CN105827688A - Method for studying communication properties of Internet of Vehicles (IOV) large-scale heterogeneous network at urban scene - Google Patents
Method for studying communication properties of Internet of Vehicles (IOV) large-scale heterogeneous network at urban scene Download PDFInfo
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Abstract
The invention provides a method for studying the communication properties of an Internet of Vehicles (IOV) large-scale heterogeneous network at an urban scene. According to the method, the modeling for the IOV is conducted based on the graph theory, and an obtained model is modified to be applicable to the transient properties of the IOV through the relevance measurement of the complex network analyzing technique. Based on the above properties, the simulation test and the data analysis on the evolution condition of the real IOV along with the time can be conducted by utilizing complex network analysis tools on the basis of an existing TAPASCologne data set. After that, the kinetic analysis on the TAPASCologne data set of a large-scale vehicle movement path is conducted to obtain a relative conclusion for the communication properties of the IOV large-scale heterogeneous network. According to the technical scheme of the invention, the properties of the IOV are deeply studied, so that the network elements of the large-scale heterogeneous network can be effectively integrated. The real-time performances of the interconnection and the intercommunication of the IOV large-scale heterogeneous network are ensured. The large-scale information exchange of the IOV is fundamentally ensured. Moreover, the application requirements of the real-time data acquisition for traffic congestion, traffic safety, haze treatment and the like in the large-area environment can be met and supported.
Description
Technical field
The present invention relates to car networking arenas, be specifically related to the analysis method of the large scale scale heterogeneous network-in-dialing character of car networking in City scenarios.
Background technology
At present, China has become automobile production maximum in the world and has sold big country, automobile becomes the important component part of people's life, also have become as the 3rd important space outside subscriber household, office simultaneously, after solving " home network ", " Office Network " by the Internet, automotive networking has just become important perpetual object.But, the increase of automobile utilization rate, the serious problems such as traffic congestion, traffic safety, air pollution will be caused.Car networking is a relatively effective solution, the new and high technologies such as advanced information technology, network technology, automatic control technology and computer technology are applied to whole data transmission system by effectively, traffic congestion can be reduced, reduce vehicle accident, reduce environmental pollution, it is established that one in real time, data transmission system accurately and efficiently.
It is a heterogeneous network system huge, complicated, that be made up of different hierarchical networks that the target of car networking and characteristic determine car networking.Its composition comprises three parts: 1. car body territory, is mainly formed a compact car volume grid by sensors various in car and terminal node, is used for obtaining the real time information of in-vehicle information and adjacent vehicle；2. physical space territory, is mainly made up of the various networks in physical environment, including different types of roadside infrastructure network, car body net and mobile communications network etc.；3. information space territory, mainly includes access network type, the service quality of network, protocol type, the network bandwidth, terminal capability etc..Car inharmonious operation between each ingredient of networking is one of root affecting car working application and development.Therefore, an important research field during the connectedness of the large scale scale heterogeneous network of car networking is car networking.In new car networking large scale network system, how from overall angle, car networking base attribute and the method for analysis are provided, the effectively performance of analysis network-in-dialing under high dynamic environment, solve car to network the real-time data transmission problem in large scale scale heterogeneous network, be a difficult point facing of car networking large scale network institute.
The existing connectivity technology to car networking mainly utilizes emulation and analytic process, and destination object is only for vehicle self-organizing network (VehicularAd-hocNETwork, VANET), do not consider the car large scale scale heterogeneous network of networking, thus it is difficult to instruct the height time variation of the channel due to the car large scale scale heterogeneous network of networking, effective integration between the large scale scale heterogeneous network element that the factors such as prominent Doppler effect and the uncertainty of network topology are brought, there is limitation greatly in the real-time that car networking large scale network is interconnected, the wretched insufficiency in efficiency and performance will be brought, the fundamentally exchange of the extensive information of restriction car networking, it is difficult to support the traffic congestion under big regional environment, traffic safety, the application demand of the real-time data acquisitions such as haze improvement.
Summary of the invention
Present invention aim at car networking character research method in open a kind of City scenarios, car networking character is goed deep into systematic research, such that it is able to realize, to the effective integration between large scale scale heterogeneous network element, having ensured the real-time that car networking large scale network interconnects.Therefore, the present invention has fundamentally ensured the exchange of the car extensive information of networking, the application demand of the real-time data acquisitions such as the traffic congestion that can meet and support under big regional environment, traffic safety, haze improvement.
Realize to this end, the present invention provides techniques below scheme:
nullResearch method of the present invention，It is characterized in that，Utilize graph theory that car networking is modeled，Utilize calculation of correlation in Complex Networks Analysis technology，They transformations are applicable to the car instantaneous character of networking，Utilize these character，At existing TAPASCologne data set (on the basis of deriving from theInstituteofTransportationSystemsattheGermanAerospaceC enter (ITS-DLR)，The evolution condition utilizing Complex Networks Analysis instrument etc. to network actual car in time carries out emulation testing and data analysis，Extensive vehicle motion track TAPASCologne data set has been carried out dynamic analysis，Draw the related conclusions of the Connectivity Properties of the car large scale scale heterogeneous network of networking.
A kind of large scale scale heterogeneous network connectivty Quality Research method of car networking in City scenarios, it is characterised in that concrete grammar comprises the steps:
Step 1. utilizes the Origin/Destination matrix method of TAPASCologne to generate new data set；
Step 2. analyzes the relevant nature of car networking, including vehicle number, number of links, link and the relation etc. of vehicle, utilizes emulation experiment to be analyzed the car intranet network fundamental property of foundation-free facility；
Step 3. utilizes emulation experiment to be analyzed the car intranet network car group structure fundamental property of foundation-free facility；
Infrastructure is introduced in car intranet network by step 4., utilizes emulation experiment to be analyzed the car intranet network fundamental property having infrastructure；
Step 5., in terms of the central research of car online vehicles, utilizes betweenness and canvassing index to find out high-quality vehicle；
Step 6. compares diameter and the relation of residue vehicle number of car networking, determines the vigorousness and robustness having the car of infrastructure to network.
The particular content of these 6 steps is as follows.
In described step 1, obtain new data setUse TAPASCologne data set (deriving from theInstituteofTransportationSystemsattheGermanAerospaceC enter (ITS-DLR)), it is to combine real road topology, accurately microcosmic to move modeling, the transport need of reality and the traffic assignation of advanced person, in the region of Cologne, Germany city 400 sq-km, generating and include 24 hours biosynthesis locus files more than 700,000 train numbers, this data set is the most complete extensive vehicle mobility model that can obtain online at present；The Origin/Destination matrix method utilizing TAPASCologne generates new data set.
In described step 2, car networking relevant nature represents and definition
T vehicle viDegree diT () is the quantity of other vehicles being likewise supplied with car connected network communication function in its communication range:
Degree distribution is the important statistic describing network character.The degree distribution p of t vehicletD () is defined as being randomly chosen a vehicle in the car of t is networked, its degree is the probability of d, or is equivalently described as the vehicle number that car networking moderate is d and accounts for the ratio of vehicle fleet.
T car networking G (t) density DGT () is the ratio of the physical link number in car networking and possible maximum number of links:
Average minimum number of links h of t car networking G (t)G(t), for car networking communicates between any pair vehicle the meansigma methods of required minimum number of links:
Wherein hijT () is t vehicle i, the jumping figure needed for the shortest communication link between j.Maximum in the jumping figure of the shortest communication link between all vehicles is referred to as the diameter of car networking.
Car group in car networking refers to the number of links in fine and close subnet, i.e. car group therein more than the number of links between different car groups.In order to find out car group, t car networking G (t) can be converted to directed graph so thatWhereinIt is t vehicle uiIn-degree and out-degree.
Certain subnet U (t) of t car networking G (t) constitutes car group, when it meets:
I.e. in car group U (t) all degree and the sum of degree more than the remainder towards car networking G (t).
In the networking of t car, certain car group k's gathers coefficient cck(t), it is the important parameter weighing network group degree:
Wherein, | | Ek(t) | | it is number of links present in t car group k, | | Nk(t) | | it is the vehicle number in t car group k.
T vehicle viNeighbouring centrality Ci(t), measurement is the time that in network, certain car spends needed for other vehicle transmission information, be represented by during this vehicle and car are networked number of links (jumping figure) needed for other all vehicle communications and inverse:
Wherein hops (vi,vj) it is car viWith car vjBetween jumping figure.
Not vehicle v in the range of being in communication with each other in car networkingiAnd vjBetween communication depend on connection vehicle viAnd vjForward-path on the relay vehicle of process, if certain car is passed through by many bar forward-paths, then represent that this car has a very important role in current vehicle is networked, describe certain car power of influence in a network quantitatively or importance can be weighed by the betweenness centrality of vehicle.
T vehicle viBetweenness centrality BCi(t):
Wherein srjkT () represents t vehicle vj,vkBetween the quantity of the shortest different forward-path, srj,k(vi, t) represent vj,vkBetween the shortest forward-path of difference in through vehicle viNumber of path.Maximum betweenness in vehicle is closely related with the synchronizing capacity of vehicle, and the maximum betweenness of vehicle is the biggest, and the synchronizing capacity of network is the most weak.
T vehicle viBridge joint centrality BRiT (), is multiplied by a bridge joint coefficient by the betweenness centrality of this vehicle and obtains:
Wherein β (vi) it is vehicle viBridge joint coefficient, uiRepresent vehicle.Bridge joint coefficient be vehicle degree inverse divided by the degree of its all of its neighbor vehicle inverse and.Bridging central purpose is to find to be in the vehicle at center in car networking, and the number of links of these vehicles is compared its adjacent vehicle and wanted that much less simultaneously.
T gives vehicle viCanvassing index LiT (), represents vehicle v in car networking G (t)iThe all of its neighbor vehicle moderate quantity at least equal to the k maximum positive integer k equal to k:
Li(t)=max{k:dk(t)≥k}(9)
Wherein dkT () represents vehicle uiEach adjacent car ujDegree, and d1(t)≥d2(t)≥d3(t)….Canvassing index can be considered as the degree d of vehicleiT the vague generalization form of (), have expressed the information of all of its neighbor vehicle in the communication range of vehicle simultaneously.
So far, the expression of the relevant nature describing the car large scale scale heterogeneous network of networking is complete with definition.
The car intranet network character of foundation-free facility
The present invention is the result of study of the network shape rule to TAPASCologne car networking scenario, the most do not include car connected network communication figure under medium access problem (compete and disturb) kinetic property.Based on following factor, such as: the movement law of (i) vehicle, (ii) gives transmission range, and () exists roadside infrastructure, can set up multiple communication links (if necessary) on many time points.All these possible links determine the character of car networking figure, and knowing for this category information is that route is designing and executory key with data distribution protocol.
The car networking car group structure character of foundation-free facility
For needing to cross over the service that the networking of whole car spreads news, they must be known by whether whole network connects.Additionally, for needing to transmit message to for the service in certain specific geographic area, estimate that the density of the communication link between the vehicle in this region can particularly useful, because so can know when when to avoid flooding operates.We have carried out car cluster analysis to car networking figure, give in TAPASCologne car intranet network the quantity of car group and gather the situation of change that coefficient elapses in time.It is observed that the quantity of car group is mainly affected by transmission range.
There is the car intranet network character of infrastructure
Owing to whole TAPASCologne car networking figure is disconnected, therefore consider to must be introduced into infrastructure (roadside unit RoadSideUnit, RSU).For the needs analyzed, present invention assumes that in network service figure and can be connected by infrastructure between all disconnected car groups, the city car being the formation of infrastructure by such mode is networked, and makes the car networking in whole city become connection.
Car online vehicles centrality character
In order to car connected network communication figure is carried out deeper into research, the present invention calculates the situation of change that the meansigma methods of various centrality tolerance elapses under different transmission ranges over time.Through observing, the distribution of " central vehicle " will not be affected by communication range, and the transmission range distribution when 50 meters with 100 meters has similar shape.Centrality tolerance for traffic change reflection the most reliable.The traffic i.e. density of vehicle and relative position.Therefore, centrality is not the indication of communication context, but the instruction of potential " behavior " to vehicle, the i.e. intention of road network and driver finally determine vehicle position in a network.
The car large scale scale heterogeneous network robustness of networking
The scalability that robustness i.e. network removes for vehicle, this character is the most extremely important for any network, because it directly influences the cohesion (therefore have impact on network interrupt probability) of car networking, and car networking for the vehicle of related communication for the immunocompetence of malicious attack.
Beneficial effect
(1), in the case of the present invention is based on car networking foundation-free facility, there is following important conclusion.
The networking of TAPASCologne car is disconnected, comprises substantial amounts of car group；
The quantity of car group is affected by transmission range, but generally remains constant in time；
The connectedness of car group is affected by transmission range；
Intensive Che Qunzhong comprises the vehicle that angle value is bigger and less simultaneously；
Connection between neighbours' vehicle of the vehicle periphery of height value is the most sparse.
(2), in the case of the present invention has infrastructure based on car networking, there is following important conclusion.
The figure of car networking is fine and close, wherein follows fine and close power-law distribution: E (t) ∝ V (t) between vehicle number and number of linksα, wherein α 2.79；
Network diameter and average traffic degree increase along with the expansion of vehicle network scale；
Average traffic degree has bigger standard deviation；
Car networking does not show small world, has bigger car networking diameter and average minimum number of links.
(3) present invention is based on car online vehicles centrality character, has following important conclusion.
Centrality is the instruction of potential " behavior " to vehicle, and this acts on any communication range is all effective；
Betweenness centrality and canvassing centrality exponent are sufficient, suitable for identifying the vehicle (vehicle centered by more) of " high-quality "；
The size of vehicle degree can not distinguish the height of vehicle mass in network.
(4) present invention is based on car online vehicles centrality character, has following important conclusion.
The situation that car networking diameter increases or reduces, it is believed that power law is obeyed in the distribution of car networking diameter change, and its relational expression is:
How the value of the power factor alpha of power-law distribution determines the robustness of car networking, if α is close to 1, network is considered stalwartness.
The traffic diagram of the car networking introducing infrastructure is healthy and strong, when α=0.97, even if having lost a number of high-quality vehicle the character of figure also will not cause significantly impact.
Research method of the present invention, is that car networking character is goed deep into systematic research, such that it is able to realize, to the effective integration between large scale scale heterogeneous network element, having ensured the real-time that car networking large scale network interconnects.Therefore, the present invention has fundamentally ensured the exchange of the car extensive information of networking, the application demand of the real-time data acquisitions such as the traffic congestion that can meet and support under big regional environment, traffic safety, haze improvement.
Accompanying drawing explanation
Fig. 1 is the acquisition flow chart of new car networking data collection provided by the present invention.
Fig. 2 vehicle number changes over situation
Fig. 3 number of links changes over situation
The relation (transmission range=50 meter) of number of links and vehicle number under Fig. 4 log-log coordinate system
The relation (transmission range=100 meter) of number of links and vehicle number under Fig. 5 log-log coordinate system
Fig. 6 car group's number changes over situation
Fig. 7 averagely gathers coefficient and changes over situation
Fig. 8 cart group vehicle number changes over situation
Fig. 9 average local cluster coefficient and the relation of vehicle degree
The average minimum number of links of Figure 10 changes over situation
The average minimum number of links of Figure 11 is with vehicle number situation of change
Figure 12 car networking diameter changes over situation
Figure 13 average traffic degree changes over situation
The mean center tolerance of Figure 14 vehicle changes over situation
The average canvassing index of Figure 15 vehicle changes over situation
Figure 16 log-log coordinate system gets off the networking diameter relation (transmission range=50 meter) with vehicle number
Figure 17 log-log coordinate system gets off the networking diameter relation (transmission range=100 meter) with vehicle number
Figure 18 is the inventive method flow chart.
Detailed description of the invention
The specific implementation process of the present invention as shown in figure 18, including following 5 aspects:
1. new car networking data collection is obtained
2. the car networking Connectivity Properties in the case of foundation-free facility is analyzed
3. the car networking Connectivity Properties in the case of analysis has infrastructure
4. car cluster center Connectivity Properties is analyzed
5. the robustness of the car large scale scale heterogeneous network-in-dialing of networking is analyzed
①
Combine flow process as shown in fig. 1 first below, the acquisition of new car networking data collection is described.
Determine the range of OSM map first with O/D matrix and extract and filter, being then converted into the form that SUMO can identify and be input in this microcosmic movable simulation device；O/D matrix is used as the input of Gawron algorithm simultaneously, determine initial traffic assignation and be supplied to SUMO, SUMO carries out vehicle movable simulation for the first time subsequently, after emulation terminates, road traffic density feeds back to Gawron algorithm as result, according to the information that these are new, calculate the traffic assignation made new advances, carry out second time SUMO emulation, so circulate, until the traffic assignation generated can maintain whole transport need.
②
The car intranet network property analysis of foundation-free facility
Through quantitative study, vehicle number and number of links develop over time and follow following relational expression:
E(t)∝V(t)α(10)
α 2.79 when wherein transmission range is 50 meters, and according to the research to this relation under different transmission range (such as 50 meters, 100 meters etc.), it is appreciated that this relation is unrelated with the value of transmission range.It is observed that this rule is all set up under any transmission range.
This observed result has great importance for Design of Routing Protocol, because so, it is possible to estimate the quantity of communication link in network.
The car networking car group structure property analysis of foundation-free facility
In order to study the relation between vehicle degree and local connectivity, using local cluster coefficient quantization locally connected situation, result shows, locally connected's situation is unrelated with communication range, and intensive car group can comprise simultaneously has less degree and the vehicle of bigger degree.But for having the vehicle of bigger degree, its local cluster coefficient, then between 0.05 to 0.1, illustrates between its neighbours' vehicle the most sparse.This is in accordance with expectation, because for a car group having a lot of vehicle, can not have too many link between vehicle therein.This observation is it is meant that in the car not having infrastructure is networked, be difficult to find " faction " including many vehicles.Actual application can be caused huge trouble by this, because such as in propagation protocol, single broadcast is just likely to arrive the vehicle of effective quantity, and data cannot be sent to target vehicle colony at all in data distribution protocol.
③
There is the car intranet network property analysis of infrastructure
The situation of change elapsed over time by minimum number of links average in car intranet network, it has been found that the figure of TAPASCologne car networking does not show small world.This point is critically important, because this tolerance provides the average time being had to wait for before car information needed for obtaining.Such as, when transmission range is 50 meters, average beeline during 7:00 in the morning is 592 jumpings.We have also observed that when transmission range is 50 meters, the average number of hops between two cars is relatively big and along with the expansion of network size shows bigger variance.On the other hand, the change of minimum number of links when transmission range is 100 meters is the most smoother, and variance is less.The situation of change that network diameter elapses in time is consistent with average minimum number of links situation of change As time goes on.
The situation of change elapsed in time by average traffic degree in car intranet network, it has been found that average traffic degree increases along with the increase of the vehicle fleet size in networking at TAPASCologne car, and has the trend similar with number of links.Transmission range is number of links when 100 meters and average traffic degree is twice when 50 meters respectively.We have also observed that the variance of average traffic angle value is quite big, but their distribution is relatively uniform.This means have the vehicle of quite a few ratio to have higher or lower degree in network, these vehicles are in crossing respectively and are in the position that traffic flow is relatively low.
④
Car online vehicles centrality property analysis
We can reach a conclusion that betweenness, more or less obey similar distribution adjacent to bridge joint centrality exponent.No matter under what transmission range, it is constant that neighbouring centrality actually changes over holding.This is owing to the networking of TAPASCologne car is the most sparse, does not has big fine and close assembly (component), this also means that each car is the most identical to (averagely) distance of remaining vehicle in network.
The situation of change that canvassing index is then measured with above-mentioned all of centrality differs greatly.Defining according to it, its physical meaning is vague generalization and the simplification in a way of betweenness centrality of vehicle degree.It should be noted that substantial amounts of vehicle has identical canvassing index, and betweenness centrality does not has such situation, the most little vehicle has sizable betweenness centrality.This observed result is the most useful, because in the design of some agreements, it would be desirable to determine the vehicle of all of " high-quality ", distributes to the role that they are special, and in other cases, it would be desirable to simply " quality " that determine the highest vehicle.Therefore, we are it can be concluded that betweenness centrality and canvassing index are sufficient, suitable for catching the structural property of car connected network communication figure, and among them, neither one can replace another.
Centrality tolerance is the most interrelated with degree, the vehicle i.e. with height is the most also the vehicle that quality is higher, answering this problem, we calculate all vehicles Pearson correlation coefficients in specific time point (morning 7:00, transmission range is 100 meters).In the ordinary course of things, the span of Pearson correlation coefficients is-1 to 1, wherein the relation of-1 or 1 expression one " perfect ".Coefficient from 0 more away from, no matter it is to bear just, and between two variablees, relation is the strongest.The vehicle with higher degree is unrelated with betweenness centrality and bridge joint centrality.On the other hand, with neighbouring centrality, there is relatively low positive correlation.Therefore, the degree of vehicle is unable to identify that the vehicle of " high-quality " in car networking, and betweenness and canvassing index are done better.
⑤
The car large scale scale heterogeneous network robustness analysis of networking
If removed from car is networked by vehicle, the linkage length between two cars will increase.A kind of simple but highly effective tolerance can be used to quantify the robustness of car networking, i.e. observe in the case of deleting the vehicle with the highest betweenness centrality value, how car networking diameter will change.After the vehicle of 10% is deleted before by betweenness centrality exponent, the situation that car networking diameter increases or reduces, it is believed that power law is obeyed in the distribution of car networking diameter change, and its relational expression is:
Wherein NiIt is the remaining vehicle number when i & lt removes vehicle, diameteriIt is then that this has NiThe diameter of the car networking of car.How the value of α determines the robustness of car networking, if α is close to 1, network is considered stalwartness.According to our result of study, the traffic diagram of the car networking introducing infrastructure is healthy and strong, when α=0.97, even if having lost a number of high-quality vehicle the character of figure also will not cause significantly impact.
Embodiment
The present embodiment is the detailed description to above detailed description of the invention.
The evolution condition utilizing Complex Networks Analysis instrument etc. to network actual car in time on the basis of existing data set carries out emulation testing and data analysis.Main purpose is answer following four key issue: the character of car networking figure is how Temporal Evolution；Can centrality tolerance be used for determining high-quality vehicle；Whether car networking figure exists fine and close subgraph；Whether car networking has robustness.
(1) car networking relevant nature represents and definition
Before car networking bulk property is studied, it is necessary first to use suitable instrument that network is described.The present invention uses non-directed graph to portray car intranet network, and non-directed graph G (t) represents the car networking of t, wherein vehicle set V (t)={ viRepresent t all vehicles, i ∈ 1,2 ..., n}, n represent figure summit sum, i.e. vehicle fleet.Link set E (t)={ eijRepresent all communication links between current time vehicle, link eijV during (t) and if only if tiWith vjExist when can directly be in communication with each other, wherein i, j ∈ 1,2 ..., n} and i ≠ j.
● car intranet network character
Definition 1t moment vehicle viDegree diT () is the quantity of other vehicles being likewise supplied with car connected network communication function in its communication range:
Degree distribution is the important statistic describing network character.The degree distribution p of t vehicletD () is defined as being randomly chosen a vehicle in the car of t is networked, its degree is the probability of d, or is equivalently described as the vehicle number that car networking moderate is d and accounts for the ratio of vehicle fleet.
Power-law distribution is all followed in the node degree distribution of many real complex networks, and mathematical form is: p (k) k-γ, wherein k is the degree of node, and γ is typically in the range of between 2 to 3.Power function is a straight line declined under log-log coordinate system.Compared with exponential function, power function decrease speed is slower so that the node that in network, presence is bigger, and commonly referred to as these nodes are hub node (hubnode).Research finds, except power-law distribution, to there is also the degree distribution of other form in live network.
Definition 2t moment car networking G (t) density DGT () is the ratio of the physical link number in car networking and possible maximum number of links:
Average minimum number of links h of definition 3t moment car networking G (t)G(t), for car networking communicates between any pair vehicle the meansigma methods of required minimum number of links:
Wherein hijT () is t vehicle i, the jumping figure needed for the shortest communication link between j.Maximum in the jumping figure of the shortest communication link between all vehicles is referred to as the diameter of car networking.
The degree degree dependency of car networking describes the big vehicle of car networking moderate and the relation spent between little vehicle.If spending big vehicle tend to and spend big vehicle and be connected.Then car networking degree of being degree is positively related；Tend to whereas if spend big vehicle and spend little vehicle and be connected, then car networking degree of being degree negative correlation.Can utilize around certain car the average degree of adjacent carAnd relation degree of the description degree dependency between the degree d of this vehicle.When car networking is positive correlation,It it is the curve being incremented by with d；When network is negative correlation,It it is the curve successively decreased with d.
Calculating Pearson (Pearson) correlation coefficient of the degree of the two ends vehicle of link, r can describe the degree degree dependency of network.It is specifically defined as:
Wherein ji,kiRepresenting the degree of the vehicle j, k of i-th both link ends, M represents the total link number that car is networked.Degree degree correlation coefficient r is in the range of 0≤r≤1.As r ＜ 0, car networking degree of being degree negative correlation；As r ＞ 0, car networking degree of being degree is positively related；As r=0, network degree of being degree is incoherent.
● the centrality of car networking
Definition 4t moment vehicle viNeighbouring centrality Ci(t), measurement is the time that in network, certain car spends needed for other vehicle transmission information, be represented by during this vehicle and car are networked number of links (jumping figure) needed for other all vehicle communications and inverse:
Wherein hops (vi,vj) it is car viWith car vjBetween jumping figure.
Not vehicle v in the range of being in communication with each other in car networkingiAnd vjBetween communication depend on connection vehicle viAnd vjForward-path on the relay vehicle of process, if certain car is passed through by many bar forward-paths, then represent that this car has a very important role in current vehicle is networked, describe certain car power of influence in a network quantitatively or importance can be weighed by the betweenness centrality of vehicle.
Definition 5t moment vehicle viBetweenness centrality BCi(t):
Wherein srjkT () represents t vehicle vj,vkBetween the quantity of the shortest different forward-path, srj,k(vi, t) represent vj,vkBetween the shortest forward-path of difference in through vehicle viNumber of path.Maximum betweenness in vehicle is closely related with the synchronizing capacity of vehicle, and the maximum betweenness of vehicle is the biggest, and the synchronizing capacity of network is the most weak.
Definition 6t moment vehicle viBridge joint centrality BRiT (), is multiplied by a bridge joint coefficient by the betweenness centrality of this vehicle and obtains:
Wherein β (vi) it is vehicle viBridge joint coefficient.Bridge joint coefficient be vehicle degree inverse divided by the degree of its all of its neighbor vehicle inverse and.Bridging central purpose is to find to be in the vehicle at center in car networking, and the number of links of these vehicles is compared its adjacent vehicle and wanted that much less simultaneously.
The definition 7t moment gives vehicle viCanvassing index LiT (), represents vehicle v in car networking G (t)iThe all of its neighbor vehicle moderate quantity at least equal to the k maximum positive integer k equal to k:
Li(t)=max{k:dk(t)≥k}(8)
Wherein djT () represents vehicle uiEach adjacent car ujDegree, and d1(t)≥d2(t)≥d3(t)….Canvassing index can be considered as the degree d of vehicleiT the vague generalization form of (), have expressed the information of all of its neighbor vehicle in the communication range of vehicle simultaneously.
● the car group structure of car networking
Empirical research shows, many live networks present a common feature: there is the structure on outlying the densest limit between group in group in network, this structure is referred to as the structure of community (communitystructure) of network, is also the bloc framework of network.As scientist's cooperative network is segmented into some different research institutions according to different research themes, research method；WWW network constitutes the hyperlink between webpage according to different themes；Structure of community in metabolic network, neuroid then reflects functional units different in network.Many scholars propose the algorithm of the structure of community identifying network.Traditional method identifying network structure of community mainly has levels the method for betweenness based on limit of clustering algorithm (hieraticalclusteringalgorithm) and analysis of spectrum (spectralanalysis) method and Girvan and Newman proposition.
Car group in car networking refers to the number of links in fine and close subnet, i.e. car group therein more than the number of links between different car groups.In order to find out car group, t car networking G (t) can be converted to directed graph so thatWhereinIt is t vehicle uiIn-degree and out-degree.
Certain subnet U (t) of definition 8t moment car networking G (t) constitutes car group, when it meets:
I.e. in car group U (t) all degree and the sum of degree more than the remainder towards car networking G (t).
In definition 9t moment car networking, certain car group k's gathers coefficient cck(t), it is the important parameter weighing network group degree:
Wherein, | | Ek(t) | | it is number of links present in t car group k, | | Nk(t) | | it is the vehicle number in t car group k.When having link between each car of Che Qunzhong, to take maximum be 1 to the coefficient that gathers of this car group.
Gather coefficient reaction is the grouping of the world economy degree of car group entirety, the grouping of the world economy percentage contribution that single vehicle to be studied is networked for whole car, it may be considered that define its localized clusters coefficient.Certain car uiD is had in ti(t) adjacent vehicle, and these adjacent vehicles have z from each otheri(t) bar link, then,
Definition 10t moment vehicle uiLocal cluster coefficient lcciT the mathematic(al) representation of () is:
Degree of being similar to degree dependency, the coefficient that averagely gathers of car networking is worth research as the relation of degree, and this relation is referred to as a bunch degree dependency.Substantial amounts of empirical research shows, the reciprocal relation of bunch degree dependency existence approximation of many live network such as Hollywood movie Actor Collaboration Network networks, semantic network interior joint:
C(k)□k-1(13)
Wherein C (d) be degree for d node gather coefficient.Also live network has uncalibrated visual servo characteristic simultaneously and bigger gathers coefficient to have research worker to consider, constructs hierarchical network model (hierarchicalnetwork).Research shows, the network with level also includes WWW, metabolism network etc., but for having the spatial network of position and geographical relationship, does not the most have this level such as power network, its possible cause is owing to being restricted by network charges, and node can only be connected with node close together.
(2) TAPASCologne data set
The present invention uses TAPASCologne data set (deriving from theInstituteofTransportationSystemsattheGermanAerospaceC enter (ITS-DLR)), it is to combine real road topology, accurately microcosmic to move modeling, the transport need of reality and the traffic assignation of advanced person, in the region of Cologne, Germany city 400 sq-km, generating and include 24 hours biosynthesis locus files more than 700,000 train numbers, this data set is the most complete extensive vehicle mobility model that can obtain online at present；The Origin/Destination matrix method utilizing TAPASCologne generates new data set, concrete to generate process as follows: determines the range of OSM map first with O/D matrix and extracts and filter, and is then converted into the form that SUMO can identify and is input in this microcosmic movable simulation device；O/D matrix is used as the input of Gawron algorithm simultaneously, determine initial traffic assignation and be supplied to SUMO, SUMO carries out vehicle movable simulation for the first time subsequently, and after emulation terminates, road traffic density feeds back to Gawron algorithm as result, according to the information that these are new, calculate the traffic assignation made new advances, carry out second time SUMO emulation, so circulate, until the traffic assignation generated can maintain whole transport need, as shown in Figure 1.
(3) the car networking of foundation-free facility
The present invention is the result of study of the network shape rule to TAPASCologne car networking scenario, the most do not include car connected network communication figure under medium access problem (compete and disturb) kinetic property.Research shows, based on following factor, such as: the movement law of (i) vehicle, (ii) gives transmission range, and () exists roadside infrastructure, many time points can set up multiple communication links (if necessary).All these possible links determine the character of car networking figure, and knowing for this category information is that route is designing and executory key with data distribution protocol.But, specifically how these links (i.e. share medium) conduct interviews, then be the work of MAC protocol, in the range of being not belonging to technical scheme.
● network fundamental analysis
First we study vehicle number V (t) in these snapshots and number of links E (t) situation over time.Fig. 2 and Fig. 3 respectively show vehicle number and number of links situation of change As time goes on.As other complex networks, the scale of car networking can expand along with the increase of the vehicle number of entrance map area and the increase of their wireless antenna transmission range.
We further study the relation between link and vehicle: Fig. 4 and Fig. 5 and describe car networking figure and substantially obey fine and close power law (DensificationPowerLaw) subsequently.Through quantitative study, vehicle number and number of links develop over time and follow following relational expression:
E(t)∝V(t)α(14)
α 2.79 when wherein transmission range is 50 meters, and according to the research to this relation under different transmission range (such as 50 meters, 100 meters etc.), it is appreciated that this relation is unrelated with the value of transmission range.According to our observation, this rule is all set up under any transmission range.This means that the figure that TAPASCologne car is networked is fine and close (dense).According to document, α=1 expression elapses over time, and average degree keeps invariable, and α=2 are corresponding to an extremely fine and close figure, it means that on average, the quantity of the link that each vehicle is connected to every other vehicle is a constant.This observed result has great importance for Design of Routing Protocol, because so, the quantity of communication link in network just can be estimated by we.
● car group structure is analyzed
For needing to cross over the service that the networking of whole car spreads news, they must be known by whether whole network connects.In addition, for needing to transmit message to service (the geography multicast in certain specific geographic area, geocast) for, estimate that the density of the communication link between the vehicle in this region can be particularly useful, because so can know when when avoid flooding operation.
We have carried out car cluster analysis to car networking figure, Fig. 6 and Fig. 7 gives in TAPASCologne car intranet network the quantity of car group and gather the situation of change that coefficient elapses in time.It is observed that the quantity of car group is mainly affected by transmission range.Specifically, when when transmission range is 50 meters, the quantity of car group is than 100 meters many 1 times.Averagely gather coefficient also to be affected by transmission range simultaneously, averagely gather during coefficient ratio 100 meters little 1.5 times when transmission range is 50 meters.
And the vehicle number that on the other hand cart group comprises is not affected by transmission range, Fig. 8 shows transmission range when being respectively 100 and 50 meters, in cart group, vehicle number accounts for the percentage ratio of now vehicle fleet, it can be seen that no matter be under which kind of transmission range, the situation of change of accounting is the most completely the same.
In order to study the relation between vehicle degree and local connectivity, we use local cluster coefficient quantization locally connected situation, as shown in Figure 9.This figure shows, locally connected's situation is unrelated with communication range, and intensive car group can comprise simultaneously has less degree and the vehicle of bigger degree.But for having the vehicle of bigger degree, its local cluster coefficient, then between 0.05 to 0.1, illustrates between its neighbours' vehicle the most sparse.This is in accordance with expectation, because for a car group having a lot of vehicle, can not have too many link between vehicle therein.This observation is it is meant that in the car not having infrastructure is networked, we are difficult to find " faction " including many vehicles.Actual application can be caused huge trouble by this, because such as in propagation protocol, single broadcast is just likely to arrive the vehicle of effective quantity, and data cannot be sent to target vehicle colony at all in data distribution protocol.
In sum, it is proposed that to draw a conclusion:
1) networking of TAPASCologne car is disconnected, comprises substantial amounts of car group；
2) quantity of car group is affected by transmission range, but generally remains constant in time；
3) connectedness of car group is affected by transmission range；
4) intensive Che Qunzhong comprises the vehicle that angle value is bigger and less simultaneously；
5) connection between neighbours' vehicle of the vehicle periphery of height value is the most sparse.
(4) car having infrastructure is networked
Owing to whole TAPASCologne car networking figure is disconnected, therefore consider to must be introduced into infrastructure (roadside unit RoadSideUnit, RSU).For the needs analyzed, it will be assumed that can be connected by infrastructure between all disconnected car groups in network communication figure, the city car being the formation of infrastructure by such mode is networked, and makes the car networking in whole city become connection.
● analysis of network
What Figure 10 represented is the situation of change that in network, average minimum number of links elapses over time.Our result of study shows, the figure of TAPASCologne car networking does not show small world.This point is critically important, because this tolerance provides the average time being had to wait for before car information needed for obtaining.Such as, when transmission range is 50 meters, average beeline during 7:00 in the morning is 592 jumpings.We have also observed that when transmission range is 50 meters, the average number of hops between two cars is relatively big and along with the expansion of network size shows bigger variance (as shown in figure 11).On the other hand, the change of minimum number of links when transmission range is 100 meters is the most smoother, and variance is less.The situation of change that network diameter elapses in time is consistent with average minimum number of links situation of change As time goes on, as shown in figure 12, and have sizable meansigma methods (transmission range be 50 meters of average cars networking diameters equal to 3050.48,100 meters time be 1917.72).
Figure 13 illustrates the situation of change that average traffic degree elapses in time.It may be noted that average traffic degree increases along with the increase of the vehicle fleet size in networking at TAPASCologne car, and there is the trend similar with number of links.Transmission range is number of links when 100 meters and average traffic degree is twice when 50 meters respectively.We have also observed that the variance of average traffic angle value is quite big, but their distribution is relatively uniform.This means have the vehicle of quite a few ratio to have higher or lower degree in network, these vehicles are in crossing respectively and are in the position that traffic flow is relatively low.
In sum, by analysis of network being obtained following observed result:
1) figure of car networking is fine and close, wherein follows fine and close power-law distribution: E (t) ∝ V (t) between vehicle number and number of linksα, wherein α 2.79；
2) network diameter and average traffic degree increase along with the expansion of vehicle network scale；
3) average traffic degree has bigger standard deviation；
4) car networking does not show small world, has bigger car networking diameter and average minimum number of links.
● centrality analysis
In order to car connected network communication figure is carried out deeper into research, we calculate the situation of change that the meansigma methods of various centrality tolerance elapses under different transmission ranges over time, and result is as shown in Figure 14 and Figure 15.Through observing, the distribution of " central vehicle " will not be affected by communication range, and the transmission range distribution when 50 meters with 100 meters has similar shape.Centrality tolerance for traffic change reflection the most reliable.The traffic i.e. density of vehicle and relative position.Therefore, centrality is not the indication of communication context, but the instruction of potential " behavior " to vehicle, the i.e. intention of road network and driver finally determine vehicle position in a network.
Betweenness can be reached a conclusion that from Figure 14, more or less obey similar distribution adjacent to bridge joint centrality exponent.No matter under what transmission range, it is constant that neighbouring centrality actually changes over holding.This is owing to the networking of TAPASCologne car is the most sparse, does not has big fine and close assembly (component), this also means that each car is the most identical to (averagely) distance of remaining vehicle in network.
The situation of change that canvassing index (as shown in figure 15) is then measured with above-mentioned all of centrality differs greatly.Defining according to it, its physical meaning is vague generalization and the simplification in a way of betweenness centrality of vehicle degree.It should be noted that substantial amounts of vehicle has identical canvassing index, and betweenness centrality does not has such situation, the most little vehicle has sizable betweenness centrality.This observed result is the most useful, because in the design of some agreements, it would be desirable to determine the vehicle of all of " high-quality ", distributes to the role that they are special, and in other cases, it would be desirable to simply " quality " that determine the highest vehicle.Therefore, we are it can be concluded that betweenness centrality and canvassing index are sufficient, suitable for catching the structural property of car connected network communication figure, and among them, neither one can replace another.
Next the centrality to present invention research measures study the most interrelated with degree, and the vehicle i.e. with height is the most also the vehicle that quality is higher.Answering this problem, we calculate all vehicles Pearson correlation coefficients in specific time point (morning 7:00, transmission range is 100 meters).In the ordinary course of things, the span of Pearson correlation coefficients is-1 to 1, wherein the relation of-1 or 1 expression one " perfect ".Coefficient from 0 more away from, no matter it is to bear just, and between two variablees, relation is the strongest.Can clearly draw from table 1, the vehicle with higher degree is unrelated with betweenness centrality and bridge joint centrality.On the other hand, with neighbouring centrality, there is relatively low positive correlation.Therefore, the degree of vehicle is unable to identify that the vehicle of " high-quality " in car networking, and betweenness and canvassing index are done better.
Table 1 centrality and vehicle degree correlation coefficient
In sum, it is proposed that to draw a conclusion:
1) centrality is the instruction of potential " behavior " to vehicle, and this acts on any communication range is all effective；
2) betweenness centrality and canvassing centrality exponent are sufficient, suitable for identifying the vehicle (vehicle centered by more) of " high-quality "；
3) size of vehicle degree can not distinguish the height of vehicle mass in network.
● robust analysis
The scalability that robustness i.e. network removes for vehicle, this character is the most extremely important for any network, because it directly influences the cohesion (therefore have impact on network interrupt probability) of car networking, and car networking for the vehicle of related communication for the immunocompetence of malicious attack.
If removed from car is networked by vehicle, the linkage length between two cars will increase.A kind of simple but highly effective tolerance can be used to quantify the robustness of car networking, i.e. observe in the case of deleting the vehicle with the highest betweenness centrality value, how car networking diameter will change.Figure 16 shows that car networking diameter increases or the situation of minimizing by after the vehicle deletion of 10% before betweenness centrality exponent.According to Figure 16, it is believed that power law is obeyed in the distribution of car networking diameter change, and its relational expression is:
Wherein NiIt is the remaining vehicle number when i & lt removes vehicle, diameteriIt is then that this has NiThe diameter of the car networking of car.How the value of α determines the robustness of car networking.If α is close to 1, network is considered stalwartness.According to our result of study, the traffic diagram of the car networking introducing infrastructure is healthy and strong, such as α=0.97 in Figure 16.Even if having lost a number of high-quality vehicle also the character of figure will not be caused significantly impact.
Innovative point
One of innovation: the calculation of correlation in graph theory and Complex Networks Analysis technology is applied in car networking property analysis, enriches and developed the Research Thinking of car networking.The incidence relation that car networking large scale network objective reality is complicated, the defect such as cause that the network interconnection intercommunication degree of coupling is low, real-time and stability are difficult to ensure that, become and hinder car networking large scale network application and the bottleneck of industry development.Existing research method mainly realizes application integration by protocol conversion and routing algorithm, lacks open network environment and gets off the theoretical basis of networking character and method, thus can not resolve the essential attribute of car networking, it is impossible to solves the Key technique problem of car networking very well.Calculation of correlation in graph theory and Complex Networks Analysis technology is applied in car networking property analysis by the present invention, the research car instantaneous character of networking, thus provides the support of essential attribute for studying of car networking association area.
The two of innovation: extensive vehicle motion track TAPASCologne data set is carried out dynamic analysis, provide the crucial conclusion of car networking relevant nature in City scenarios.First, the fundamental property of car networking is analyzed in the case of there is no infrastructure, relation etc. including vehicle number, number of links, link and vehicle, find that fine and close power-law distribution obeyed by link and vehicle, and to wherein bunch being analyzed, finding that whole network is disconnected, the neighbor node of height value vehicle periphery interconnects the most sparse；Secondly, infrastructure being introduced in car networking makes the whole network be connected, and has shown that this network does not show the conclusion of small world, and research central for car online vehicles finds, betweenness and canvassing index can be used to find out high-quality vehicle, but degree cannot distinguish between；Finally, for the robustness of car networking, present invention employs the diameter of car networking and the relation of residue vehicle number, it is thus identified that the car networking having infrastructure is healthy and strong, can tackle chain rupture and malicious attack.
Claims (4)
1. the large scale scale heterogeneous network connectivty Quality Research method of car networking in a City scenarios, it is characterized in that, utilize graph theory that car networking is modeled, utilize calculation of correlation in Complex Networks Analysis technology, they transformations are applicable to the car instantaneous character of networking, utilize these character, on the basis of existing TAPASCologne data set, the evolution condition utilizing Complex Networks Analysis instrument etc. to network actual car in time carries out emulation testing and data analysis, extensive vehicle motion track TAPASCologne data set has been carried out dynamic analysis, draw the related conclusions of the Connectivity Properties of the car large scale scale heterogeneous network of networking.
2. the large scale scale heterogeneous network connectivty Quality Research method of car networking in a City scenarios, it is characterised in that concrete grammar comprises the steps:
Step 1. utilizes the Origin/Destination matrix method of TAPASCologne to generate new data set；
Step 2. analyzes the relevant nature of car networking, including vehicle number, number of links, link and the relation etc. of vehicle, utilizes emulation experiment to be analyzed the car intranet network fundamental property of foundation-free facility；
Step 3. utilizes emulation experiment to be analyzed the car intranet network car group structure fundamental property of foundation-free facility；
Infrastructure is introduced in car intranet network by step 4., utilizes emulation experiment to be analyzed the car intranet network fundamental property having infrastructure；
Step 5., in terms of the central research of car online vehicles, utilizes betweenness and canvassing index to find out high-quality vehicle；
Step 6. compares diameter and the relation of residue vehicle number of car networking, determines the vigorousness and robustness having the car of infrastructure to network.
3. the car large scale scale heterogeneous network connectivty Quality Research method of networking in City scenarios as claimed in claim 2, it is characterised in thatIn described step 1,The Origin/Destination matrix method utilizing TAPASCologne generates new data set.
4. the car large scale scale heterogeneous network connectivty Quality Research method of networking in City scenarios as claimed in claim 2, it is characterised in thatIn described step 2, car networking relevant nature represents and definition
T vehicle viDegree diT () is the quantity of other vehicles being likewise supplied with car connected network communication function in its communication range:
Degree distribution is the important statistic describing network character；The degree distribution p of t vehicletD () is defined as being randomly chosen a vehicle in the car of t is networked, its degree is the probability of d, or is equivalently described as the vehicle number that car networking moderate is d and accounts for the ratio of vehicle fleet；
T car networking G (t) density DGT () is the ratio of the physical link number in car networking and possible maximum number of links:
Average minimum number of links h of t car networking G (t)G(t), for car networking communicates between any pair vehicle the meansigma methods of required minimum number of links:
Wherein hijT () is t vehicle i, the jumping figure needed for the shortest communication link between j；Maximum in the jumping figure of the shortest communication link between all vehicles is referred to as the diameter of car networking；
Car group in car networking refers to the number of links in fine and close subnet, i.e. car group therein more than the number of links between different car groups；In order to find out car group, t car networking G (t) can be converted to directed graph so thatWhereinIt is t vehicle uiIn-degree and out-degree；
Certain subnet U (t) of t car networking G (t) constitutes car group, when it meets:
I.e. in car group U (t) all degree and the sum of degree more than the remainder towards car networking G (t)；
In the networking of t car, certain car group k's gathers coefficient cck(t), it is the important parameter weighing network group degree:
Wherein, | | Ek(t) | | it is number of links present in t car group k, | | Nk(t) | | it is the vehicle number in t car group k；
T vehicle viNeighbouring centrality Ci(t), measurement is the time that in network, certain car spends needed for other vehicle transmission information, be represented by during this vehicle and car are networked number of links (jumping figure) needed for other all vehicle communications and inverse:
Wherein hops (vi,vj) it is car viWith car vjBetween jumping figure；
Not vehicle v in the range of being in communication with each other in car networkingiAnd vjBetween communication depend on connection vehicle viAnd vjForward-path on the relay vehicle of process, if certain car is passed through by many bar forward-paths, then represent that this car has a very important role in current vehicle is networked, describe certain car power of influence in a network quantitatively or importance can be weighed by the betweenness centrality of vehicle；
T vehicle viBetweenness centrality BCi(t):
Wherein srjkT () represents t vehicle vj,vkBetween the quantity of the shortest different forward-path, srj,k(vi, t) represent vj,vkBetween the shortest forward-path of difference in through vehicle viNumber of path；Maximum betweenness in vehicle is closely related with the synchronizing capacity of vehicle, and the maximum betweenness of vehicle is the biggest, and the synchronizing capacity of network is the most weak；
T vehicle viBridge joint centrality BRiT (), is multiplied by a bridge joint coefficient by the betweenness centrality of this vehicle and obtains:
Wherein β (vi) it is vehicle viBridge joint coefficient, uiRepresent vehicle；Bridge joint coefficient be vehicle degree inverse divided by the degree of its all of its neighbor vehicle inverse and；Bridging central purpose is to find to be in the vehicle at center in car networking, and the number of links of these vehicles is compared its adjacent vehicle and wanted that much less simultaneously；
T gives vehicle viCanvassing index LiT (), represents vehicle v in car networking G (t)iThe all of its neighbor vehicle moderate quantity at least equal to the k maximum positive integer k equal to k:
Li(t)=max{k:dk(t)≥k}(9)
Wherein dkT () represents vehicle uiEach adjacent car ujDegree, and d1(t)≥d2(t)≥d3(t)…；Canvassing index can be considered as the degree d of vehicleiT the vague generalization form of (), have expressed the information of all of its neighbor vehicle in the communication range of vehicle simultaneously.
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| CN112287503B (en) * | 2020-11-18 | 2023-04-07 | 成都星宇数云科技有限公司 | Dynamic space network construction method for traffic demand prediction |
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