CN110891293A - Multi-attribute network selection method based on vehicle track prediction - Google Patents

Multi-attribute network selection method based on vehicle track prediction Download PDF

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CN110891293A
CN110891293A CN201911093932.4A CN201911093932A CN110891293A CN 110891293 A CN110891293 A CN 110891293A CN 201911093932 A CN201911093932 A CN 201911093932A CN 110891293 A CN110891293 A CN 110891293A
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张登银
丁齐
丁飞
赵莎莎
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/08—Reselecting an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/16—Performing reselection for specific purposes
    • H04W36/165—Performing reselection for specific purposes for reducing network power consumption
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/32—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]

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Abstract

The invention discloses a vehicle track prediction-based multi-attribute network selection method in the technical field of vehicle networking information transmission, and aims to solve the technical problem that in the prior art, a vehicle-mounted mobile terminal can receive network signals covered by a plurality of different base stations, and due to the fact that the mobility of vehicles in the vehicle networking is large and the movement speed is high, if the time for accessing a certain network is too short, frequent switching of the network is easily caused, and signals are unstable. The method comprises the following steps: predicting the track and the speed of the vehicle; predicting residence time of the vehicle in different networks based on the track and the vehicle speed; comparing the residence time with a preset threshold value, and extracting the network with the residence time larger than the preset threshold value as a candidate network; and selecting an access network from the candidate networks.

Description

Multi-attribute network selection method based on vehicle track prediction
Technical Field
The invention relates to a vehicle trajectory prediction-based multi-attribute network selection method, and belongs to the technical field of vehicle networking information transmission.
Background
There are many different wireless communication technologies in the internet of vehicles, including Wireless Local Area Networks (WLANs), 4/5G networks, dedicated short range communication technologies (DSRC), and satellite communication networks, among others, with different networks possessing different network properties. The bandwidth and the spectrum resources in the wireless network are limited, in a hot spot area covered by multiple wireless networks simultaneously, the vehicle-mounted mobile terminal needs to select a proper network according to the service requirement, and on the premise of meeting the real-time information interaction and sharing of the vehicle-mounted mobile terminal in the internet of vehicles, the utilization rate of the wireless resources is saved as much as possible, and the access efficiency of the wireless network is improved. The vehicle-mounted mobile terminal can receive network signals covered by a plurality of different base stations, and due to the fact that vehicles in the vehicle networking are high in mobility and high in movement speed, if the time for accessing a certain network is too short, frequent switching of the network is easily caused, and the problem of signal instability, namely ping-pong effect, can be caused.
Disclosure of Invention
The invention aims to provide a vehicle trajectory prediction-based multi-attribute network selection method, aiming at solving the technical problems that in the prior art, a vehicle-mounted mobile terminal can receive network signals covered by a plurality of different base stations, and signals are unstable due to the fact that vehicles in a vehicle networking have high mobility and high movement speed, and frequent switching of the networks is easily caused if the time for accessing a certain network is too short.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a multi-attribute network selection method based on vehicle track prediction comprises the following steps:
predicting the track and the speed of the vehicle;
predicting residence time of the vehicle in different networks based on the track and the vehicle speed;
comparing the residence time with a preset threshold value, and extracting the network with the residence time larger than the preset threshold value as a candidate network;
and selecting an access network from the candidate networks.
Preferably, predicting the trajectory of the vehicle and the vehicle speed comprises:
predicting a trajectory of the vehicle based on the Markov chain;
predicting a road section on which the vehicle is going to pass based on the predicted track;
acquiring vehicle speed information on the road section;
and predicting the speed of the vehicle when the vehicle passes through the road section based on the speed information.
Preferably, predicting the trajectory of the vehicle based on the markov chain comprises:
acquiring one-step transition probability of the vehicle at any intersection based on the historical track data of the vehicle;
acquiring a one-step transition probability matrix of the vehicle at all intersections based on the one-step transition probability;
and predicting the track of the vehicle based on the one-step transition probability matrix.
Preferably, the expression of the one-step transition probability matrix is as follows:
Figure BDA0002267714700000021
wherein,
Figure BDA0002267714700000022
wherein P is a one-step transition probability matrix of the vehicle, M is the number of intersections, and PijThe transition probability of the intersection i to the intersection j is one step, N (i, j) is the number of times of transition from the intersection i to the intersection j counted based on the historical track data, and N (i) is the number of times of passing through the intersection i counted based on the historical track data.
Preferably, predicting the trajectory of the vehicle based on the one-step transition probability matrix comprises:
acquiring a row number i in the one-step transition probability matrix P based on the matching of the current position of the vehicle;
comparing the ith row elements in the one-step transition probability matrix P, and extracting the element with the maximum one-step transition probability according to the comparison result;
extracting the train number of the element with the maximum one-step transition probability as the current position of the vehicle;
and iterating the process to obtain the track of the vehicle.
Preferably, predicting residence times of the vehicle in different networks based on the trajectory and the vehicle speed comprises:
acquiring coverage ranges of different networks on the road section based on the base station attribute;
and acquiring the residence time of the vehicle in different networks based on the predicted speed of the vehicle passing through the road section and the acquired coverage areas of the different networks on the road section.
Preferably, selecting an access network from the candidate networks includes:
selecting the attributes of the candidate network and obtaining the attribute vector of the candidate network, wherein the attributes comprise at least any two items of transmission rate, transmission time delay, transmission power and interruption probability;
based on the attribute vector, calculating a weighted attribute vector of the candidate network by using an analytic hierarchy process;
based on the weighted attribute vector, calculating a comprehensive index value of the candidate network;
and selecting the candidate network with the maximum comprehensive index value as an access network.
Preferably, the obtaining of the weighted attribute vector of the candidate network by using an analytic hierarchy process based on the attribute vector comprises:
defining the relative importance degree of every two attributes in any candidate network;
and calculating the weighted attribute vector of the candidate network based on the relative importance degree and the attribute vector.
Preferably, the obtaining of a synthetic index value of the candidate network based on the weighted attribute vector includes:
defining the relative dependence degree of each two candidate networks on any attribute;
solving a comprehensive result matrix based on the relative dependence degree and the weighted attribute vector;
and the comprehensive index value is the sum of all elements of the row vector of any row in the comprehensive result matrix.
Compared with the prior art, the invention has the following beneficial effects: the method adopts the Markov chain to predict the vehicle track, then calculates the residence time according to the predicted track, and eliminates the network with the residence time lower than the preset threshold value. The method has the advantages that the network which is easy to generate the ping-pong effect is removed in advance, the network which cannot generate the ping-pong effect is selected in a centralized way, the stability of subsequent network connection is ensured, and the network selection efficiency is also improved. Then, the network selection is carried out by adopting an analytic hierarchy process, the influence of each attribute of the network on the final selection cannot be segmented by the method, and the weight of each layer influences the final selection. The selected optimal network is guaranteed to be superior to other candidate networks not only in one or more attributes, but also in the whole after being subjected to multi-aspect weighted comparison.
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FIG. 1 is a schematic view of an intersection where a vehicle is located in an embodiment of the present invention;
FIG. 2 is a schematic diagram of the coverage of a network where a vehicle is located according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a hierarchical structure according to an embodiment of the present invention;
FIG. 4 is a flow chart of an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The specific implementation mode of the invention provides a vehicle trajectory prediction-based multi-attribute network selection method, as shown in fig. 4, which is a schematic flow chart of an embodiment of the invention, and the method comprises the following steps:
step one, track prediction based on Markov chain
The probability of the vehicle to the next state position is predicted mainly by means of a state transition probability matrix. The positions of vehicles in the internet of vehicles are not discrete, but each intersection can be used as a state point for prediction. As shown in fig. 1, which is a schematic diagram of an intersection where a vehicle is located in the embodiment of the present invention, a process of driving the vehicle from the intersection X to the intersection O is referred to as: the vehicle travels in the direction XO. If the vehicle is about to pass through intersection O at the next time, the vehicle will have three driving possibilities next, namely: OX direction, OY direction and OZ direction. The process can convert the trajectory prediction of the vehicle into a point-to-point problem of selecting different driving directions;
suppose that the vehicle is traveling through the position information { X) of each intersectionnN ∈ N } is a random sequence of discrete state spaces, N ═ 1, 2. According to the nature of the Markov chain, it can be derived that:
P(Xn+1|...，Xn-2,Xn-1,Xn)＝P(Xn+1|Xn) (1)
in the formula, P (X)n+1|Xn) Is composed of XnTransfer to Xn+1Probability of, next state Xn+1The probability of occurrence is only with the current state XnRegardless of the previous state. The one-step transition probability of the vehicle transitioning from the state i to the state j at the time n is as follows:
pij(n)＝P(Xn+1＝j|Xn＝i) (2)
in the formula, pij(n) is abbreviated as pij. P can be obtained through statistics of a large number of moving historical track data of the vehicleijThe specific process is as follows: assuming that N (i, j) is the counted number of times that the intersection i turns to the intersection j, and N (i) is the number of times that a certain intersection i is passed, then:
Figure BDA0002267714700000041
if the two intersections are not connected, pij0. All one step transition probability PijThe formed matrix is called a one-step transition probability matrix at a certain time and is marked as P. Assuming that there are M intersections, the one-step transition probability matrix P is an M × M second-order matrix, and its expression is as follows:
Figure BDA0002267714700000051
for each vehicle, a one-step transition probability matrix may be derived from its historical trajectory. Scanning a row number in the one-step transition probability matrix P according to the current position, comparing each element in the ith row corresponding to the previous position to obtain a column number j of the element with the maximum one-step transition probability, wherein the position corresponding to the j is the predicted position of the next state of the vehicle; and then scanning the row number in the one-step transition probability matrix P again according to the predicted position, and carrying out iterative solution to obtain the future movement track of the vehicle.
Step two, estimating the residence time of the vehicle in the network
Since the moving track of the vehicle at the next intersection can be predicted by vehicle track prediction, it can be considered that the road section on which the vehicle is traveling is known; meanwhile, the network coverage area can be known according to the base station attribute, so that the coverage areas of different networks on a road section can be obtained. And then, the estimated vehicle speed of the front road section can be comprehensively measured and calculated according to the vehicle speed information reported by a plurality of vehicles in front in the vehicle networking range, so that the residence time of the vehicles in the front network can be calculated.
In order to select an access network from a plurality of networks covering a road section, a threshold value of residence time can be preset, if the residence time of a vehicle in a certain network coverage range is higher than the threshold value, the residence time of the vehicle in the network coverage area is longer, a ping-pong effect is not easy to generate, and the network can be used as a candidate network; and otherwise, the vehicle is indicated to stay in the coverage range of the network for a short time and is positioned at the edge of the network, and the network is excluded. As shown in fig. 2, which is a schematic diagram of network coverage where a vehicle is located in the embodiment of the present invention, the vehicle travels on an XO road, it is assumed that the probability that the vehicle travels to an intersection Y at an intersection O after trajectory prediction is the largest, and it is assumed that signal coverage of a network E, F, G, H exists in an OY road section, and lengths of roads covered by the four networks are L respectivelyE、LF、LG、LHIf the estimated average speed of the OY road section is VThe residence time of the vehicle in the different networks can be calculated, and the network with the residence time larger than a preset threshold value is selected as a candidate network;
the residence time of the vehicle in the network E, F, G, H is, in order: l isE/V、LF/V、LG/V、LH/V。
Step three, determining an access network according to an Analytic Hierarchy Process (AHP)
For candidate networks, the optimal network for final access can be determined by comparing the attributes of different networks through an analytic hierarchy process. Firstly, the problem influencing network selection is decomposed into attributes of the network, and then the relative importance of the attributes is determined by pairwise comparison according to the network attributes and the degree of dependence of the network on the attributes, so that the degree of influence of the attributes on the network can be obtained, and the weight of the attributes in the network can be solved. And comparing the influence degree of each single attribute on different candidate networks, and giving quantitative representation. And finally substituting the specific network attribute values into the network attribute values to calculate to obtain the optimal network in the candidate networks to access.
Fig. 3 is a schematic diagram of a hierarchical structure according to an embodiment of the present invention, and it is assumed that E, F, G networks are selected as candidate networks. An attribute that generally has a large impact on the network is the transmission rate (A)1) Propagation delay (A)2) A transmission power (A)3) And interruption probability (A)4) Wherein the transmission delay (A)2) And interruption probability (A)4) For negative effects, the lower the value, the better, so the attribute value is given as a negative number. The values of the attributes of the network E, F, G are shown in Table 1;
table 1: network attribute value
Figure BDA0002267714700000061
Defining a candidate network as an attribute vector
Figure BDA0002267714700000062
The attribute vector of network E, F, G is in turn
Figure BDA0002267714700000063
The steps for determining the optimal access network with different network attributes are as follows:
firstly, a network attribute judgment matrix A is constructed. The scale definition of the network attribute judgment matrix is shown in Table 2, and the scale value aijIs attribute AiPhase contrast attribute AjRelative importance of. Suppose transmission delay A of the Internet of vehicles access network2The degree of importance of is the transmission rate A1Twice, then there are:
a21＝2，a21＝1/2；
table 2: network attribute decision matrix scale definition
Figure BDA0002267714700000064
Figure BDA0002267714700000071
According to the influence degree of the four attributes of the standard layer on the target layer network access in fig. 3, a network attribute judgment matrix a is established (a)ij)N×NAnd i, j is 1,2, … N, then:
Figure BDA0002267714700000072
Figure BDA0002267714700000073
where N is the number of the selected network attributes, and in this embodiment, N is 4.
Figure BDA0002267714700000074
A standardized matrix of matrix a is determined for the network attributes,
Figure BDA0002267714700000075
wherein the element is denoted as a'ijNamely:
Figure BDA0002267714700000077
secondly, calculating and acquiring a candidate network access characteristic vector W ═ a1,a2,…,ai,…,aN) I.e. the weight representation of each attribute in the network, the ith element a in WiComprises the following steps:
Figure BDA0002267714700000076
thirdly, calculating the maximum eigenvalue lambda of the candidate network access eigenvector WmaxThe formula is as follows:
Figure BDA0002267714700000081
wherein the vector AW is the product of the network attribute judgment matrix A and the candidate network access characteristic vector W, (AW)iIs the ith element of the vector AW.
And fourthly, carrying out consistency check. The average random consistency indicator RI value is generally defined as shown in Table 3;
table 3: index of consistency
Order of the scale 1 2 3 4 5 6 7 8 9 10
RI value 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 0.46
Figure BDA0002267714700000082
Figure BDA0002267714700000083
In the formula, CI is a consistency index, and CR is a consistency ratio. If CR < 0.1, consistency is satisfied. If the consistency check is not satisfied, the numerical value of the network attribute judgment matrix needs to be adjusted until the consistency check is satisfied.
The fifth step, construct the network judgment matrix, the scale definition of which is shown in Table 4, and the scale value bijIs the relative degree of dependence of two networks on a certain attribute. Assuming dependence of the network E on the transmission rateThe degree of dependence is twice that of network F, then there are:
bEFb is 2, on the contraryEF＝1/2；
Table 4: network decision matrix scale definition
Scale value bij
Of equal importance 1
Of slight importance 3
Of importance 5
Is very important 7
Of utmost importance 9
Median value 2,4,6,8
According to the dependence degree of the three candidate networks of the decision layer in the figure 3 on the first attribute (transmission rate) of the standard layer, a network judgment matrix B for pairwise comparison is constructed1＝(bij)3×3Where i, j correspond to E, F, G networks. Then, a network judgment matrix B is obtained1Characteristic vector theta for transmission rate1. In the same way, according to the three waiting periodsThe network selection process includes the steps of selecting the dependence degree of the network on the other three attributes (transmission delay, transmitting power and interruption probability), and sequentially constructing a judgment matrix B2、B3、B4And respectively calculating the characteristic vector theta of each judgment matrix for three attributes2、θ3、θ4. Finally, the four eigenvectors are combined into a matrix R, i.e., R ═ theta1,θ2,θ3,θ4)3×4。
And sixthly, referring to the third step and the fourth step, and performing consistency check. If each candidate network meets the consistency check, turning to the seventh step; if the consistency check can not be completely met, the scale definition of the network judgment matrix needs to be adjusted until the consistency check is completely met.
The seventh step, calculating the weighted attribute vector deltaE、δF、δGThe expression is as follows:
Figure BDA0002267714700000091
in the formula, denotes a Hadamard product. DeltaE、δF、δGThe resultant matrix is K ═ δE,δF,δG)4×3。
Eighthly, performing Hadamard product calculation on the transpose of the matrix R and the matrix K to obtain a comprehensive result matrix S, wherein the expression is as follows:
S＝R*KT(12)
adding all elements of the row vector of each row of the matrix S to obtain the comprehensive index value S of the E, F, G network in turnE、SF、SGComparison SE、SF、SGThe network corresponding to the maximum value is the optimal network.
The method adopts the Markov chain to predict the vehicle track, then calculates the residence time according to the predicted track, and eliminates the network with the residence time lower than the preset threshold value. The method has the advantages that the network which is easy to generate the ping-pong effect is removed in advance, the network which cannot generate the ping-pong effect is selected in a centralized way, the stability of subsequent network connection is ensured, and the network selection efficiency is also improved. Then, the network selection is carried out by adopting an analytic hierarchy process, the influence of each attribute of the network on the final selection cannot be segmented by the method, and the weight of each layer influences the final selection. The selected network is guaranteed to be better than other candidate networks not only in one or more attributes, but also in the whole after the weighted comparison in multiple aspects.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (9)

1. A multi-attribute network selection method based on vehicle track prediction is characterized by comprising the following steps:
predicting the track and the speed of the vehicle;
predicting residence time of the vehicle in different networks based on the track and the vehicle speed;
comparing the residence time with a preset threshold value, and extracting the network with the residence time larger than the preset threshold value as a candidate network;
and selecting an access network from the candidate networks.
2. The vehicle trajectory prediction-based multi-attribute network selection method of claim 1, wherein predicting the trajectory and speed of the vehicle comprises:
predicting a trajectory of the vehicle based on the Markov chain;
predicting a road section on which the vehicle is going to pass based on the predicted track;
acquiring vehicle speed information on the road section;
and predicting the speed of the vehicle when the vehicle passes through the road section based on the speed information.
3. The vehicle trajectory prediction-based multi-attribute network selection method of claim 2, wherein predicting the trajectory of the vehicle based on a markov chain comprises:
acquiring one-step transition probability of the vehicle at any intersection based on the historical track data of the vehicle;
acquiring a one-step transition probability matrix of the vehicle at all intersections based on the one-step transition probability;
and predicting the track of the vehicle based on the one-step transition probability matrix.
4. The vehicle trajectory prediction-based multi-attribute network selection method according to claim 3, wherein the expression of the one-step transition probability matrix is as follows:
Figure FDA0002267714690000011
wherein,
Figure FDA0002267714690000012
wherein P is a one-step transition probability matrix of the vehicle, M is the number of intersections, and PijThe transition probability of the intersection i to the intersection j is one step, N (i, j) is the number of times of transition from the intersection i to the intersection j counted based on the historical track data, and N (i) is the number of times of passing through the intersection i counted based on the historical track data.
5. The vehicle trajectory prediction-based multi-attribute network selection method of claim 4, wherein predicting the trajectory of the vehicle based on the one-step transition probability matrix comprises:
acquiring a row number i in the one-step transition probability matrix P based on the matching of the current position of the vehicle;
comparing the ith row elements in the one-step transition probability matrix P, and extracting the element with the maximum one-step transition probability according to the comparison result;
extracting the train number of the element with the maximum one-step transition probability as the current position of the vehicle;
and iterating the process to obtain the track of the vehicle.
6. The vehicle trajectory prediction-based multi-attribute network selection method of claim 2, wherein predicting vehicle residence times in different networks based on the trajectory and vehicle speed comprises:
acquiring coverage ranges of different networks on the road section based on the base station attribute;
and acquiring the residence time of the vehicle in different networks based on the predicted speed of the vehicle passing through the road section and the acquired coverage areas of the different networks on the road section.
7. The vehicle trajectory prediction-based multi-attribute network selection method according to any one of claims 1 to 6, wherein selecting an access network from the candidate networks comprises:
selecting the attributes of the candidate network and obtaining the attribute vector of the candidate network, wherein the attributes comprise at least any two items of transmission rate, transmission time delay, transmission power and interruption probability;
based on the attribute vector, calculating a weighted attribute vector of the candidate network by using an analytic hierarchy process;
based on the weighted attribute vector, calculating a comprehensive index value of the candidate network;
and selecting the candidate network with the maximum comprehensive index value as an access network.
8. The method of claim 7, wherein the step of using an analytic hierarchy process to find the weighted attribute vector of the candidate network based on the attribute vector comprises:
defining the relative importance degree of every two attributes in any candidate network;
and calculating the weighted attribute vector of the candidate network based on the relative importance degree and the attribute vector.
9. The method of claim 7, wherein the obtaining a composite index value for the candidate network based on the weighted attribute vector comprises:
defining the relative dependence degree of each two candidate networks on any attribute;
solving a comprehensive result matrix based on the relative dependence degree and the weighted attribute vector;
and the comprehensive index value is the sum of all elements of the row vector of any row in the comprehensive result matrix.
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