CN108650141B - Large-scale network accessibility model design method based on Internet of vehicles communication base - Google Patents

Large-scale network accessibility model design method based on Internet of vehicles communication base Download PDF

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CN108650141B
CN108650141B CN201810488727.7A CN201810488727A CN108650141B CN 108650141 B CN108650141 B CN 108650141B CN 201810488727 A CN201810488727 A CN 201810488727A CN 108650141 B CN108650141 B CN 108650141B
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程久军
黄震华
陈敏军
余润身
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Tongji University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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Abstract

A large-scale network accessibility model based on a vehicle networking connectivity base. The method is characterized in that the redundancy characteristic of the nodes of the Internet of vehicles network is considered, a topology structure of the Internet of vehicles network, namely an Internet of vehicles communication base, is provided, and a communication base construction method is provided by utilizing a heuristic algorithm. The method is based on the internal structure attribute and the dynamic characteristic of a communication base, combines a smooth Gauss-half Markov movement model, proves the connectivity and the stability of the large-scale network of the Internet of vehicles, provides an accessibility theoretical model of the Internet of vehicles, can accurately evaluate the network connectivity and the stability under the high dynamic environment of the Internet of vehicles, provides theoretical support for a network layer, provides real-time data guarantee for an application layer, and ensures that important information, such as traffic accident information, real-time media data and the like, can be stably and durably transmitted under the high dynamic network environment of the Internet of vehicles, thereby having important theoretical and practical application values.

Description

Large-scale network accessibility model design method based on Internet of vehicles communication base
Technical Field
The invention relates to the field of a complex network of a vehicle networking.
Background
In recent years, the research on the performance of the internet of vehicles by scholars at home and abroad mainly focuses on a certain scene or network connectivity research based on certain assumed conditions, and the existing internet of vehicles connectivity research mainly focuses on two aspects: (1) end-to-end connectivity; (2) and (4) network overall connectivity.
The current end-to-segment connectivity research mainly researches the connectivity probability between two nodes in a dynamic network environment. Such research generally aims at a certain road section in a highway or urban scene, models the road section into a 1-D network space, and analyzes the relation between the connectivity probability and the vehicle density according to a certain node distribution model such as random distribution, average distribution or Poisson distribution. However, in these researches, on the premise that the distribution of vehicle nodes belongs to a certain mathematical distribution model, the relationship between the end-to-end connectivity probability and the node density between two nodes in a highway scene or a road section in a city is researched, and the overall connectivity and stability of the network are not researched from the large-scale network scene of the internet of vehicles.
The network overall connectivity is mainly based on the relationship between the network connectivity and the node density researched by the percolation theory, the node density required by the network connectivity is solved according to the percolation theory, the mobility of the nodes of the internet of vehicles and the internal attributes of the network topological structure are not considered in the solving process, and a scale for measuring the network performance in real time under the large-scale network environment is still not provided.
In summary, the related research on the performance of the car networking network mainly focuses on the network connectivity, including both the end-to-end connectivity and the overall network connectivity. The end-to-end connectivity research mainly considers the relationship between the communication probability between two nodes and the vehicle density and the communication radius under the small-scale network environment by using a node distribution model, and the network connectivity performance is not researched from the perspective of the whole network. The research on the overall network connectivity generally analyzes the change of the network static connectivity along with the vehicle density based on the percolation theory, does not consider the node mobility, and cannot provide a scale for measuring the network connectivity in real time. Therefore, the research on the performance of the internet of vehicles is not deeply researched, and the performance of a large-scale network cannot be accurately measured in real time.
Disclosure of Invention
Chengjijun et al, applied for connection base component construction method for interconnection and interworking of large-scale internet of vehicle networking on 11/10/2017 (applicant: college of Tongji, patent application No. 2017103978077), applied for the definition of connection base of internet of vehicles, namely: for the car networking topology G ═ V, E, if there are subgraphs
Figure GDA0003098387570000021
The following conditions are satisfied:
⑴
Figure GDA0003098387570000022
wherein
Figure GDA0003098387570000023
⑵
Figure GDA0003098387570000024
Are all provided with
Figure GDA0003098387570000025
Or v at least and
Figure GDA0003098387570000026
one node is adjacent;
⑶
Figure GDA0003098387570000027
are connected;
then call
Figure GDA0003098387570000028
Is the vehicle networking communication base of the network G. The invention provides a large-scale network accessibility model of the Internet of vehicles based on interconnected and intercommunicated communication base components.
The technical scheme of the invention is as follows:
a large-scale network accessibility model of the Internet of vehicles comprises the following steps:
step 1, analyzing accessibility of the Internet of vehicles based on a communication base;
step 2, an accessibility model based on a connected base;
step 21, node motion modeling;
step 22, network connectivity;
step 23, network stability;
step 24, network accessibility model
Advantageous effects
Aiming at the problems that the performance of a large-scale network cannot be accurately measured in real time and the like because the existing internet of vehicles does not start from the perspective of the whole network and does not consider the mobility of nodes and cannot provide a scale for measuring the network connectivity in real time, the invention is based on an interconnection and intercommunication base component (Chengdu et al inventor, application No. 2017103978077, applied in 2017, 10, 11, the construction method of the interconnection and intercommunication base component of the internet of vehicles and the large-scale network (applicant: university of the same economy), combined with a smooth Gauss-half Markov mobile model, researches the connectivity and stability of the large-scale network of the internet of vehicles and provides an internet of vehicles accessibility theoretical model which can accurately evaluate the network connectivity and stability under the high dynamic environment of the internet of vehicles and simultaneously provides theoretical support for a network layer, the method provides real-time data guarantee for an application layer, so that important information such as traffic accident information, real-time media data and the like can be stably and durably transmitted in the high-dynamic network environment of the Internet of vehicles, and the method has important theoretical and practical application values.
Drawings
FIG. 1 is a schematic view of a communication path
FIG. 2 Single hop Link model
Network stability error CDF under different delta t when fig. 3 alpha is 0.9
Fig. 4 Δ t ═ 0.2s network stability error CDF under different α
FIG. 5 communication radius 300 m network accessibility and delivery rate versus vehicle density
FIG. 6 is a graph of network accessibility and delivery rate versus vehicle density for a communication radius of 500 meters
FIG. 7 is a graph of network accessibility and delivery rate versus mean square error of velocity for a 300 meter communication radius
FIG. 8 is a graph of network accessibility and delivery rate versus mean square error of velocity for a communication radius of 500 meters
FIG. 9 is a flow chart of the method of the present invention
Detailed Description
The research on the accessibility problem of the Internet of vehicles is an important direction in the field of Internet of vehicles research, is an important index for judging the performance of the Internet of vehicles, and relates to whether the data of the network can accurately reach a destination and keep stable connection. Current research on connectivity in the internet of vehicles focuses mainly on both end-to-end connectivity and overall connectivity of the network. The end-to-end connectivity research mainly considers the relationship between the communication probability between two nodes and the vehicle density and the communication radius under the small-scale network environment by using a node distribution model, and the network connectivity performance is not researched from the perspective of the whole network. The research on the overall network connectivity generally analyzes the change of the network static connectivity along with the vehicle density based on the percolation theory, does not consider the node mobility, and cannot provide a scale for measuring the network connectivity in real time, so that the performance of a large-scale network cannot be accurately measured in real time. The invention provides a large-scale network accessibility model based on a vehicle networking communication base, aiming at the problems, the model is based on an interconnection and intercommunication communication base component (Tokujun et al, inventor, application of' construction method of interconnection and intercommunication communication base component of vehicle networking large-scale network (applicant, university of Tongji, patent application number 2017103978077) applied in 2017, 10, 11, the patent application provides the technical scheme that a vehicle networking network topological structure, namely a vehicle networking communication base, is provided by considering redundancy characteristics of vehicle networking network nodes, a communication base construction method is provided by utilizing a heuristic algorithm, the connectivity and stability of the vehicle networking large-scale network are proved by combining a smooth Gauss-half Markov mobile model from internal structure attributes and dynamic characteristics of the communication base, a vehicle networking accessibility theoretical model is provided, and the theoretical model can accurately evaluate the network connectivity and stability under a high-dynamic environment of the vehicle networking, meanwhile, theoretical support is provided for a network layer, real-time data guarantee is provided for an application layer, and important information such as traffic accident information and real-time media data can be stably and durably transmitted in a high dynamic network environment of the Internet of vehicles, so that the method has important theoretical and practical application values.
The specific implementation process of the invention is shown in fig. 9, and includes the following 3 aspects:
vehicle networking accessibility analysis based on communication basis
Accessibility model based on connected basis
Experiment of
Network stability verification
Verification of network accessibility model
①
Vehicle networking accessibility analysis based on communication base
The method aims at the accessibility of the large-scale network of the Internet of vehicles, and reduces the redundancy of network nodes because the Internet of vehicles only screens a plurality of connected element nodes to form the connected base, the network scale is smaller than that of the original network, the normal operation of the network is not influenced, and the calculation complexity of the accessibility can be effectively reduced by researching the accessibility of the network based on the connected base.
Here, for any given network G, where the set of vehicle nodes is V and the set of links is E. G can be seen to consist of network node devices, communication links connecting the nodes, and a dynamic topology consisting of nodes and links. If there is a connectivity group in the network G
Figure GDA0003098387570000041
G is divided into a connected meta-node and a common node, and the communication between the common nodes is completely handed to the connected meta-node which governs the common nodes for processing, so that any two points v in the networki，vjThe network accessibility is vi，vjThe accessibility of the communication base and the link connected with the two nodes are added, namely:
Figure GDA0003098387570000042
wherein
Figure GDA0003098387570000043
Is v isi，vjA communicating group therebetween.
When the number of intermediate forwarding connected element nodes is large, the accessibility of the link between the connected base and the node can be ignored, and v is the timei，vjThe network accessibility between the two is:
Figure GDA0003098387570000044
then the accessibility of network G is approximately equal to its connectivity base
Figure GDA0003098387570000045
Accessibility of (1). That is:
Figure GDA0003098387570000046
the accessibility of the network mainly depends on the network connectivity and the network stability, and the invention analyzes the influence on the network accessibility from the two aspects:
(1) network connectivity Co
In the internet of vehicles, the wireless transmission range of the nodes is limited, and the nodes are connected when in corresponding communication radius range. The network connectivity reflects the communication state between terminals and is an important factor for determining the network accessibility.
(2) Network stability St
Due to the high-speed mobility of the vehicle nodes, the transmission distance between the nodes changes constantly, the old path is disconnected and the new path is established frequently and alternately, and the network topology changes rapidly. Network stability is the basis for ensuring that data can be transmitted stably.
Respectively obtaining the connection base of network G
Figure GDA0003098387570000051
Connectivity of
Figure GDA0003098387570000052
And stability
Figure GDA0003098387570000053
The accessibility Ac (G) of G can be obtained, and the following functional relation is shown:
Figure GDA0003098387570000054
②
accessibility model based on connectivity basis
(1) Node motion modeling
For the internet of vehicles, the motion of vehicles has certain regularity, and the motion of network nodes needs to be modeled to research the accessibility of the network in a mobile state. The invention assumes that the topological structure of the network does not change in a short time, and describes the change of the topological structure along with the time by discretizing continuous motion by using a smooth Gaussian-half Markov motion model SGM.
For a given internet of vehicles network, the continuous time is first cutDivided into equal short time intervals Δ t, such that each time bin can be represented as tk＝Δt+tk-1(ii) a k is 1,2, …, n. Assuming that all nodes in the network move according to the SGM and that the initial positions of the nodes are known, node viHas an initial position of (x)i0,yi0). Then v is obtainediAt the k-ththPosition of individual moment (x)ik,yik) Comprises the following steps:
Figure GDA0003098387570000055
wherein VeikDenotes viAt the k-ththSpeed of the moment, thetaikDenotes viThe direction of movement of (a). During the time interval Δ t, we consider the node movement direction as unchanged. Therefore, the position of the node at any moment in the network can be solved through the formula, and if the value of delta t is smaller, the solved position of the node is more accurate.
From equation (5) node v can be calculatediAnd vjAt the k-ththThe euclidean distance at each moment is:
Figure GDA0003098387570000056
according to the Euclidean distance D between nodesij(tk) The communication radius with the nodes can judge the connection state between the nodes, aijRepresenting the connection state between the nodes, and R represents the communication radius, then
Figure GDA0003098387570000057
According to the kththThe connection state between any nodes at any moment can construct a time-varying adjacency matrix of the network as follows:
Figure GDA0003098387570000058
A(tk) Denotes the kththThe network topology structure of the time divides the whole network period into n time intervals, and the adjacency matrix of each time of the network can be obtained according to the formula, so that the network connection state of each time can be known, and the network topology structure has important significance for establishing the accessibility model of the network in a mobile state.
(2) Network connectivity
The connected bases are topological structures covering the whole network, the distance from each node to the connected element is guaranteed to be one hop, and the vehicle networking connectivity evaluation based on the connected bases is the evaluation of the connectivity of the connected bases.
Define 1 communication path: alternating sequence of nodes and edges in a network w ═ v0e0v1e1v2e2…vkekvk+1Is a communication path. Where k is the number of hops of the communication path. If v in the communicating path w0＝vk+1The communication path is called a communication ring.
Fig. 1 is a schematic view of a communication path. In network a, v1And v6The number of communication paths with 3 hops between them is 4, which are: v. of1v2v3v6，v1v2v5v6，v1v4v3v6，v1v4v5v6. And in network b, v1And v5The number of communication paths with a hop count of 3 is: v. of1v2v4v5And v1v3v4v5. The network a has more communication paths than the network b, and the network a can still be connected even if partial links in the network are disconnected. Therefore, network a is more connected than b. It can therefore be concluded that the number of alternative paths between nodes in the network determines the network connectivity. It can be concluded therefrom that connectivity of the connected base-based vehicle networking depends on the size of the number of communication paths in the connected base. For network G, the connectivity group is
Figure GDA0003098387570000061
The number of the middle communication paths is any communication element pair
Figure GDA0003098387570000062
Number of communication paths with number of inter-hop k
Figure GDA0003098387570000063
The sum of (1). But in large scale networks
Figure GDA0003098387570000064
The calculation difficulty of (2) is large, the calculation complexity is high, the number of the communication loops can also measure the number of the communication paths in the network, and the calculation complexity is low.
Figure GDA0003098387570000065
The number of connected loops is:
Figure GDA0003098387570000066
wherein
Figure GDA0003098387570000067
Indicates the number of hops is k and the starting point is
Figure GDA0003098387570000068
Number of connected loops, NkRepresenting the number of all connected loops with the number of hops k in the connected basis. The larger Sum indicates the larger number of backup paths, the stronger the connectivity of the connected base. However, when Sum is calculated, including the case where edges and nodes are duplicated, the final Sum value may tend to infinity. For any connected element pair
Figure GDA0003098387570000069
The communication loop with more hops has higher repetition rate calculated with other loops, and the proportion of the communication loop with more hops in Sum should be reduced, so the ratio of the communication loop with more hops to the Sum is NkPerforming a weighting calculation, namely:
Figure GDA00030983875700000610
wherein λiIs the eigenvalue of the connected basis adjacency matrix.
From the above equation, Sum' can be calculated from the eigenvalues of the adjacency matrix. However, when the network size is large, Sum' will be a very large number. Conveniently, Sum' is subjected to logarithm operation to obtain a connected basis
Figure GDA0003098387570000071
Connectivity value of
Figure GDA0003098387570000072
Figure GDA0003098387570000073
In view of the form of the utility model,
Figure GDA0003098387570000074
is in a direct proportion relation with Sum',
Figure GDA0003098387570000075
is a connected basis adjacency matrix
Figure GDA0003098387570000076
Is the special average value of all characteristic values, but passes an exponential operation and a logarithmic operation.
After modeling the motion of the node, the k-th is obtained from equation (11)thTime of day network connectivity
Figure GDA0003098387570000077
Similarly, after the network period is dispersed into a plurality of time intervals Δ t, the network connectivity at each moment can be solved. The smaller the value of delta t is, the more accurate the obtained network connectivity is.
The specific network connectivity calculation steps should be as follows:
1. construction of a communicating base
Figure GDA0003098387570000078
Confirming time interval delta t, communication radius of nodes and initial positions of connected elements, confirming connection states of the connected elements according to position information of the connected elements, and further constructing an adjacency matrix at an initial moment
Figure GDA0003098387570000079
2. According to the kththAdjacency matrix of time instants
Figure GDA00030983875700000710
Computing
Figure GDA00030983875700000711
Then calculating connectivity according to equation (11)
Figure GDA00030983875700000712
3. If the network life cycle is not finished, calculating the (k +1) th time according to the SGMthThe position of each connected element in the time network and the adjacency matrix at the moment
Figure GDA00030983875700000713
The connectivity is recalculated until the whole network life cycle is over.
(3) Network stability
The connected bases are topological structures covering the whole network, the stability of the whole network is analyzed through analyzing the topological stability of the dynamic network of the connected bases, and the research on the stability of the network based on the connected bases is essentially to research the stability of the connected bases.
The greatest difference between the internet of vehicles and the traditional network lies in the topological dynamics, the frequent establishment and disconnection of links, and the service life of a link can be measured. In an actual network, certain errors and interference exist in the available time length of links between nodes by directly sending messages between the nodes, and the Kalman filtering technology can remove noise and restore real data. Therefore, the link life is calculated based on the node movement model and Kalman filtering, and the stability of the network is deduced.
For any two nodes vAAnd vBLet VeAkAnd VeBkRespectively represent vAAnd vBAt the k-ththThe speed of the moment. Obtaining the k +1 th node according to the SGM modelthThe speed of the moment. Further, node vAAnd vBAt the (k +1)thThe relative velocity at a time can be calculated as follows:
Figure GDA00030983875700000714
Figure GDA0003098387570000081
wherein VeRk＝VeAk-VeBk，yRk＝yAk-yBkIs apparent from yRkIs a mean of 0 and a variance of σR 2＝2σ2Gaussian random variable of (2).
To calculate the link lifetime, consider the following single-hop link model, as in fig. 2. Two vehicle nodes vAAnd vBMove in the network according to the map. Although v isAAnd vBAll according to SGM motion, assume vAAt rest, vBMove according to the relative velocity in equation (12). Considering the network as a coordinate system, vehicle vAAt the origin, as long as the vehicle vBBetween coordinates (-R,0) to (R,0), vAAnd vBA link is established. When the vehicle vBEnter vAAt transmission radius of (v)AThe Beacon message will be received and begin to calculate v using kalman filteringBAnd v andBrelative speed between them, and then calculates the link lifetime between two nodes.
The Kalman filter is a real-time recursive algorithm for optimal estimation of state variables of dynamic systems, and includes two important equations: the state equation of the system and the observation equation.
Firstly, a state equation is given, and in the two-node linkage model, the state variables comprise vehicles vBAnd v and the running distance ofBMoving speed, according to SGM model, both variables are related to the previous moment, hence (k +1)thThe state process equation at the moment is:
Figure GDA0003098387570000082
wherein xk+1And xkRespectively refer to vehicles vBAt the (k +1)thAnd kthThe location of the time of day; y isRkAnd yxkIs independent and irrelevant Gaussian random variable, the mean values are all 0, and the variances are respectively sigmaR 2And σx 2. The matrix form of the state equation is:
Figure GDA0003098387570000083
the process equation can be regarded as a general form X through the above formulak+1＝TXk+wk，Xk+1Is a state variable vector, T is the kththState transition matrix of time, wkTo observe the noise, a gaussian random variable with mean 0 and covariance matrix Q is used. Q can be obtained according to formula (14) as:
Figure GDA0003098387570000084
the observation equation is given by Kalman filtering in a unified way, the kththThe observation equation of the time is Zk＝HXk+μkWherein Z iskIs the observation vector, H is the observation matrix, μkIs the observed noise, which is a gaussian variable with mean 0 and covariance matrix R.
Then order
Figure GDA0003098387570000091
Is XkIs estimated a priori of the time-of-flight,
Figure GDA0003098387570000092
is XkIs estimated by the a posteriori of (c),
Figure GDA0003098387570000093
and PkA priori and a posteriori covariance matrices, respectively.
Figure GDA0003098387570000094
I.e. the value when k is 0, in which case the diagonal elements should be particularly large values, while the non-diagonal elements should be 0. Thus, it is possible to provide
Figure GDA0003098387570000095
The initial values of (a) are:
Figure GDA0003098387570000096
according to the time updating and observation updating process of the Kalman filtering algorithm, the link life of the network can be estimated. When the vehicle v is, as shown in fig. 2BEnter vAIs within communication range of vAWill pass through vBThe transmitted Beacon information detects vBObtaining a vehicle vAAnd vBAt the (k +1)thThe link life estimate at that time is:
Figure GDA0003098387570000097
wherein
Figure GDA0003098387570000098
For network G, the connectivity group is
Figure GDA0003098387570000099
The network stability of the communication base is represented by the weighted average life of all links of the communication base, and the weight of each link is the proportion of the number of communication paths containing the link in the communication base to all the communication paths. Then network stability
Figure GDA00030983875700000910
Comprises the following steps:
Figure GDA00030983875700000911
wherein EjDenotes the j link in the connectivity base, njFor the link E included in the connected basejThe number of the communicating paths (c) of the communication path (c),
Figure GDA00030983875700000912
is the number of links in the connected base.
Figure GDA00030983875700000913
A calculation formula for measuring the stability of the network G at a certain moment is given, and the stability value of the network at any moment in the dynamic network can be obtained according to the formula (18) and a Kalman filtering algorithm.
(4) Network accessibility model
The accessibility of the Internet of vehicles, namely, the Internet interconnection and intercommunication can be realized quickly according to the current task, and the aim of keeping a stable state in real time to complete the current task is fulfilled. The accessibility network refers to a network which is connected and stable, and the network accessibility model is used for comprehensively measuring the connectivity and stability of a target network. Because the large-scale network nodes of the Internet of vehicles are large in scale, the difficulty in directly analyzing the accessibility of the nodes is high, and the network accessibility model established based on the Internet of vehicles can be smaller in network scale. For any two nodes v in GAAnd vBNetwork accessibility of vAAnd vBThe accessibility of the network G is the accessibility of the communication base, and a calculation formula of the network accessibility can be obtained:
Figure GDA00030983875700000914
wherein
Figure GDA0003098387570000101
Being a linking group of the network G, CothThe network connectivity threshold is based on the vehicle networking application requirements of the source vehicle. The network connectivity is a condition that the accessibility network needs to meet first, therefore, the network accessibility model should first judge whether the network connectivity meets the application requirements, when the connectivity value is greater than the threshold value, the network meets the application program requirements, and the network accessibility is the product of connectivity and stability. When the network connectivity is less than the connectivity threshold, the accessibility is 0.
③
Experiment of
The simulation platform and the experimental method used by the invention are the same as those of a communication base component construction method of interconnection and intercommunication of the Internet of vehicles large scale network (applicant: university of unity, patent application number 2017103978077) applied by inventor of chengdu et al on 2017, 10, 11, the invention continuously uses an urban scene road Internet of vehicles scene designed by a communication base component construction method of interconnection and intercommunication of the Internet of vehicles large scale network (applicant: university of unity, patent application number 2017103978077) applied by inventor of chengdu et al on 2017, 10, 11, in order to verify the correctness of the proposed accessibility model, a fixed roadside infrastructure, a source node, a target node and a communication radius of IEEE802.11p of 300 m to 1000 m are respectively arranged at the upper left corner and the lower right corner of the experimental scene, in the experiment, the change relation between the network accessibility and the network performance index is analyzed by respectively adopting 300 meters and 500 meters.
The specific experimental steps are as follows:
(1) firstly, constructing a communication base of the Internet of vehicles according to a communication base construction method given in a communication base component construction method for interconnection and intercommunication of the Internet of vehicles large-scale network (applicant: college of Tongji university, patent application number 2017103978077) applied by an inventor of Chengdu et al on 2017, 10 and 11;
(2) and controlling related memory parameters alpha and time intervals delta t in the smooth Gaussian-half Markov model, observing the correctness of the stability of the proposed network, and researching the optimal alpha and delta t values suitable for a simulation scene.
(3) And sending a data packet to the destination node by the source node by using a map-based routing protocol DSDV geographical source routing protocol. And controlling the vehicle density and the vehicle speed standard deviation in the network, researching the delivery rate and the network delay of end-to-end data packets in the network, comparing the network accessibility with the network accessibility calculated by the accessibility model, and verifying the correctness of the accessibility model.
④
Network stability verification
To calculate the network stability, the simulation time needs to be divided by Δ t. And updating the position and the speed of the node according to the SGM model, and updating the position and the speed of the node once every delta t. When the vehicle B enters the communication range of the vehicle A, the A starts to calculate the relative speed and the relative position with the B, and measurement data of Kalman filtering are obtained. In the simulation, each vehicle calculates the stability of the side of the vehicle at intervals of delta t, extracts the network snapshot at the moment at intervals of delta t, considers each snapshot network as a static network, and analyzes the network stability at the moment. The network stability error η is defined as the ratio between the difference between the stability value and the true stability value calculated according to the network stability model and the true stability value:
Figure GDA0003098387570000111
the network stability error values are collected and plotted graphically as shown in fig. 3 and 4.
Fig. 3 shows the network stability error CDF at different Δ t when α is 0.9. The results show that when Δ t is 0.5s or less, the 70% network stability error will be less than 20%. This is because as Δ t becomes larger, the number of values calculated by kalman filtering becomes smaller, and the derived network stability error becomes larger. Fig. 4 shows the different α network stability errors CDF when Δ t is 0.2 s. When α is 0.8 or 0.9, the error of network stability exceeding 60% is less than 20%. It will be appreciated that as α decreases, the node speed changes more randomly between each time bin, thus resulting in an increased network stability error. From the above experiment, it can be inferred that when α is 0.9 and Δ t is 0.2s, the network stability model can calculate the stability of the network more accurately.
⑤
Verification of network accessibility models
In order to verify the correctness of the network accessibility model, the network data packet delivery rate is considered to be compared with the network accessibility.
Firstly, simulation experiments are carried out under different vehicle densities, and the mean square deviations of the vehicle speeds in the simulation are set to be 5 m/s. And counting the average delivery rate of the data packets from the source node to the target node under different vehicle densities under different communication radiuses, simultaneously calculating corresponding average network accessibility values under different vehicle densities, and drawing a curve graph of the network accessibility and the vehicle densities. As shown in fig. 5 and 6, the blue line represents the theoretical value of network accessibility, and the orange line represents the average delivery rate of the DSDV protocol, which is obtained by statistics.
Under different communication radiuses, when the vehicle density is insufficient, the network accessibility is low, the network accessibility is slowly increased with the increase of the vehicle density, when a threshold value is reached, the accessibility is rapidly increased to reach a maximum value finally, and finally, a threshold value is also provided, and the speed is slowly increased after the threshold value.
As seen from fig. 5 and 6, the communication radius of the vehicle affects the position of the threshold, and the position where the network accessibility starts to increase sharply is 0.025 vehicles/meter when the transmission range is 300 meters, and 0.02 vehicles/meter when the transmission range is 500 meters. This means that, in the case where other conditions are consistent, the larger the node transmission range, the easier the accessibility network is to implement. When the transmission range of the node is 300 or 500 meters, the accessibility value calculated according to the accessibility model is relatively consistent with the conversion curve of the packet delivery rate along with the vehicle density through the correspondence of the blue line position and the orange line position. According to the change relation between the network accessibility value and the data packet delivery rate along with the vehicle density, when the nodes are sparse, the connectivity value cannot reach the connectivity threshold value, the network accessibility is 0, at the moment, the network accessibility value does not accord with the data packet delivery rate change curve, but the network accessibility can accurately measure the data transmission capacity of the network in most cases.
Subsequently, simulation experiments were performed at different mean square deviations of the speed, and the vehicle density in the simulation was set to 0.035 vehicles/meter. And counting the average delivery rate of the data packets from the source node to the target node under different speed mean square deviations under different communication radiuses, simultaneously calculating corresponding average network accessibility values under different vehicle densities, and drawing a curve graph of the network accessibility and the vehicle densities. As shown in fig. 7 and 8.
Fig. 7 shows the network accessibility and the variation relationship between the packet delivery rate and the mean square error of the speed when the node communication radius is 300 meters. The delivery rate reached 80% when the velocity mean square error was 2m/s, both network accessibility and data delivery rate began to decrease as the velocity mean square error increased, and reached the lowest at a velocity mean square error of 10 m/s. Through research on the change of network topology property along with the change of the mean square error of the speed, the fact that the relative speed between vehicles is increased due to the fact that the mean square error of the speed is increased, links in a network are prone to breaking, the network topology dynamics is increased, and therefore the data delivery rate is reduced along with the increase of the mean square error of the speed. This is also demonstrated in fig. 8 when the node transmission range is 500 meters. As can be seen from fig. 7 and 8, even under different communication radii and different mean square deviations of velocity, the network accessibility and the delivery rate have substantially the same trend of change with mean square deviation of velocity, which confirms that the network accessibility model is in accordance with the simulation results.
In conclusion, the vehicle networking large-scale network accessibility model based on the connectivity base can accurately evaluate the data transmission capacity of the network. Therefore, the network accessibility model is accurate and reliable in predicting the network accessibility, and is suitable for large-scale network scenes of the Internet of vehicles.
Innovation point
The invention provides a theoretical model for representing the accessibility of the large-scale network of the Internet of vehicles, thereby being beneficial to accurately measuring the real-time and stable network performance of the large-scale network of the Internet of vehicles. At present, the research on the connectivity of the internet of vehicles does not start from the perspective of the whole network, and the mobility of nodes is not considered, so that a scale for measuring the network connectivity in real time cannot be provided, and the performance of a large-scale network cannot be accurately measured in real time. The invention provides a large-scale network accessibility model based on a vehicle networking communication base, aiming at the problems, the model is based on an interconnection and intercommunication communication base component (Tokujun et al, inventor, application of' construction method of interconnection and intercommunication communication base component of vehicle networking large-scale network (applicant, university of Tongji, patent application number 2017103978077) applied in 2017, 10, 11, the patent application provides the technical scheme that a vehicle networking network topological structure, namely a vehicle networking communication base, is provided by considering redundancy characteristics of vehicle networking network nodes, a communication base construction method is provided by utilizing a heuristic algorithm, the connectivity and stability of the vehicle networking large-scale network are proved by combining a smooth Gauss-half Markov mobile model from internal structure attributes and dynamic characteristics of the communication base, a vehicle networking accessibility theoretical model is provided, and the theoretical model can accurately evaluate the network connectivity and stability under a high-dynamic environment of the vehicle networking, meanwhile, theoretical support is provided for a network layer, real-time data guarantee is provided for an application layer, and important information such as traffic accident information and real-time media data can be stably and durably transmitted in a high dynamic network environment of the Internet of vehicles, so that the method has important theoretical and practical application values.

Claims (1)

1. A large-scale network accessibility model design method based on a vehicle networking connectivity base is characterized in that an accessibility network refers to a network which is both connected and stable, and a network accessibility model is used for comprehensively measuring the connectivity and stability of a target network;
for any two nodes v in the network GAAnd vBNetwork accessibility of vAAnd vBThe accessibility of the network G is the accessibility of the communication base thereofThe calculation formula of the network accessibility Ac (G) is as follows:
Figure FDA0003070480860000011
wherein
Figure FDA0003070480860000012
Being a linking group of the network G, CothAs network connectivity threshold, CothAccording to the vehicle networking application program requirement of the source vehicle;
the car networking communication base is as follows: for the car networking topology G ═ V, E, if there are subgraphs
Figure FDA0003070480860000013
The following conditions are satisfied:
⑴
Figure FDA0003070480860000014
wherein
Figure FDA0003070480860000015
⑵
Figure FDA0003070480860000016
Are all provided with
Figure FDA0003070480860000017
Or v at least and
Figure FDA0003070480860000018
one node is adjacent;
⑶
Figure FDA0003070480860000019
are connected;
then call
Figure FDA00030704808600000110
The vehicle networking communication base is a network G;
linking group
Figure FDA00030704808600000111
Connectivity value of
Figure FDA00030704808600000112
Figure FDA00030704808600000113
Figure FDA00030704808600000114
Is based on a connected basis adjacency matrix
Figure FDA00030704808600000115
Calculating the characteristic value of the target; lambda [ alpha ]iThe characteristic value of the communication base adjacency matrix is shown, i is the communication path serial number, and n is the total communication path number;
network stability
Figure FDA00030704808600000116
Comprises the following steps:
Figure FDA00030704808600000117
wherein EjDenotes the j link in the connectivity base, njFor the link E included in the connected basejThe number of the communicating paths (c) of the communication path (c),
Figure FDA00030704808600000118
is the number of links in the connected basis; n isAllLET (E) as the total number of communication pathsj) A link life estimation function.
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