value corresponding to each neighbor node by using the second reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the second reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period; applying the evaluation value corresponding to each neighbor node to a utility function respectively to obtain a utility function value corresponding to each neighbor node, and determining the neighbor node with the minimum corresponding utility function value as a next hop relay node of the unmanned aerial vehicle node; wherein, each adjacent node of this unmanned aerial vehicle node is the node that is located this unmanned aerial vehicle node's communication range.
Specifically, each unmanned aerial vehicle node updates the information of neighbor nodes in the omnidirectional communication range of the unmanned aerial vehicle node in real time, wherein the information comprises current geographic position information and a motion state;
and each unmanned aerial vehicle node can predict the motion trail of the neighbor node in the omnidirectional communication range and store the predicted information.
Each unmanned aerial vehicle node can select utility function in omnidirectional communication range
Figure BDA0002286926010000201
And the neighbor node with the minimum value is filled as the relay node of the next hop until the communication with the destination node is realized.
As shown in the schematic diagram of updating the node information of the unmanned aerial vehicle provided in fig. 8, the source node S needs to communicate with the destination node D to establish an optimal virtual routing line S-D, and determine a virtual relay node { S, V ] on the optimal virtual routing line1,V2,V3D }; each unmanned aerial vehicle selects a neighbor node with the minimum utility function value based on a reference node selected on the optimal virtual routing line, determines the neighbor node as a next hop relay node of the unmanned aerial vehicle node, and triggers the next hop relay node to continue to select the route for the target node until the route selection is finished. The route from the source node S to the destination node D is set as { S, R1,R2,R3,D}。
In the scheme provided by the embodiment of the invention, the rapid mobility of the unmanned aerial vehicle is considered, the positions of neighbor nodes of the unmanned aerial vehicle are predicted, an optimal virtual routing line is established, and reference nodes are selected from virtual relay nodes on the optimal virtual routing line according to a preset reference node selection rule; and then calculating evaluation values of the neighbor nodes, applying the evaluation values corresponding to the neighbor nodes to the utility function to obtain utility function values corresponding to the neighbor nodes, selecting the neighbor node with the best performance, namely the neighbor node with the minimum utility function value, and determining the neighbor node as a next hop relay node until the routing of the unmanned aerial vehicle is finished. Therefore, the positions of the neighbor nodes are predicted, the judgment values of the neighbor nodes are calculated by using the predicted positions, the judgment values are applied to the utility function, the neighbor node with the minimum utility function value is selected as the next-hop relay node, the influence of the rapid mobility of the unmanned aerial vehicle on the routing stability is considered, the routing stability is improved, and the reliability of the network communication link of the unmanned aerial vehicle is improved. Therefore, the problem that the network communication routing link of the unmanned aerial vehicle is unreliable due to high-speed mobility of the unmanned aerial vehicle can be solved through the scheme.
Corresponding to the above method embodiment, an embodiment of the present invention further provides an unmanned aerial vehicle network route selecting device, as shown in fig. 7, where the method may include:
a determining module 710, configured to determine whether a destination node is located within a communication range of a source node and the destination node when the route between the source node and the destination node needs to be established by the drone node;
the first processing module 720 is configured to, if the determination module determines that the destination node is the next hop node of the unmanned aerial vehicle node, take the destination node as the next hop node of the unmanned aerial vehicle node, and end the routing selection;
a second processing module 730, configured to, if the determining module determines that the virtual relay node is not a virtual relay node, determine at least one virtual relay node on an optimal virtual routing line by using a spatial straight line between the source node and the destination node as the optimal virtual routing line; selecting a reference node from the at least one virtual relay node according to a preset reference node selection rule; determining the predicted position of each neighbor node in the current time period by using the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node; calculating a judgment value corresponding to each neighbor node by using the reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period; and respectively applying the evaluation value corresponding to each neighbor node to a utility function to obtain a utility function value corresponding to each neighbor node, determining the neighbor node with the minimum utility function value as a next hop relay node of the unmanned aerial vehicle node, and triggering the next hop relay node to continue routing selection for the target node.
Optionally, the determining, by the second processing module, at least one virtual relay node on the optimal virtual routing line includes:
calculating the target number of the virtual relay nodes to be determined by using a first formula;
according to the target quantity, equally dividing the optimal virtual routing line, and determining each equal division point on the optimal virtual routing line as a virtual relay node;
wherein the first formula is:
Figure BDA0002286926010000221
in the formula, NVIs the target number, dSDIs the spatial linear distance between the source node and the destination node, dmaxThe maximum communication distance between the nodes of the unmanned aerial vehicle.
Optionally, the selecting, by the second processing module, a reference node from the at least one virtual relay node according to a predetermined reference node selection rule includes:
judging whether a distance between a virtual relay node and a target point on an optimal virtual routing line is smaller than or equal to a preset distance, wherein the target point is a perpendicular point of the unmanned aerial vehicle node on the optimal virtual routing line;
if so, taking the next virtual relay node of a first node as a reference node, wherein the distance between the first node and a target point on the optimal virtual routing line is less than or equal to a preset distance;
and if not, selecting a node closest to the destination point from a plurality of virtual relay nodes behind the destination point as a reference node.
Optionally, the determining, by the second processing module, the predicted position of each neighboring node in the current time period by using the actual position and the motion state sent by each neighboring node in the communication range of the drone node includes:
aiming at each neighbor node in the communication range of the unmanned aerial vehicle node, substituting the actual position and the motion state sent by the neighbor node into a probability density function of a Gaussian-Markov model, and determining the predicted position of the neighbor node in the current time period, wherein the predicted position of the neighbor node is a position coordinate enabling the probability density function value to be maximum, and the probability density function is as follows:
Figure BDA0002286926010000231
wherein, p { ((x)n,yn,zn)|(xn-1,yn-1,zn-1) Denotes a coordinate at a position of (x) }n-1,yn-1,zn-1) Upper neighbor node arrival position coordinate (x)n,yn,zn) Probability of (x)n-1,yn-1,zn-1) Is the actual position of the neighboring node, (x)n,yn,zn) For the predicted position of the neighbour node, δ(k)A parameter representing a gaussian distribution with respect to an acceleration in the motion state in a kth unit time step of a current period,
Figure BDA0002286926010000232
is a parameter of a gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure BDA0002286926010000233
is a parameter of a gaussian distribution with respect to the value of the y coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure BDA0002286926010000234
a parameter that is a gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of a current period of time, wherein the current period of time is divided into m unit time steps in advance.
Optionally, the utility function comprises:
Figure BDA0002286926010000235
in the formula (I), the compound is shown in the specification,
Figure BDA0002286926010000236
for the purpose of the utility function, it is,
Figure BDA0002286926010000237
characterizing a function for the distance in the kth unit time step in the current time period,
Figure BDA0002286926010000238
represents a neighbor node RiA duration of connection with the drone node within a current time period,
Figure BDA0002286926010000239
representing an average distance characterization over the persistent connection period, MAX being a predetermined maximum distance value;
wherein the distance characterization function is:
Figure BDA0002286926010000241
in the formula eta1、η2Is a threshold value of the signal-to-noise ratio, η1+η2＝1，
Figure BDA0002286926010000242
As a neighbor node RiIs measured from the predicted location of the destination node D within the kth unit time step within the current time period,
Figure BDA0002286926010000243
as a neighbor node RiWith the reference node VpThe distance in the kth unit time step in the current time period,
Figure BDA0002286926010000244
a maximum distance for directional communication for the drone node; m is the current timeThe number of time steps in a segment,
Figure BDA0002286926010000245
each neighbor node of the unmanned aerial vehicle node.
Optionally, the apparatus further comprises:
a calculation module, configured to calculate, by using a third formula, an angle to be adjusted corresponding to directional communication after the second processing module determines the neighbor node having the smallest utility function value as a next-hop relay node that forwards the data packet, adjust a data transmission angle for the directional communication according to the angle to be adjusted, and transmit data to the next-hop relay node after adjusting the angle;
wherein the third formula is:
Figure BDA0002286926010000246
wherein in the formula, beta ∈ (0,1), dijDistance of the drone node to the predicted location of the next-hop relay node,
Figure BDA0002286926010000247
the maximum distance of directional communication when the angle to be adjusted of the unmanned aerial vehicle node is theta, beta is dijAnd the above-mentioned
Figure BDA0002286926010000248
With a predetermined safety factor in between.
The embodiment of the present invention further provides an unmanned aerial vehicle node, as shown in fig. 9, including a processor 901, a communication interface 902, a memory 903 and a communication bus 904, where the processor 901, the communication interface 902, and the memory 903 complete mutual communication through the communication bus 904,
a memory 903 for storing computer programs;
the processor 901 is configured to implement the steps of selecting a network route of the unmanned aerial vehicle provided by the embodiment of the present invention when executing the program stored in the memory 903.
The communication bus mentioned in the above-mentioned drone node may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the unmanned aerial vehicle node and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
In another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the above methods for network routing for drones.
In yet another embodiment, a computer program product is provided that includes instructions that, when executed on a computer, cause the computer to perform any of the methods of drone network routing of the preceding embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the device and apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (8)

1. The unmanned aerial vehicle network route selection method is applied to unmanned aerial vehicle nodes and comprises the following steps:
when the unmanned aerial vehicle node is required to establish a route between a source node and a destination node, judging whether the destination node is located in a communication range of the unmanned aerial vehicle node, if so, taking the destination node as a next hop node of the unmanned aerial vehicle node, and finishing route selection;
if not, taking a spatial straight line between the source node and the destination node as an optimal virtual routing line, and determining at least one virtual relay node on the optimal virtual routing line;
selecting a reference node from the at least one virtual relay node according to a preset reference node selection rule;
substituting the actual positions and the motion states sent by all neighbor nodes in the communication range of the unmanned aerial vehicle node into a probability density function of a Gaussian-Markov model, and determining the predicted position of the neighbor node in the current time period, wherein the predicted position of the neighbor node is a position coordinate enabling the probability density function value to be maximum, and the probability density function is as follows:
Figure FDA0003186281450000011
wherein, p { ((x)n,yn,zn)|(xn-1,yn-1,zn-1) Denotes a coordinate at a position of (x) }n-1,yn-1,zn-1) Upper neighbor node arrival position coordinate (x)n,yn,zn) Probability of (x)n-1,yn-1,zn-1) Is the actual position of the neighboring node, (x)n,yn,zn) For the predicted position of the neighbour node, δ(k)A parameter representing a gaussian distribution with respect to an acceleration in the motion state in a kth unit time step of a current period,
Figure FDA0003186281450000012
is a parameter of a gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure FDA0003186281450000013
is a parameter of a gaussian distribution with respect to the value of the y coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure FDA0003186281450000014
a parameter that is a gaussian distribution of values about the x coordinate axis in the actual position and the velocity in the motion state within a kth unit time step of a current period of time, wherein the current period of time is divided into m unit time steps in advance;
calculating a judgment value corresponding to each neighbor node by using the reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period;
applying evaluation values corresponding to the neighbor nodes to a utility function respectively to obtain utility function values corresponding to the neighbor nodes, determining the neighbor node with the minimum utility function value as a next hop relay node of the unmanned aerial vehicle node, and triggering the next hop relay node to continue routing selection for the target node, wherein the utility function comprises:
Figure FDA0003186281450000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003186281450000022
for the purpose of the utility function, it is,
Figure FDA0003186281450000023
characterizing a function for the distance in the kth unit time step in the current time period,
Figure FDA0003186281450000024
represents a neighbor node RiA duration of connection with the drone node within a current time period,
Figure FDA0003186281450000025
representing an average distance characterization over the persistent connection period, MAX being a predetermined maximum distance value;
wherein the distance characterization function is:
Figure FDA0003186281450000026
in the formula eta1、η2Is a threshold value of the signal-to-noise ratio, η1+η2＝1，
Figure FDA0003186281450000027
As a neighbor node RiIs measured from the predicted location of the destination node D within the kth unit time step within the current time period,
Figure FDA0003186281450000028
as a neighbor node RiWith the reference node VpThe distance in the kth unit time step in the current time period,
Figure FDA0003186281450000029
a maximum distance for directional communication for the drone node; m is the number of time steps in the current time period,
Figure FDA00031862814500000210
each neighbor node of the unmanned aerial vehicle node.
2. The method of claim 1, wherein the determining at least one virtual relay node on the optimal virtual routing line comprises:
calculating the target number of the virtual relay nodes to be determined by using a first formula;
according to the target quantity, equally dividing the optimal virtual routing line, and determining each equal division point on the optimal virtual routing line as a virtual relay node;
wherein the first formula is:
Figure FDA0003186281450000031
in the formula, NVIs the target number, dSDIs the spatial linear distance between the source node and the destination node, dmaxThe maximum communication distance between the nodes of the unmanned aerial vehicle.
3. The method of claim 1, wherein the selecting a reference node from the at least one virtual relay node according to a predetermined reference node selection rule comprises:
judging whether a distance between a virtual relay node and a target point on an optimal virtual routing line is smaller than or equal to a preset distance, wherein the target point is a perpendicular point of the unmanned aerial vehicle node on the optimal virtual routing line;
if so, taking the next virtual relay node of a first node as a reference node, wherein the distance between the first node and a target point on the optimal virtual routing line is less than or equal to a preset distance;
and if not, selecting a node closest to the destination point from a plurality of virtual relay nodes behind the destination point as a reference node.
4. The method of claim 1, wherein after determining the neighbor node with the smallest utility function value as a next-hop relay node for forwarding the packet, the method further comprises:
calculating an angle to be adjusted corresponding to directional communication by using a third formula, adjusting a data sending angle aiming at the directional communication according to the angle to be adjusted, and sending data to the next hop relay node after adjusting the angle;
wherein the third formula is:
Figure FDA0003186281450000032
in the formula, beta is belonged to (0,1), dijDistance of the drone node to the predicted location of the next-hop relay node,
Figure FDA0003186281450000033
the maximum distance of directional communication when the angle to be adjusted of the unmanned aerial vehicle node is theta, beta is dijAnd the above-mentioned
Figure FDA0003186281450000034
With a predetermined safety factor in between.
5. An unmanned aerial vehicle network route selecting method is characterized by comprising the following steps:
when a route between the source node and a destination node needs to be established, the source node in the unmanned aerial vehicle network judges whether the destination node is located in a communication range of the destination node, if so, the destination node is used as a next hop node, and route selection is finished;
if not, taking a spatial straight line between the source node and the destination node as an optimal virtual routing line, determining at least one virtual relay node on the optimal virtual routing line, selecting a first reference node from the at least one virtual relay node according to a preset reference node selection rule, substituting the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node into a probability density function of a Gaussian-Markov model, and determining the predicted position of the neighbor node in the current time period, wherein the predicted position of the neighbor node is a position coordinate enabling a probability density function value to be maximum, and the probability density function is as follows:
Figure FDA0003186281450000041
wherein, p { ((x)n,yn,zn)|(xn-1,yn-1,zn-1) Denotes a coordinate at a position of (x) }n-1,yn-1,zn-1) Upper neighbor node arrival position coordinate (x)n,yn,zn) Probability of (x)n-1,yn-1,zn-1) Is the actual position of the neighboring node, (x)n,yn,zn) For the predicted position of the neighbour node, δ(k)A parameter representing a gaussian distribution with respect to an acceleration in the motion state in a kth unit time step of a current period,
Figure FDA0003186281450000042
is a parameter of a gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure FDA0003186281450000043
is a parameter of a gaussian distribution with respect to the value of the y coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure FDA0003186281450000044
a parameter that is a gaussian distribution of values about the x coordinate axis in the actual position and the velocity in the motion state within a kth unit time step of a current period of time, wherein the current period of time is divided into m unit time steps in advance;
calculating a judgment value corresponding to each neighbor node by using the first reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the first reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period;
respectively applying evaluation values corresponding to all neighbor nodes to a utility function to obtain utility function values corresponding to all neighbor nodes, determining the neighbor node with the minimum utility function value as a next hop relay node of the unmanned aerial vehicle node, and triggering the next hop relay node to continue routing selection for the target node; the neighbor nodes of the source node are nodes located in the communication range of the source node, wherein the utility function includes:
Figure FDA0003186281450000051
in the formula (I), the compound is shown in the specification,
Figure FDA0003186281450000052
for the purpose of the utility function, it is,
Figure FDA0003186281450000053
characterizing a function for the distance in the kth unit time step in the current time period,
Figure FDA0003186281450000054
represents a neighbor node RiA duration of connection with the drone node within a current time period,
Figure FDA0003186281450000055
representing an average distance characterization over the persistent connection period, MAX being a predetermined maximum distance value;
wherein the distance characterization function is:
Figure FDA00031862814500000510
in the formula eta1、η2Is a threshold value of the signal-to-noise ratio, η1+η2＝1，
Figure FDA0003186281450000056
As a neighbor node RiIs measured from the predicted location of the destination node D within the kth unit time step within the current time period,
Figure FDA0003186281450000057
as a neighbor node RiWith the reference node VpThe distance in the kth unit time step in the current time period,
Figure FDA0003186281450000058
a maximum distance for directional communication for the drone node; m is the number of time steps in the current time period,
Figure FDA0003186281450000059
each neighbor node of the unmanned aerial vehicle node;
each unmanned aerial vehicle node belonging to a next hop relay node in the unmanned aerial vehicle network judges whether the destination node is located in a communication range of the unmanned aerial vehicle node when the unmanned aerial vehicle node is required to establish a route between the source node and the destination node, if so, the destination node is used as the next hop node, and the route selection is finished;
if not, taking a spatial straight line between the source node and the destination node as an optimal virtual routing line, determining at least one virtual relay node on the optimal virtual routing line, selecting a second reference node from the at least one virtual relay node according to a preset reference node selection rule, substituting the actual position and the motion state sent by each neighbor node in the communication range of the unmanned aerial vehicle node into a probability density function of a Gaussian-Markov model, determining the predicted position of the neighbor node in the current time period, wherein the predicted position of the neighbor node is a position coordinate enabling the probability density function value to be maximum, and the probability density function is as follows:
Figure FDA0003186281450000061
wherein, p { ((x)n,yn,zn)|(xn-1,yn-1,zn-1) Denotes a coordinate at a position of (x) }n-1,yn-1,zn-1) Upper neighbor node arrival position coordinate (x)n,yn,zn) Probability of (x)n-1,yn-1,zn-1) Is the actual position of the neighboring node, (x)n,yn,zn) For the predicted position of the neighbour node, δ(k)A parameter representing a gaussian distribution with respect to an acceleration in the motion state in a kth unit time step of a current period,
Figure FDA0003186281450000062
is a parameter of a gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure FDA0003186281450000063
is a parameter of a gaussian distribution with respect to the value of the y coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure FDA0003186281450000064
a parameter that is a gaussian distribution of values about the x coordinate axis in the actual position and the velocity in the motion state within a kth unit time step of a current period of time, wherein the current period of time is divided into m unit time steps in advance;
calculating a judgment value corresponding to each neighbor node by using the second reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the second reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period;
applying the evaluation value corresponding to each neighbor node to a utility function respectively to obtain a utility function value corresponding to each neighbor node, and determining the neighbor node with the minimum corresponding utility function value as a next hop relay node of the unmanned aerial vehicle node; wherein, each adjacent node of this unmanned aerial vehicle node is the node that is located this unmanned aerial vehicle node's communication range, wherein, utility function includes:
Figure FDA0003186281450000071
in the formula (I), the compound is shown in the specification,
Figure FDA0003186281450000072
for the purpose of the utility function, it is,
Figure FDA0003186281450000073
characterizing a function for the distance in the kth unit time step in the current time period,
Figure FDA0003186281450000074
represents a neighbor node RiA duration of connection with the drone node within a current time period,
Figure FDA0003186281450000075
representing an average distance characterization over the persistent connection period, MAX being a predetermined maximum distance value;
wherein the distance characterization function is:
Figure FDA0003186281450000076
in the formula eta1、η2Is a threshold value of the signal-to-noise ratio, η1+η2＝1，
Figure FDA0003186281450000077
As a neighbor node RiIs measured from the predicted location of the destination node D within the kth unit time step within the current time period,
Figure FDA0003186281450000078
as a neighbor node RiWith the reference node VpThe distance in the kth unit time step in the current time period,
Figure FDA0003186281450000079
a maximum distance for directional communication for the drone node; m is the number of time steps in the current time period,
Figure FDA00031862814500000710
each neighbor node of the unmanned aerial vehicle node.
6. The utility model provides an unmanned aerial vehicle network route selects device which characterized in that is applied to the unmanned aerial vehicle node, includes:
the judging module is used for judging whether the destination node is positioned in the communication range of the unmanned aerial vehicle node when the unmanned aerial vehicle node is required to establish a route between a source node and the destination node;
the first processing module is used for taking the destination node as a next hop node of the unmanned aerial vehicle node if the judgment module judges that the destination node is the next hop node of the unmanned aerial vehicle node, and finishing the route selection;
the second processing module is used for taking a spatial straight line between the source node and the destination node as an optimal virtual routing line and determining at least one virtual relay node on the optimal virtual routing line if the judgment module judges that the spatial straight line is not the optimal virtual routing line;
selecting a reference node from the at least one virtual relay node according to a preset reference node selection rule;
substituting the actual positions and the motion states sent by all neighbor nodes in the communication range of the unmanned aerial vehicle node into a probability density function of a Gaussian-Markov model, and determining the predicted position of the neighbor node in the current time period, wherein the predicted position of the neighbor node is a position coordinate enabling the probability density function value to be maximum, and the probability density function is as follows:
Figure FDA0003186281450000081
wherein, p { ((x)n,yn,zn)|(xn-1,yn-1,zn-1) Denotes a coordinate at a position of (x) }n-1,yn-1,zn-1) Upper neighbor node arrival position coordinate (x)n,yn,zn) Probability of (x)n-1,yn-1,zn-1) Is the actual position of the neighboring node, (x)n,yn,zn) For the predicted position of the neighbour node, δ(k)A parameter representing a gaussian distribution with respect to an acceleration in the motion state in a kth unit time step of a current period,
Figure FDA0003186281450000082
to be at presentA parameter of a Gaussian distribution with respect to the value of the x coordinate axis in the actual position and the velocity in the motion state for the kth unit time step of a time period,
Figure FDA0003186281450000083
is a parameter of a gaussian distribution with respect to the value of the y coordinate axis in the actual position and the velocity in the motional state in the kth unit time step of the current time period,
Figure FDA0003186281450000084
a parameter that is a gaussian distribution of values about the x coordinate axis in the actual position and the velocity in the motion state within a kth unit time step of a current period of time, wherein the current period of time is divided into m unit time steps in advance;
calculating a judgment value corresponding to each neighbor node by using the reference node and the predicted position, wherein the judgment value corresponding to any neighbor node comprises: the distance between the neighbor node and a target node, the distance between the neighbor node and the reference node and the continuous connection time between the neighbor node and the unmanned aerial vehicle node in the current time period;
applying evaluation values corresponding to the neighbor nodes to a utility function respectively to obtain utility function values corresponding to the neighbor nodes, determining the neighbor node with the minimum utility function value as a next hop relay node of the unmanned aerial vehicle node, and triggering the next hop relay node to continue routing and selecting the destination node, wherein the utility function comprises:
Figure FDA0003186281450000085
in the formula (I), the compound is shown in the specification,
Figure FDA0003186281450000091
for the purpose of the utility function, it is,
Figure FDA0003186281450000092
characterizing a function for the distance in the kth unit time step in the current time period,
Figure FDA0003186281450000093
represents a neighbor node RiA duration of connection with the drone node within a current time period,
Figure FDA0003186281450000094
representing an average distance characterization over the persistent connection period, MAX being a predetermined maximum distance value;
wherein the distance characterization function is:
Figure FDA0003186281450000095
in the formula eta1、η2Is a threshold value of the signal-to-noise ratio, η1+η2＝1，
Figure FDA0003186281450000096
As a neighbor node RiIs measured from the predicted location of the destination node D within the kth unit time step within the current time period,
Figure FDA0003186281450000097
as a neighbor node RiWith the reference node VpThe distance in the kth unit time step in the current time period,
Figure FDA0003186281450000098
a maximum distance for directional communication for the drone node; m is the number of time steps in the current time period,
Figure FDA0003186281450000099
each neighbor node of the unmanned aerial vehicle node.
7. An unmanned aerial vehicle node is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for finishing mutual communication through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 4 when executing a program stored in the memory.
8. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 4.
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